The Data Scientist role at NextEra Energy, Inc. is a high-impact position situated at the intersection of large-scale infrastructure, renewable energy optimization, and data-driven decision-making. As the world’s largest renewable energy company, NextEra Energy, Inc. relies on its data science teams to extract actionable insights from vast datasets—ranging from grid performance and wind turbine diagnostics to complex market forecasting and customer demand modeling.
You will be tasked with transforming raw, complex data into strategic assets that influence the company’s operational efficiency and competitive edge. Whether you are building predictive models for energy consumption or evaluating the validity of competitor claims, your work directly informs the business logic that sustains a Fortune 200 organization. You can expect a fast-paced environment where you are expected to bridge the gap between deep technical rigor and clear, executive-level communication.
Recruiter Screening
reportedA screening call is a matching exercise run by someone who will not evaluate your statistics. They are checking that the work described on your resume is work you personally did, and that its scope matches the level the role is written for. Logistics get settled in the same half hour so nobody spends an interviewer's afternoon on a mismatch. The answer that fails is the one narrated in the plural. If every sentence is 'we built' and 'the team decided', there is nothing specific to write down about you. Name the piece that was yours, the decision you made inside it, and what changed after.
What to demonstrate
- Whether the ownership implied by your resume survives one round of follow-up about who actually did which part
- Whether your described scope (data size, stakeholders, what shipped) matches the seniority the role is written at
- Whether timeline, location and compensation expectations make the rest of the loop worth scheduling
How to prepare
- Rewrite your top three resume bullets in the first person singular, each with the decision you made and what moved afterwards, then say them out loud once so the 'we' does not return under pressure
- Attach one number to each project: the baseline, the change, and the window it was measured over. Where impact was never measured, say that plainly rather than inventing a figure
- Settle your compensation range before the call and give it as a range with a reason behind it, such as current total comp or a competing timeline, instead of deflecting the question twice
On-Demand Video Interview
reportedBecause the format is not fixed, prepare the reasoning rather than the ritual. Nearly every version of this round draws on the same underlying material: a design you can defend, a metric you can define exactly, an analysis whose assumptions you can state out loud. Only the wrapper changes, whether that is a take-home, a live case, a deep dive on past work, or a rough estimate on a whiteboard. Answers rehearsed to fit one shape stall the moment the shape differs. Practise naming the assumption behind a number, then saying how much the conclusion moves if that assumption is wrong.
What to demonstrate
- Whether your justification for a method survives the question 'why not the simpler thing', including when the simpler thing would have worked
- Precision under pressure: what exactly counts as an active user, a conversion or a success, over what window, with what exclusions
- Whether you carry an argument through to a recommendation instead of stopping at a list of tradeoffs
How to prepare
- For each project you plan to mention, write the metric definition in one sentence: numerator, denominator, time window, exclusions. Say it out loud once, because vagueness shows up in speech before it shows up on paper.
- Rehearse the same project at three lengths: two minutes, ten minutes, and a deep dive on one technical decision. Cutting live is harder than it sounds.
- For your headline result, write down what would have had to be true for it to be wrong, and how you ruled that out.
Technical Interview
reportedBefore anything else, this round is a reading test. You are given a small schema and a question phrased in business language, and most of the difficulty sits in the gap between them. Who counts as an active user, does a refunded order still count as an order, is that date column an event time or a load time. Weak answers start typing immediately and compute something precise about the wrong population. Strong ones pin the definition in one sentence, name the column that encodes it, then write the query. On a timed assessment with nobody to tell, write the definition in a comment anyway.
What to demonstrate
- Whether an ambiguous term becomes a specific column and filter before any computation happens
- Whether you read the schema for keys and cardinality rather than only for column names
- Whether the result answers the question at the grain it was asked at, per user or per session or per day
How to prepare
- Take three metrics you already use and write down the exact filter and exact grain behind each, then practise stating one of them in a single sentence out loud
- On a schema you have never seen, spend the first minute writing what one row of each table means and which key it is unique on, then predict which joins can duplicate rows
- Rehearse a version where the definition changes halfway through, and edit the query you have instead of starting over
On-Site Visit
reportedA day of back-to-back interviews samples your floor, not your ceiling. Four hours in, the habits that carry a good answer are the first to go: restating the question before solving it, asking what the data would have to look like, checking a number before quoting it. What the day decides is whether the tired version of you is still someone to leave alone with an ambiguous problem. The round that sinks a candidate is usually not the hardest one. It is the one immediately after the round that went badly.
