As a Data Scientist at National Grid, you will sit at the very heart of the global energy transition. National Grid is responsible for delivering electricity and gas safely, reliably, and efficiently to millions of people across the UK and the US. In this role, your work directly impacts the company's ability to decarbonize the energy grid, optimize asset management, and predict future energy demand. You will build and deploy predictive models that help transition traditional energy networks into smart, highly responsive grids of the future.
The problems you will solve are massive in scale and complexity. Whether you are forecasting energy load variations, predicting equipment failures before they happen, or optimizing the integration of renewable energy sources like wind and solar, your models will drive multi-million-dollar operational decisions. This is not a purely theoretical role; it is a highly applied position where your data pipelines and machine learning models directly influence real-world infrastructure and green energy initiatives.
Working on teams such as Grid Modernization, Asset Management, or Transmission Planning, you will collaborate closely with software engineers, power systems engineers, and business stakeholders. To succeed, you must possess a strong foundation in statistical modeling and machine learning, combined with the communication skills necessary to translate complex algorithmic outputs into actionable business strategies for non-technical leaders.
Online Application
reportedA handful of shapes account for most of what gets asked in this format: a ranking or deduplication inside groups, a running or rolling total, a period-over-period comparison, and a cohort tracked forward over time. Recognising the shape quickly is most of the speed here; deriving it from scratch while a clock runs is where the time goes. Know that a window function keeps every row while a GROUP BY collapses them, and know which one the question needs. If the exercise is in Python instead of SQL, the same shapes arrive as groupby with transform, shift and merge, and the same grain mistakes are available.
What to demonstrate
- Whether you reach the right construct without a detour, such as ROW_NUMBER over a partition to deduplicate instead of a self-join against a MAX subquery
- Whether you know what your window frame actually is, since adding ORDER BY inside OVER changes the default frame and silently changes a running total
- Whether the thing runs. A near-miss that throws an error scores below a plainer query that returns the right rows.
How to prepare
- Write each of the four shapes once from memory against a small schema and keep the working version somewhere you will reread it: dedupe with ROW_NUMBER, a running total, a month-over-month change with LAG, and a retention table
- Compute one running total twice on data with tied timestamps, once on the default frame and once with ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, and look at where the two disagree
- If Python is on the table, rebuild the dedupe and the running total with groupby and cumsum, then assert the two implementations return identical rows
Digital Assessment
reportedMuch of what gets scored here happens out loud while you type. Nobody can see your reasoning inside a half-written query, so five silent minutes read as being stuck even when they are not. State the plan in plain language first: which tables, what grain you are aggregating to, and the one filter that defines the population. Then write it. The narration doubles as insurance, because a wrong plan gets caught early and cheaply while a wrong query gets caught at the end with no time left to redo it. A timed statistics section, where one exists, is a separate test with its own clock.
What to demonstrate
- Whether the query you write matches the plan you just described
- What you do with a hint, meaning whether the correction gets absorbed or the first approach gets defended
- Whether you can debug your own wrong output by reading the result set and naming which part of the query produced the anomaly
How to prepare
- Solve three problems while screen-sharing into a recording, then watch it back and mark every stretch longer than thirty seconds where you said nothing
- Practise compressing the plan into one sentence before typing, then check afterwards whether the finished query actually matched it
- Time yourself on statistics questions that carry a business reading, such as what a confidence interval does and does not claim, rather than re-reading notes without a clock
1:1 Interview
reportedRounds outside the standard loop often open with something deliberately under-specified: a loose business problem, an open question about a product area, a dataset described in one sentence. The common failure is surveying, listing six plausible approaches and committing to none of them. The thing that separates a strong answer is scoping out loud. State what you are treating as the goal, name the metric you would move, say what you are choosing not to do and why, then take one path through to an actual answer. An interviewer can follow you down a narrow path. Nobody can grade a menu.
What to demonstrate
- Whether you turn an ambiguous prompt into a stated question with a measurable outcome before doing any work
- The judgement visible in what you cut, and whether you say why you cut it rather than silently dropping it
- Whether you land on a concrete recommendation with its caveat attached, rather than an unranked set of options
How to prepare
- Take three vague prompts, such as 'is this feature working', 'why did retention drop', and 'should we expand into a new segment'. For each, write one sentence of goal, one primary metric with its window, and two things you are explicitly not doing.
- Practise giving the recommendation first and the reasoning second, in five minutes. Loosely defined rounds are usually time-boxed, and an answer that arrives last often does not arrive.