What to demonstrate
- Whether the late rounds get the same clarifying questions as the first one, or whether you start answering immediately to save effort
- Whether a weak answer stays in the room it happened in, instead of following you into the next conversation as apology or distraction
- Whether the quality of your questions holds up, since fatigue removes curiosity about the problem before it removes knowledge of the method
How to prepare
- Rehearse the length, not just the content: book four mock interviews of different types in one afternoon with short gaps, because the one you need to observe is the fourth
- Put the two or three questions you ask at the start of any problem on a card in front of you, so that under fatigue it is a habit you run rather than a decision you make
- Decide in advance what the gap between rooms is for: water, one line of notes on anything you promised to follow up, and an explicit close on the round that just ended so it does not travel
- Prepare a different closing question for each interviewer, so the end of a long day does not produce the same one four times
PracHub editorial advice for the preparation topics above.
Averaging rates across SKU-locations instead of re-summing
Fill rate, turns, OEE and on-time rate are all ratios whose denominators differ by orders of magnitude between cells, so an unweighted mean gives a slow-moving C item at a small node the same vote as a high-volume A item at a national node. The blended figure then moves whenever the portfolio mix moves, and it can improve in every cell while the company-level ratio worsens, or the reverse, which is Simpson's paradox with a warehouse attached. Always sum numerator and denominator to the reporting level and divide once, and when a rate must be compared across nodes, standardise on a fixed SKU mix before reading anything into the difference.
Computing average inventory from period-end snapshots
Shipments cluster before period close, so the month-end on-hand position is systematically the lowest point of the month; turns computed against it are biased high and days of supply biased low, frequently by ten to twenty percent, and the bias grows precisely when close-period push is strongest. The same shape of error appears when a daily snapshot is joined to shipment events on date equality: a SKU with several legs on one day fans the snapshot out and multiplies the valued inventory. Average across every daily snapshot in the window for the denominator, and join snapshots to events with an explicit as-of condition and a row-count check at each grain before aggregating.
Reading a dozen metrics with no multiplicity control
Nominate one primary metric before launch and treat the rest as guardrails or exploratory, with Bonferroni or Benjamini-Hochberg applied when you intend to make claims from them. Twenty independent tests at 0.05 under the null produce at least one false positive about 64 percent of the time.
Writing SQL without stating NULL and tie-breaking behaviour
Before calling a query finished, say what it does with NULLs, ties and empty groups. NOT IN against a subquery containing a single NULL returns no rows at all, and RANK, DENSE_RANK and ROW_NUMBER differ precisely on ties, so name which one the question requires.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
What is the trade-off between model complexity and overfitting?
What is the trade-off between model complexity and overfitting?
Approach
- Say how the offline result would be validated online before it is trusted.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Check what information would not exist at prediction time, and exclude it.
Follow-up
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
As-of join snapshots to order lines without merge_asof
You have fct_order_line (order_line_id, sku_id, ship_from_location_id, actual_ship_at_utc, shipped_qty, shipment_id) and fct_inventory_daily (inventory_date, sku_id, location_id, available_qty). For every shipped line, attach available_qty from the latest snapshot strictly before the ship date for that exact sku and node, plus the shipment's delivery timestamp from fct_shipment_leg (leg_id, shipment_id, delivered_at_utc, leg_status). The output must have exactly one row per input order line. pandas.merge_asof is not available; implement the as-of match yourself.
Approach
- Collapse fct_shipment_leg to one row per shipment_id before joining anything. A shipment is one or more legs, so a direct line-to-leg join multiplies every line by its leg count and quietly inflates every sum computed afterwards.
- Define the shipment-level delivery as the maximum delivered_at_utc across its legs, and set it null if any leg is not delivered, since the customer has the order only when the last leg lands.
- Sort the snapshot frame by (sku_id, location_id, inventory_date) and the line frame by the same key plus ship date, then use np.searchsorted with side='left' on the sorted dates within each key group to find the insertion point; the matching snapshot is the row at position minus one.
- Guard the boundary explicitly: an insertion point of 0 means no snapshot exists before that ship date, and the result must be null rather than the first available future snapshot. Count those lines and report them.
- Assert the output row count equals the input line count immediately after each merge, before any aggregation. Row-count drift is the only symptom a silent fan-out produces, and it is invisible once the frame has been grouped.
Worked solution 30 min
- Aggregate legs by shipment_id to produce delivered_at_utc as the max, leg_count, and a delivered_all flag from leg_status.
- Left join that one-row-per-shipment frame onto fct_order_line by shipment_id and assert the row count is unchanged.
- Sort snapshots and lines by the composite key, then compute per-group insertion points with np.searchsorted and subtract one.
- Mask positions below zero to null, take available_qty at the remaining positions, and attach it as available_qty_prior.
- Validate on a single sku-location pair by printing its snapshot dates and one line's ship date and confirming the chosen date by eye.
Follow-up
- The snapshot is taken at the node's local end of day and actual_ship_at_utc is UTC. Where does that break the strictly-before rule and what do you need from dim_location to fix it?
- How would you extend this to allocate each leg's linehaul cost back to the order lines it carried?
- What changes if a line ships in two partial shipments?
Measure demand amplification hop by hop up the network
Using dim_location (location_id, parent_location_id, echelon, location_type) and fct_shipment_leg (leg_id, shipment_id, origin_location_id, destination_location_id, direction, tendered_at_utc, shipped_units), quantify how order variability grows upstream. fct_shipment_leg carries no sku_id and no link back to order lines, so shipped_units is the node's whole mixed-SKU flow and the measurement is node-level by construction. For each node compute weekly units flowing out (what it served downstream) and weekly units flowing in (what it ordered up), then the ratio of their variances on detrended, deseasonalised residuals. Walk parent_location_id from a store to echelon 0 and report the ratio at each hop, naming the hop where amplification is introduced. dim_location has no path column; build the chain yourself.
Approach
- Date orders by tendered_at_utc, not delivered_at_utc. Tender is the closest observable proxy for the moment the ordering decision was made; dating by delivery shifts the series by transit time and smears the variance you are trying to measure.
- Build both series on the same calendar weeks and in the same units. Variance scales with the aggregation window, so a ratio formed from daily inbound against weekly outbound measures the calendar, not the ordering rule. The units are mixed-SKU counts because the leg table carries no sku_id, so a node whose product mix drifts toward smaller or larger pack sizes moves both series for reasons unrelated to its ordering rule; print that caveat next to the number rather than implying a per-SKU result.
- Detrend and deseasonalise before taking variances, or compute the ratio on residuals from a simple weekly seasonal baseline. A growing node otherwise scores as amplifying, when all you have measured is its trend.
- Walk the parent chain iteratively: start from the store rows, join dim_location to itself on parent_location_id, and repeat until parent is null, capping the loop at the known echelon depth and asserting the path length matches the echelon difference so a data cycle raises rather than hangs.
- Read the output as a sequence, not a set of numbers. A pass-through node such as a cross-dock should sit near 1.0, and the hop where the ratio jumps is where a batching rule, a minimum order quantity or truckload rounding lives. That is the node to fix, not the node that is complaining.
Follow-up
- The ratio at one hop is 4.2 and the node insists it orders exactly to forecast. What ordering rules would produce that number anyway?
- What would it take to run this for a single SKU family rather than total units, given fct_shipment_leg carries neither a sku_id nor any link to order lines, and which hops would still be unmeasurable after you added that link?
- What would you expect this measurement to look like during a promotion, and does that change your conclusion?
How do you use SQL window functions to calculate running totals or mov…
How do you use SQL window functions to calculate running totals or moving averages?
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- State the window function and its partition and ordering out loud before writing it.