- Keep a running assumption list as you talk, on paper or in the shared doc, so the interviewer can challenge one assumption instead of your whole answer.
Technical Take-Home Assignment
reportedYour submission is read asynchronously by someone who cannot ask you a clarifying question, and who will usually skim it before reading it properly. That changes what good looks like. The conclusion belongs near the top with the supporting analysis beneath it, and the code should run start to finish on a clean machine without a manual step you forgot to document. A reviewer forced to reconstruct your reasoning from the order of notebook cells is already discounting the work. The submissions that land are the ones where a busy reader gets the answer immediately and can verify it if they want to.
What to demonstrate
- Whether the answer arrives before the methodology, so a reader who stops after the first page still has the recommendation
- Whether the code runs end to end from the submitted files, with dependencies and data paths declared rather than assumed
- Whether each chart is legible on its own, carrying axis labels and units, and exists to support a claim made in the text
How to prepare
- Restructure a past analysis so the opening paragraph holds the recommendation and the number behind it, then check that nothing later in the document quietly contradicts it
- Copy your own submission into an empty directory, run it in a clean environment, and fix everything that breaks; hidden local state is caught here or by the reviewer
- For every chart, write the one sentence it is meant to prove, and delete the chart if you cannot write that sentence
Presentation to Panel
reportedA loop is not scored one interview at a time. The people you meet compare notes afterwards, usually in a meeting you are not in, and the outcome turns on what each of them can say about you when asked. That rewards something other than survival: every room needs one specific thing worth repeating, and none of them can contradict another. The common way to lose is to tell the same project four times with different numbers in it, or to be uniformly fine in a way that leaves nobody with anything to argue for.
What to demonstrate
- Whether your account of a project survives being told twice, with the same scale, the same metric definition and the same numbers each time
- Whether each interviewer leaves with one concrete claim they could make on your behalf later, rather than an absence of complaints
- Whether a question you already answered in an earlier room gets the same answer at the same depth, without visible impatience
How to prepare
- Write a one-page fact sheet for your two or three main projects that fixes the numbers you will quote: rows of data, the metric as a single sentence, the effect you measured and how long the work took. Say them aloud from the sheet until they come out identical every time
- For each kind of room you expect, decide the one sentence you want that interviewer repeating in a debrief, then check during the mock that you said it outright instead of implying it
- Rehearse answering the same project question twice in one sitting, the second time as though you had not just answered it, because the thing that needs fixing is the flatness that creeps into a repeated story
Technical and Behavioral Q&A
reportedBehavioural answers from data candidates get audited in a way that answers from other roles do not. When you say a model lifted retention, the next question is the denominator, the window, and how you knew the lift was not seasonal. So attach the measurement to each claim while you tell it: what the metric was before, over what period, and against what comparison. Numbers with no baseline read as rounded-up memory, and one unsupported figure tends to make the rest of the story sound rehearsed.
What to demonstrate
- Whether each impact number arrives with a baseline, a window and a comparison, or as a bare percentage
- Whether you can name the method that attributed the effect to your work (an experiment, a staged rollout, a seasonal control) or concede the link was correlational
- Whether the magnitudes stay internally consistent when the interviewer multiplies them against the scale you described earlier
How to prepare
- For each story, write the impact line as metric, value before, value after, window, and how attribution was established. Any line missing two of those five is a follow-up you will answer badly.
- Re-derive one headline number from the source table rather than the deck that reported it. Resume numbers drift upward across retellings.
- Decide in advance which figures you cannot share, and prepare the ratio or relative change you can give instead, so a confidentiality limit does not read as evasion.
PracHub editorial advice for the preparation topics above.
Counting on an identity key that changes underneath the metric
visitor_id is per browser and per device, and it resets on cookie clearance, private browsing and platform privacy changes, so the distinct-visitor count drifts upward for reasons unrelated to reach. Any rate with visitors in the denominator therefore decays over time even when behaviour is constant, and any rate with visitors in the numerator inflates. The stitching at signup makes it worse in both directions: a user who signed up on mobile and returns on desktop is two visitors and one user, while a shared device is one visitor and several users. Decide which key each metric is counted on, write it into the definition, and when comparing a period before and after a platform privacy change, expect a level shift in every visitor-keyed metric and do not attribute it to the product.