- Say which table is the grain you start from, and join outward from it.
Follow-up
- What breaks if events arrive late or out of order?
- How would you verify this result without re-running the same query?
What is the difference between a stored procedure and a function?
What is the difference between a stored procedure and a function?
Approach
- State the window function and its partition and ordering out loud before writing it.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Say which table is the grain you start from, and join outward from it.
Follow-up
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
Describe a situation where you had to optimize a slow-running SQL quer…
Describe a situation where you had to optimize a slow-running SQL query.
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- State the window function and its partition and ordering out loud before writing it.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- How does the query change if the join becomes one-to-many?
- How would you verify this result without re-running the same query?
Dead stock cells excluding SKUs used as substitutes
Find dead stock: sku_id x location_id cells whose latest fct_inventory_daily row has on_hand_qty > 0 and which have no fct_order_line with that sku_id and ship_from_location_id in the last 90 days. Also exclude any SKU that filled another line as a substitute in that window; fct_order_line.substituted_sku_id is NULL on every line that was not substituted. Value each cell at on_hand_qty * standard_cost_cents taken from the snapshot, not from dim_sku. Return the cell, days since its last demand line, and value, ordered by value. Treat line_status = 'cancelled_no_supply' as demand.
Approach
- Take the latest snapshot per sku-location with ROW_NUMBER() OVER (PARTITION BY sku_id, location_id ORDER BY inventory_date DESC) = 1, or an equivalent MAX(inventory_date) join, and keep rows with on_hand_qty > 0.
- Anti-join demand with NOT EXISTS against fct_order_line on both sku_id and ship_from_location_id over the 90-day window, counting every line status including cancelled_no_supply, because unmet demand is still demand and a cell with it is not dead.
- Write the substitute exclusion as NOT EXISTS, or force substituted_sku_id IS NOT NULL inside the subquery. NOT IN over a column that contains NULLs evaluates to NULL for every candidate that is not an explicit match, so the predicate is never true and the result set is empty.
- Value from the snapshot's standard_cost_cents rather than dim_sku, since the dimension carries only the cost in force today and is revised at period close without back-dating.
- Get days since last demand from a LEFT JOIN LATERAL MAX(ordered_at_utc), and report cells that never had a line in a separate bucket instead of as a very large number.
Worked solution 30 min
- CTE latest_snap: ROW_NUMBER over sku_id, location_id ordered by inventory_date DESC, filtered to rn = 1 and on_hand_qty > 0.
- CTE last_demand: MAX(ordered_at_utc) grouped by sku_id, ship_from_location_id over all of fct_order_line, with no status filter.
- Select from latest_snap LEFT JOIN last_demand, keeping rows where the max is NULL or older than 90 days.
- Add AND NOT EXISTS (SELECT 1 FROM fct_order_line o WHERE o.substituted_sku_id = latest_snap.sku_id AND o.ordered_at_utc >= now() - interval '90 days').
- Project value_cents = on_hand_qty * standard_cost_cents, order by it descending, and run the same query once with NOT IN to see the empty result for yourself.
Follow-up
- The query returns zero rows. Name the two likeliest causes and say which you would test first, with the one-line test.
- How do you separate genuinely dead stock from a seasonal item out of season and from a lifecycle_status = 'pre_launch' SKU?
- The same SKU is dead at one node and short at another. Write the query that finds that pattern and say what it implies about the policy.
If we notice a sudden drop in a key performance metric, what is your s…
If we notice a sudden drop in a key performance metric, what is your systematic process for diagnosing the root cause?
Approach
- State what result would change your recommendation, so the answer is falsifiable.
- Restate the decision this analysis has to support, and who acts on the answer.
- Fix the population and the time window before naming any metric.
Follow-up
- Which segment would you cut first, and what would that rule out?
- What would you do if the primary metric and the guardrail moved in opposite directions?
How would you design a product metric to measure the success of a new …
How would you design a product metric to measure the success of a new energy efficiency tool?
Approach
- Restate the decision this analysis has to support, and who acts on the answer.
- State what result would change your recommendation, so the answer is falsifiable.
- Fix the population and the time window before naming any metric.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
If a new competitor claims they can increase profit by attaining X cus…
If a new competitor claims they can increase profit by attaining X customers and charging Y rate, how would you verify this info and determine if it is valid?
Approach
- Fix the population and the time window before naming any metric.
- Name one primary metric, then the guardrail that stops it being gamed.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- Which segment would you cut first, and what would that rule out?
- How would you detect that the metric is being gamed rather than genuinely improving?
How do you determine the required sample size for a new A/B test?
How do you determine the required sample size for a new A/B test?
Approach
- Name the randomisation unit first; it decides the variance and what the test can detect.
- Decide the analysis before seeing data, including how long it runs and when you look.
- State the primary metric and the minimum effect worth shipping, then size the test.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- How would you handle interference between treated and control units?
If an experiment shows no significant difference, how do you communica…
If an experiment shows no significant difference, how do you communicate those results to the product team?
Approach
- Name the guardrails that would stop a launch even on a positive primary result.
- State the primary metric and the minimum effect worth shipping, then size the test.
- Name the randomisation unit first; it decides the variance and what the test can detect.
Follow-up
- How would you handle interference between treated and control units?
- What would you conclude if the result is positive but the test is underpowered?
Separate novelty from steady state in a planner tool rollout
An override assistant was given to 18 of 36 planners, randomised at the planner and held for 14 weeks. WMAPE at lag 7 improves 6% relative in weeks 1-3, 2% in weeks 4-8, and is flat after. Downstream unit fill rate does not move until week 6, then gains 0.8 points. Decide whether the early forecast gain is novelty, whether the late service gain is real, and specify the readout window you would pre-register next time.
Approach
- Separate the two candidate stories before touching the data. Novelty is a behavioural effect that fades as a planner stops attending to a new tool; a real effect that is being diluted as easy wins are exhausted looks identical in calendar time. With one simultaneous launch, exposure time and calendar time are collinear and the two are not separable, which is itself the honest first answer.
- Get identification from staggered exposure where it exists: index the effect by weeks since first exposure and compare a late cohort's week-2 effect against an early cohort's week-2 effect. Matching at equal exposure age but different calendar dates isolates novelty; a pattern that aligns on calendar week instead points at seasonality or a demand-side shock.
- Put the two outcomes on their own clocks. Forecast error can respond in the week an override is made, but fill rate cannot respond until the changed order has been placed, received and consumed, which is one lead time plus one review period. A six-week onset is the expected physics of the replenishment loop, not evidence of a delayed behavioural effect.
- Check the mechanism rather than the curve: count override actions per planner per week. Falling override volume alongside a falling effect is novelty. Steady override volume alongside a falling effect means the overrides stopped paying, because the largest corrections were taken first, and that is a different and more durable finding.
- Pre-register a readout window that starts after a burn-in of at least two replenishment cycles for any service metric, states the forecast window separately, and defines the primary on post-burn-in weeks only.
Worked solution 30 min
- Build a planner-by-week panel with arm, weeks since exposure, WMAPE at lag 7, unit fill rate, and override counts.
- Estimate the effect by weeks since exposure and, separately, by calendar week, and plot both against each other.
- If exposure was staggered, compare cohorts at matched exposure age; if it was simultaneous, state that novelty is not separable in this design and name the design change that would have separated it.
- Compute the system lag directly: median inbound lead time from fct_shipment_leg where direction = 'inbound', plus the review period, and compare it against the observed six-week onset.
- Write the pre-registration: forecast metrics from week 1 on their own clock, service metrics from the first week after two replenishment cycles, primary defined on the post-burn-in span.
Follow-up
- Planners cover each other's portfolios during absence and sit in the same weekly review. What does that do to a planner-level randomisation?
- Weeks 1-3 contained a promotion. How would you test whether the 6% is a promotion effect rather than novelty, using only data you already have?