Comparing cohort retention curves of different maturities, or building the curve from users who are still present
A cohort four weeks old has no week-8 value, so an average taken across cohorts silently drops young cohorts from the later columns and keeps them in the earlier ones. The curve then bends upward at the tail, and the reading that 'retention is improving over time' is an artefact of which cohorts survived to be measured. The same error appears in the denominator when retention is computed over users active in the current period rather than over the full original cohort, which conditions on survival and guarantees a flattering number. The fix is a triangle: fix the cohort at signup, bound every window on both sides, and only compare cells where every cohort has had the full elapsed time, publishing the rest as blank rather than as a partial average.
Never asking what decision the analysis will inform
Open with who makes the decision, what the options are, and by when. The answer determines the precision you need, the segments worth cutting, and whether an observational read suffices or an experiment is required.
Analysing at a different unit than the one randomised
Say out loud what was randomised (user, device, account, cluster) and make the analysis unit match, or account for the clustering with cluster-robust standard errors, the delta method, or aggregation up to the randomised unit. Randomising users and then running a test over sessions understates variance and inflates the false-positive rate.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you evaluate the performance of a forecasting model when the un…
How do you evaluate the performance of a forecasting model when the underlying data distribution changes over time?
Approach
- Set a baseline first, so any model has something honest to beat.
- Check what information would not exist at prediction time, and exclude it.
- Say how the offline result would be validated online before it is trusted.
Follow-up
- What would you monitor after launch to know the model is still valid?
- Where could label leakage enter this setup?
Walk me through how you would handle highly seasonal data with multipl…
Walk me through how you would handle highly seasonal data with multiple overlapping cycles (e.g., daily, weekly, and annual patterns).
Approach
- Set a baseline first, so any model has something honest to beat.
- 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.
Follow-up
- What would you monitor after launch to know the model is still valid?
- Where could label leakage enter this setup?
Implement seven-day activation from its written definition
Implement the seven-day activation rate. Inputs: dim_user with user_id, account_created_at_utc and is_internal; fct_event with user_id, occurred_at_utc and is_core_action. A user activates when core-action events carrying a non-NULL user_id fall on at least two distinct UTC dates inside [account_created_at_utc, account_created_at_utc + 7 days). The denominator is every non-internal user whose account_created_at_utc lands in the cohort week, including users with no events at all. Return one row per cohort week with numerator, denominator and rate, publishing only weeks whose last signup is at least eight days old.
Approach
- Build the denominator first, from dim_user alone, filtered on is_internal = False. Deriving it from the join is the standard way to lose every user who never fired an event, which is exactly the population the metric is about.
- Join events to users on user_id with a left join from the user side, then apply the window as a half-open interval: occurred_at >= created AND occurred_at < created + 7 days. The right bound is exclusive, so an event at exactly created + 7 days does not count.
- Count distinct UTC dates per user, not distinct events. Floor occurred_at_utc to date before the nunique, and do it in UTC rather than local time so the threshold does not move with the user's country.
- Apply the >= 2 threshold, aggregate to cohort week, and compute the rate by re-summing numerator and denominator per week rather than averaging any per-user or per-day rate. Fix the week anchor explicitly: cohort_week is the Monday of the signup week in UTC, which is what Postgres DATE_TRUNC('week') returns and what any SQL version of this metric will produce. In pandas, subtract dt.weekday days from the floored timestamp. If you reach for periods instead, the anchor that matches is to_period('W') (equivalently 'W-SUN'), whose weeks end Sunday and therefore start Monday; to_period('W-MON') labels weeks that end on Monday, so it runs Tuesday through Monday and its start_time is a Tuesday. Mixing the two shifts every cohort label by one day and silently moves Mondays into the previous week.
- Suppress immature weeks: drop any cohort week whose maximum account_created_at_utc is within 8 days of the data cut, and return them as absent rather than as a partial number.
Worked solution 30 min
- users = dim_user[~dim_user.is_internal].copy(); created = users['account_created_at_utc']; users['cohort_week'] = created.dt.floor('D') - pd.to_timedelta(created.dt.weekday, unit='D'), which is the Monday-start week. The period spelling that agrees with it is created.dt.to_period('W').dt.start_time; 'W-MON' does not agree and is off by a day.
- ev = events[events.is_core_action & events.user_id.notna()]; merge onto users on user_id with how='inner' for the numerator side only.
- Filter to the half-open window, add ev_date = occurred_at_utc.dt.date, group by user_id and count distinct dates, keep users with >= 2.
- numer = users.merge(activated_user_ids, how='left', indicator=True) then group by cohort_week and sum the indicator; denom = users.groupby('cohort_week').size().