Landed cost per unit rose with every lane flat
Landed cost per delivered unit rose from 4.12 dollars to 4.49 dollars month over month, yet cost per unit is flat or lower in every individual lane and no contract rate changed. From fct_shipment_leg (linehaul_cost_cents, fuel_surcharge_cents, accessorial_cost_cents, expedite_premium_cents, shipped_units, mode, service_level, origin_location_id, destination_location_id, leg_status, direction), quantify how much of the 37-cent move is mix and how much is rate, then state what you would do about it. Multi-stop loads are allocated across legs by weight.
Approach
- Reproduce the headline by summing the four cost columns and shipped_units over legs with leg_status = 'delivered' and direction IN ('outbound','interfacility'), dividing once. Confirm both facts hold on the same row set; if the lane figures came from an unweighted average of lane rates, the paradox is an artefact of the averaging and the work stops here.
- Write cost per unit as a weighted mean of lane costs, C = SUM(w_i * c_i), where w_i is the lane's share of shipped_units. Compute within = SUM(w0 * (c1 - c0)), mix = SUM(c0 * (w1 - w0)) and the interaction term, and verify they sum to 37 cents.
- If mix dominates, find which weights moved. Cut w by mode, by service_level and by distance band: parcel and ltl cost multiples of ftl per unit for the same goods, and 'expedite' carries expedite_premium_cents, so a migration of units across those categories raises the blend with no rate moving anywhere.
- Separate a rate change from a surcharge change by decomposing the numerator as well. Fuel surcharge and accessorials move independently of contract linehaul, so report all four cost components per unit side by side for both months before accepting that rates held.
- State the action in the units the decomposition produced. A mix problem is solved by load consolidation, order cut-off changes or minimum order quantities. A carrier negotiation only touches the within-lane term, which by hypothesis is the small one.
Follow-up
- Units migrated from truckload to parcel. Is that a transport failure, an order-profile change, or an inventory-positioning failure, and how would you tell them apart?
- You allocated multi-stop cost by weight. Which lanes change rank if you allocate by cube instead, and for what kind of freight?
- How would you build a mix-adjusted cost index that a monthly review can read without redoing this decomposition every time?
For someone who can already write the query and train the model but stalls when asked what to measure or whether a change is worth making. Metric definition and case structure come first; the technical work is kept as maintenance rather than the centre of the week.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Metric anatomy
- For three products you use daily, write one primary metric, two input metrics that plausibly move it, and one guardrail that would catch a cheap way of moving the primary at the cost of the product.
- For one of them, specify the metric precisely enough that two analysts would return the same number: numerator, denominator, unit of observation, time window, and how returning and deleted accounts are treated.
- Pick a ratio metric and write what happens to it when the denominator shrinks for reasons unrelated to the numerator, with a concrete example of that happening.
Deliverable: A one-page metric tree for one product, with the primary metric written as an unambiguous spec.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Diagnosing a drop without guessing
- Take the prompt "weekly active users fell 8 percent week over week" and write the segmentation plan before proposing any cause: platform, region, tenure cohort, acquisition channel, and whether the movement sits in the numerator or in a changed denominator.
- List the instrumentation failures that manufacture fake drops (a client release that stopped firing an event, a bot filter change, a shifted date boundary or timezone) and write the query that rules out each one.
- Rehearse stating the boring explanations first, seasonality and day-of-week composition, before reaching for a product cause.
Deliverable: A drop-diagnosis checklist short enough to recite from memory in under a minute.
Practice prompt ↗Practice prompt ↗Practice prompt ↗03Should we build it
- Take a feature idea and write it as a bet: what you believe is true, what would have to be true for it to pay off, the metric that would confirm it, and the effect size that would justify the engineering cost.
- Size the opportunity top-down and bottom-up, then reconcile the two numbers in writing instead of quoting whichever is friendlier.
- Write the counter-metric that would make you kill the feature even if it wins on the primary metric.
Deliverable: A one-page product memo ending in a decision rather than a list of considerations.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04The places aggregate numbers lie
- Construct a Simpson's paradox numerically: two segments where the treatment wins within each segment yet loses overall, and identify the shift in segment weights that causes it.
- Take a heavy right-tailed quantity such as revenue per user and write why the mean is the wrong summary, which percentile you would report instead, and what a moving mean with a stable median tells you.
- Write your definition of a session for the product from day one, then name two real behaviours it misclassifies.
Deliverable: One page holding a worked Simpson's paradox table and a session definition with its two known failure cases.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Technical maintenance, aimed at metrics
- Solve four timed SQL prompts that all end in a ratio metric, so the question of grain stays live in every answer.
- Compute a 95 percent confidence interval for a proportion on a small sample, and state why the normal approximation is unreliable when either np or n(1 minus p) falls below roughly 10, along with which interval you would use instead.
- Take one metric from your day-one tree, write the query that computes it correctly, then write the query that computes it wrong in the most plausible way and explain how you would notice.
Deliverable: Four solved prompts plus a matched correct and plausible-wrong query for one metric.
Practice prompt ↗Practice prompt ↗06Turning engineering work into data science stories
- Write three project stories as situation, decision, trade-off, outcome, each carrying one number and one thing you got wrong.
- For the story you will lead with, prepare an answer to "what would you do differently" that names a decision you made, not a constraint you were handed.
- Practise the sentence that reframes a systems project as a question project: the question the work answered, ahead of the pipeline it shipped.
Deliverable: Three written stories with the lead story delivered aloud and timed under four minutes.
Practice prompt ↗Practice prompt ↗07Mock case and gap list
- Run a 40-minute mock case with someone playing a product manager who pushes back on your metric choice, and record it.
- Listen back and mark every moment you proposed a solution before the success metric existed.
- Rewrite those moments as the question you should have asked, and rehearse the first 90 seconds of the case until scoping comes before solving.
Deliverable: A recorded case plus a rewritten opening 90 seconds.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
An answer without a quantity is hard to interrogate, so interviewers keep probing until they find one. Come with the baseline, the change, the window it was measured over, and how confident you were. If the effect never got measured, say so and say what you would have measured. Fabricated precision is worse than an honest gap.
Why do you want to work at NextEra Energy, Inc. specifically, and what…
Why do you want to work at NextEra Energy, Inc. specifically, and what do you know about our current energy initiatives?
Approach
- Name the disagreement or constraint, and how you resolved it with evidence.
- State the situation in two sentences and spend the rest on your reasoning.
- Quantify the outcome, including what you would not claim credit for.
Follow-up
- How did you know the outcome was caused by your change?
- What did you decide not to do, and why?
Disagree with a planning manager about a safety stock cut
A planning product manager proposes cutting safety stock 30 percent on every C and Z class sku-location cell, citing a MAPE improvement from 41 to 28 percent over two quarters. Checking the query, you find MAPE is computed over fct_inventory_daily rows where the actual is greater than zero, and the actual used is shipped_qty rather than demand_qty; stockout_flag is true on 14 percent of the rows that were kept. You have fifteen minutes in his planning review. Make the disagreement, and propose what you would do instead of the flat cut.
Approach
- Lead with the decision at risk, not the metric error: a 30 percent cut on intermittent items is the cheapest way to convert a measurement artefact into stockouts eight weeks later, by which time nobody will connect the two.
- Name the two defects precisely. Dropping zero-actual rows is not a rounding choice, it removes most of the history for a C-class item at a forward-stocking location and it removes the rows where over-forecasting is penalised, so the surviving MAPE is biased toward whichever series stocked out. Using shipped_qty as the actual scores the forecast against a supply ceiling, so a series looks more accurate the more often it ran out.
- Offer the replacement metric in the same breath: WMAPE, sum of absolute errors over sum of actuals against demand_qty, computed at the sku-location-week grain the ordering decision uses and at the lag it uses, reported next to signed bias so a persistently short series cannot hide inside an accuracy number.
- Propose the smaller action that is defensible now: recompute on the corrected basis, rank cells by bias rather than accuracy, and cut cover only where the corrected series is unbiased or over-forecasting, in a staged rollout with a service guardrail.
- Give him the win he actually wants: if the corrected numbers still support cuts on a subset, say so in advance, so the disagreement is about evidence rather than about territory.