- rate = numer / denom; drop weeks where users.groupby('cohort_week')['account_created_at_utc'].max() > data_max - 8 days.
Follow-up
- The threshold is 2 distinct days. What changes in the reported history if someone moves it to 3, and how would you publish that change?
- Invited seats and SSO-provisioned users have no pre-signup session. Should they be in this denominator at all, and what does including them do to the rate for sales-assisted accounts?
- How would you produce the same metric at account grain, and which of the two would you put on the dashboard?
Explain how you would handle missing values and outliers in a large ti…
Explain how you would handle missing values and outliers in a large time-series dataset using Pandas.
Approach
- 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.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
Follow-up
- How would you verify this result without re-running the same query?
- How does the query change if the join becomes one-to-many?
What is the difference between bagging and boosting, and in what scena…
What is the difference between bagging and boosting, and in what scenarios would you choose one over the other?
Approach
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
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?
Explain the difference between L1 (Lasso) and L2 (Ridge) regularizatio…
Explain the difference between L1 (Lasso) and L2 (Ridge) regularization.
Approach
- Compute rates by summing numerator and denominator separately, never by averaging rates.
- Handle the rows that do not match: a LEFT JOIN with a NULL check is usually the question.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
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?
Paying accounts with no active seat in 28 days
dim_account holds account_id, account_type, lifecycle_status, seats_licensed. fct_event holds account_id, user_id, occurred_at_utc, is_core_action, and its account_id is NULL for every signed-out and pre-signup event. Find accounts with lifecycle_status = 'active' and account_type <> 'internal' that had no distinct user complete a core action in the trailing 28 days. Return account_id, seats_licensed and days since that account's most recent core action, with NULL where the account has never emitted one. Order by seats_licensed descending.
Approach
- Build the recent-activity set first: fct_event rows with is_core_action = TRUE, occurred_at_utc >= now() - interval '28 days', and an explicit account_id IS NOT NULL. Making the NULL exclusion explicit in the CTE is what lets you reason about the anti-join afterwards.
- Express the exclusion with NOT EXISTS (correlated on account_id) or a LEFT JOIN with an IS NULL guard. Do not use NOT IN against this column: it is nullable, and SQL's three-valued logic turns the whole predicate UNKNOWN, returning zero rows.
- Compute last-seen separately as MAX(occurred_at_utc) per account over all history, LEFT JOINed on, so an account that has never emitted a core action (NULL) is distinguishable from one that went quiet six weeks ago. Those two cases have different causes and different owners.
- Rank by seats_licensed, or better by the account's current mrr_cents_constant_fx if you are allowed the subscription table, because a silent fifty-seat account is a renewal conversation and a silent one-seat account is noise.
- Before shipping, check whether the never-seen group is a cluster by signup date or surface. A block of accounts with no events at all is usually an instrumentation gap, not a set of customers who stopped using the product.
Worked solution 25 min
- Count NULL account_id rows in fct_event over the window so you know the trap is live in this data rather than hypothetical.
- Write the active-account spine and the 28-day activity CTE.
- Write the anti-join with NOT EXISTS, then deliberately run the NOT IN version and record that it returns zero rows.
- Add the all-time MAX(occurred_at_utc) LEFT JOIN and derive days_since as a date difference.
- Split the output into 'quiet' and 'never seen' and eyeball the never-seen group for a shared signup window or surface.
Follow-up
- How would you distinguish a genuinely idle account from one whose events lost their account_id after an instrumentation change?
- Would you count on fct_event.account_id or resolve user_id through dim_user instead, and what does each choice miss?
- Licensed-seat utilisation is the continuous version of this. How would you turn this boolean into that ratio?
How do you prioritize your tasks when managing multiple competing dead…
How do you prioritize your tasks when managing multiple competing deadlines on a technical project?
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.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- How would you detect that the metric is being gamed rather than genuinely improving?
- What would you do if the primary metric and the guardrail moved in opposite directions?
Choose one success number for a homepage redesign
A homepage redesign is ready to test. Marketing wants visit-to-signup conversion as the single success metric: distinct fct_session.visitor_id with a 'signup_completed' event in fct_event, over distinct visitor_id with a session started in the window where is_bot_flagged = FALSE and consent_state <> 'denied'. Sessions carry referrer_channel and device_type. Argue for or against that metric, name what you would decide on instead, and give the one guardrail you would refuse to ship without. Deliverable: the metric, the guardrail, and the failure mode in writing.
Approach
- List everything that moves this rate without the page changing: referrer_channel mix, device_type mix, cookie lifetime and bot-rule changes, then pre-register the segments you will decompose on so the decomposition is not chosen after seeing the result.
- Argue that signup is an intermediate outcome the homepage can inflate by over-promising, and propose activated signups per 1,000 eligible visitors — the same numerator further restricted to users clearing the seven-day activation bar — as the number the decision actually rests on.
- Explain the consent exclusion rather than copying it: a 'denied' session can never be joined forward to a user, so leaving it in the denominator puts visitors there who have no path into the numerator and depresses the level permanently.
- Separate what the metric can and cannot be used for: inside a randomised comparison over one window it is fine, but as a trend line across a platform privacy change it will step down because visitor_id resets more often, with no behaviour change behind it.
- State the trade you would accept in advance: a smaller conversion gain with flat activation beats a larger conversion gain with activation down, and write the threshold before the readout.
Worked solution 15 min
- Restate the proposed metric and list every input that can move it with the page unchanged.
- Compute the mix-held-fixed counterfactual: reweight this period's per-segment rates by last period's segment volumes, and report the difference against the pooled rate as the mix effect.
- Define the decision metric: activated signups per 1,000 eligible visitors, with the seven-day activation predicate spelled out.
- Write the guardrail and the explicit trade-off sentence you would put in the ship decision.
- Record the denominator exclusions with the reason for each, not just the predicate.
Follow-up
- The redesign wins overall but the entire gain sits in paid social. What do you do, and what would change your mind?
- How do you roll four weekly conversion rates up to a month, and why does the obvious way give a different answer?
- Where does holding the segment mix fixed stop working as a correction?
Gross revenue churn doubled with no cancellations behind it
Gross monthly revenue churn computed from fct_subscription_period doubled from 1.8% to 3.6% in one month. The support queue shows no rise in cancellations and renewals look normal. You have fct_subscription_period with subscription_id, account_id, period_start_utc, period_end_utc, mrr_cents, mrr_cents_constant_fx, seats_billed, period_status, change_reason and canceled_at_utc, plus dim_account. Decompose the 1.8-point rise into named mechanisms, size each in points of the headline, and state the remainder you cannot explain.
Approach
- Reconstruct the numerator row by row and group it by change_reason before arguing about causes. A mid-period plan or seat change closes the current period row and opens a new one, so any implementation that treats a closed row as lost revenue books upgrades, downgrades and seat changes as churn; the change_reason breakdown of the numerator makes that visible in one query.
- Check the recognition timestamp. Churn belongs at period_end_utc, because revenue continues until the period ends, not at canceled_at_utc when the button was pressed. Then count period_end_utc rows per month across thirteen months: annual cohorts concentrate their period ends in the month twelve months after they were signed, so a spike that repeats in the same month last year is seasonality in the book, not an event this month.
- Recompute the whole numerator and denominator on mrr_cents_constant_fx. If the constant-currency figure is materially flatter, the move is an exchange-rate translation and belongs nowhere in a churn narrative.
- Check period_status handling. Rows with period_status = 'past_due' are dunning, not cancellation; a status-based rule counts them as loss while a period-end rule does not, and a dunning backlog can move the number by itself.
- Express every mechanism in points of the headline, sum them, and print the residual explicitly next to the month-to-month standard deviation of the prior twelve months. A decomposition without a stated remainder is a story, not an accounting.
Follow-up
- Which of these mechanisms should be fixed in the metric definition and which should be reported as a genuine business fact?
- How would you present a month whose churn is dominated by an annual cohort anniversary without the audience concluding the business is deteriorating?
- What would you change so that an upgrade can never enter the churn numerator again, and how would you test that it worked?
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 ↗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 ↗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 ↗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 ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Work that nobody used is a common and unflattering pattern in data careers, and interviewers probe for it. Have a story about an analysis that changed a decision, and be specific about how you got it in front of the person who could act. Also have one about work that went nowhere, with your reading of why.
Describe a situation where you had to collaborate with a difficult sta…
Describe a situation where you had to collaborate with a difficult stakeholder to deliver a data-driven solution.
Approach
- Close with what you would do differently, concretely.
- Quantify the outcome, including what you would not claim credit for.
- Name the disagreement or constraint, and how you resolved it with evidence.
Follow-up
- What did you decide not to do, and why?
- How did you know the outcome was caused by your change?