Follow-up
- He says demand_qty is itself incomplete because customers stop ordering what shows out of stock. Is he right, and what do you do about it?
- Which items would you leave alone regardless of what the corrected metric says?
Quantify your own impact on a safety stock change honestly
In March you re-sized safety stock at twelve regional DCs using the variable lead time formula. Unit fill rate at those nodes rose from 94.1 to 96.5 percent by June. In the same window a supplier that had been running 71 percent inbound on-time recovered to 93 percent, and Q2 volume is seasonally about 8 percent below Q1. Your manager asks for one sentence on impact for a performance review. Write the claim you are willing to defend under challenge, and explain how you bounded the portion attributable to your change.
Approach
- Refuse the naive claim first: the 2.4 point gross movement contains at least three generating processes, and attributing all of it is the sort of claim that collapses the moment someone plots the supplier's on-time series next to it.
- Build a comparison group from nodes that did not get the change, check that their pre-period fill rate moved in parallel with the treated nodes for at least two quarters before March, and estimate the difference in differences with variance clustered at the node, since twelve treated clusters is the effective sample size regardless of how many node-weeks there are.
- Strip the confounders you can measure directly rather than trusting the design alone: restrict to sku-location cells sourced from other suppliers to remove the recovery, and hold the sku mix fixed across periods so the seasonal volume drop does not enter through mix.
- Sanity-check the mechanism, not just the coefficient: if your change worked, the improvement should concentrate in cells whose reorder point rose and in short_reason_code = 'no_stock' lines, and should be absent where the policy did not move. If the gain is spread evenly, something else caused it.
- State the result as an interval with the confounders named, and state the cost side too: extra units held at the applicable cost of capital, so the claim is a net one rather than a service headline.
- Write the sentence so that it survives someone else re-running it, which usually means a smaller number than the gross movement and a named method.
Follow-up
- The comparison nodes are not parallel in the pre-period. What do you do next?
- Your bounded estimate is roughly half the gross movement. How do you present that to a manager who already told his boss the larger number?
- 01
Why do you want to work at NextEra Energy, Inc. specifically, and what do you know about our current energy initiatives?
- 02
A planning product manager proposes cutting safety stock 30 percent on every C and Z class sku-location cell, citing a MAPE improvement from 41 to 28 percent over two quarters. Checking the query, you find MAPE is computed over fct_inventory_daily rows where the actual is greater than zero, and the actual used is shipped_qty rather than demand_qty; stockout_flag is true on 14 percent of the rows that were kept. You have fifteen minutes in his planning review. Make the disagreement, and propose what you would do instead of the flat cut.
- 03
In March you re-sized safety stock at twelve regional DCs using the variable lead time formula. Unit fill rate at those nodes rose from 94.1 to 96.5 percent by June. In the same window a supplier that had been running 71 percent inbound on-time recovered to 93 percent, and Q2 volume is seasonally about 8 percent below Q1. Your manager asks for one sentence on impact for a performance review. Write the claim you are willing to defend under challenge, and explain how you bounded the portion attributable to your change.
Is this an official NextEra Energy, Inc. interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at NextEra Energy, Inc.. Rounds and questions reflect what candidates have reported, not a process NextEra Energy, Inc. has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical interviews?
The difficulty can vary by team, but expect deep-dive questions on machine learning concepts and rigorous data manipulation tasks. Preparation is key; do not assume your resume will speak for itself.
PracHub interview research ↗Is there a coding assessment?
While you may not always face a whiteboard coding test, you should be prepared to discuss your code in detail. Be ready to explain your logic for complex SQL queries or machine learning model choices.
PracHub interview research ↗What is the best way to prepare for the project presentation?
Focus on the "why" and the "impact." Describe the business problem, your methodology, the challenges you faced, and the final outcome. Ensure your slides are clear and tell a cohesive story.
PracHub interview research ↗How long is the hiring process?
The process typically moves at a steady pace, usually spanning a few weeks from the initial screen to the final round. Keep your communication with the recruiter prompt and professional.
PracHub interview research ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Data Scientist practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
Explore related engineering environments and compare the preparation focus.
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