Describe a scenario where you had to explain a complex AI training ter…
Describe a scenario where you had to explain a complex AI training term or machine learning concept to a non-technical stakeholder.
Approach
- State the situation in two sentences and spend the rest on your reasoning.
- Close with what you would do differently, concretely.
- Pick a story where you drove the decision, not one where you observed it.
Follow-up
- What did you decide not to do, and why?
- What would you do differently if you ran that project again?
Defend a flat experiment readout against a post-hoc segment
A feature you evaluated is flat on seven-day activation: +0.05pp with a 95% interval of [-0.47pp, +0.57pp], from 61,000 exposed users per arm in fct_experiment_exposure joined to dim_user and fct_event. Baseline activation is 32%. The launch team asks you to drop every surface except mobile_web, where the point estimate is +1.1pp, and re-run. You have ten minutes in their planning meeting. Deliver a spoken position: what you will and will not do, and the decision you recommend.
Approach
- Recognise what is being probed: whether you hold a statistical position under social pressure without becoming either rigid or apologetic. A generic answer says the segment is not significant; a strong one separates the request into a question that is answerable (is the mobile_web number real?) and one that is not (can we ship on it?), and answers both.
- Price the multiplicity out loud. The slice was chosen after seeing the results, so its estimate is selected on favourable noise and is biased away from zero. With k independent looks at a nominal 5% level, the chance of at least one false positive is 1 - 0.95^k: 26% at six segments, 64% at twenty. Quote the k you actually inspected, not the k you reported.
- Use the arithmetic already in front of you. On the point estimates, a +1.1pp mobile_web effect combined with a pooled +0.05pp implies the remaining surfaces average negative in proportion to mobile_web's share of exposures. State that as a testable implication of their story rather than as a rebuttal of it.
- Ask the one question that settles the category: was mobile_web named in the analysis plan before launch? If it was, it is a planned comparison and gets a corrected reading. If it was not, it is a hypothesis, and the honest move is to size the test that would confirm it.
- Convert the refusal into a cost. Size a mobile_web-only confirmatory test at the claimed effect, state the weeks of mobile_web traffic it needs, and close with the recommendation: do not ship this as a lift, and note that the interval already rules out anything at or above +0.6pp, which is itself a useful input to the roadmap.
Follow-up
- The confirmatory test you sized needs nine weeks of mobile_web traffic and the team has three. What do you recommend instead?
- Suppose mobile_web was pre-registered. How does your reading change, and what correction do you apply?
- Your interval excludes +0.6pp. Is that the same as saying the feature does nothing?
- 01
Describe a situation where you had to collaborate with a difficult stakeholder to deliver a data-driven solution.
- 02
Describe a scenario where you had to explain a complex AI training term or machine learning concept to a non-technical stakeholder.
- 03
A feature you evaluated is flat on seven-day activation: +0.05pp with a 95% interval of [-0.47pp, +0.57pp], from 61,000 exposed users per arm in fct_experiment_exposure joined to dim_user and fct_event. Baseline activation is 32%. The launch team asks you to drop every surface except mobile_web, where the point estimate is +1.1pp, and re-run. You have ten minutes in their planning meeting. Deliver a spoken position: what you will and will not do, and the decision you recommend.
Is this an official National Grid interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at National Grid. Rounds and questions reflect what candidates have reported, not a process National Grid has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How technical is the National Grid interview process?
The process is highly technical but balanced. While you will be thoroughly evaluated on your Python (Pandas) skills and machine learning knowledge through the take-home assignment and panel presentation, National Grid also places an exceptionally high value on behavioral competencies and communication.
PracHub interview research ↗What should I expect from the take-home assignment?
You will typically receive a realistic dataset (often related to forecasting or load prediction) and be asked to build a predictive model. Expect to spend a significant amount of time wrangling the data, engineering features, and training your model. You will then present your methodology and results to a panel of team members.
PracHub interview research ↗Does National Grid ask standard "Why National Grid?" motivational questions?
Interestingly, past candidates have reported that some interview loops focus almost entirely on technical competency and behavioral scenarios, with very few or no direct motivational questions. However, you should still prepare a clear, concise explanation of why you want to work in the energy sector and contribute to grid modernization.
PracHub interview research ↗What is the company culture and work environment like?
National Grid has a highly professional, collaborative, and respectful environment. The interview panels are known for being polite and providing constructive, thoughtful feedback. The company places a strong emphasis on safety, reliability, and sustainability, which is reflected in how teams collaborate and solve problems.
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
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