As a Data Scientist at Sandia National Laboratories, you play a pivotal role in leveraging data to drive innovation and support national security missions. Your expertise in statistical analysis, machine learning, and data visualization directly impacts the lab’s ability to solve complex scientific and engineering problems. The work you do contributes to critical areas such as renewable energy, nuclear security, and advanced materials, ensuring that the United States remains at the forefront of technological advancement.
This role is not only about analyzing data; it's about transforming insights into actionable strategies that inform decisions at the highest levels of government and industry. You will collaborate with interdisciplinary teams, working alongside research scientists and engineers to address substantial challenges facing the nation. The diversity of projects ensures that no two days are alike, making this position both challenging and rewarding, while allowing you to see the tangible effects of your contributions on national and global scales.
Screening Call
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
Team Interviews
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.
Job Talk
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.
PracHub editorial advice for the preparation topics above.
Watching an experiment daily and stopping when it crosses significance
A fixed-sample test controls type I error at one pre-declared look. Checking repeatedly and stopping at the first p < 0.05 inflates the false positive rate to roughly 0.15 to 0.20 for ten looks, and it rises further with more frequent checks, because the p-value takes a random walk that will eventually dip below the threshold under the null. The usual defences are a fixed horizon declared before launch, group-sequential boundaries such as O'Brien-Fleming that spend alpha across a planned number of looks, or always-valid confidence sequences that are correct under continuous monitoring. Compounding it, the effect size reported conditional on having crossed the threshold is biased away from zero, and the bias is larger the lower the power was, so an underpowered test that 'won' typically overstates the lift it found.
Treating last-touch attribution as the causal value of a channel
The attribution label on dim_user is the output of a rule that assigns full credit to whichever touch happened to be recorded last inside a lookback window, and that rule systematically rewards channels that sit close to the conversion, especially branded search and retargeting, which largely intercept demand that already existed. Reallocating spend on those labels moves budget toward the channels that are best at being last, which is why attributed return on ad spend often improves while total signups do not. Nothing in the touchpoint data can settle this, because the counterfactual of not running the channel was never observed. The credible reads are a geo holdout or a scheduled pause, sized in advance on the total-signups metric rather than on the attributed one, and the honest framing in the meantime is that the label describes correlation with conversion and not incremental contribution.
Ignoring interference between units in a marketplace experiment
Ask whether one unit's treatment can change another unit's outcome through shared inventory, a matching pool, a social graph or a common budget. Where it can, randomise at a level that contains the spillover, such as region or time slice, and say explicitly what that costs you in statistical power.
Interpreting a change before checking data quality and logging
Spend the first pass on row volume by day, null rates, duplicate keys, and whether the step change lands on a release or tracking-migration date. A discontinuity that coincides with a deploy is an instrumentation hypothesis before it is a behavioural one.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Can you explain the concept of overfitting and how to prevent it?
Can you explain the concept of overfitting and how to prevent it?
Approach
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Say how the offline result would be validated online before it is trusted.
Follow-up
- How would you choose the decision threshold, and who owns that choice?
- Where could label leakage enter this setup?
How would you approach optimizing a poor-performing model?
How would you approach optimizing a poor-performing model?
Approach
- Pick an evaluation metric that matches the cost of each error type, not a default.
- Set a baseline first, so any model has something honest to beat.
- Frame the prediction: the label, the moment of prediction, and the action it triggers.
Follow-up
- Where could label leakage enter this setup?
- How would you choose the decision threshold, and who owns that choice?
Rebuild per-visitor ordering without groupby convenience methods
You have a DataFrame of 2 million fct_event rows with visitor_id, occurred_at_utc and event_id, unsorted and containing duplicate timestamps within a visitor. Produce three new columns: event_rank, the 1-based position of the event within its visitor ordered by occurred_at_utc; seconds_since_prev, the gap to that visitor's previous event, NULL for the first; and is_first_for_visitor. You may use sort_values, shift, cumsum, numpy and boolean masking. You may not use groupby.transform, groupby.apply, groupby.cumcount, groupby.rank or merge_asof. Break timestamp ties on event_id.
Approach
- Sort once by ['visitor_id', 'occurred_at_utc', 'event_id'] and reset the index. The whole exercise reduces to row arithmetic on a sorted frame, and the tiebreak on event_id is what makes the result reproducible across runs.
- Mark visitor boundaries with is_first = df['visitor_id'].ne(df['visitor_id'].shift()). This is the single fact every other column derives from.
- Compute seconds_since_prev as the diff of the timestamp column, then overwrite it with NaT/NaN wherever is_first is True. The shift crosses the boundary between visitors and will otherwise hand the first row of each visitor the last event of the previous one.
- Build event_rank from a running counter that resets at boundaries: take a global cumulative position (np.arange(len(df))) and subtract, per row, the global position at which that visitor started. Get the start position by forward-filling the positions where is_first is True, which is a cumsum-free reset and is O(n).
- Verify against the forbidden method once, as a test rather than as the implementation, and confirm the two agree on every row.
Worked solution 20 min
- Sort on the three-key tuple and reset_index(drop=True).
- Compute is_first via .ne(.shift()), which is True for row 0 because the shifted value is NaN.
- pos = np.arange(len(df)); start = pd.Series(np.where(is_first, pos, np.nan)).ffill(); event_rank = (pos - start + 1).astype(int).
- gap = df['occurred_at_utc'].diff().dt.total_seconds(); gap[is_first] = np.nan.
- Assert event_rank equals df.groupby('visitor_id').cumcount() + 1 on the sorted frame.
Follow-up
- The frame does not fit in memory. How does your approach change if you can only process one visitor-partitioned chunk at a time?
- occurred_at_utc is client-supplied and sometimes runs backwards within a visitor. Does your seconds_since_prev go negative, and should it?
- How would you extend this to reset the counter at every change of surface as well as visitor?
Pair flow starts to completions within a thirty-minute bound
fct_event holds event_id, flow_id, flow_instance_id, event_name, occurred_at_utc, surface, app_version. Using only rows where event_name is 'flow_started', 'flow_completed' or 'error_shown', compute the daily core-flow completion rate by surface and app_version. Numerator: distinct flow_instance_id whose flow_completed occurs within 30 minutes of its flow_started with no error_shown for the same instance in between. Denominator: distinct flow_instance_id with a flow_started that day. Report rows with a NULL flow_instance_id as a separate coverage count, not inside either side.
Approach
- Collapse the stream to one row per flow_instance_id using conditional aggregation: MIN(occurred_at_utc) FILTER (WHERE event_name = 'flow_started') AS started, MIN(...) FILTER (WHERE event_name = 'flow_completed') AS completed, MIN(...) FILTER (WHERE event_name = 'error_shown') AS first_error. This makes the 'first' semantics explicit and avoids a self-join entirely.
- Apply the predicates on that single row: completed IS NOT NULL AND completed <= started + interval '30 minutes' AND (first_error IS NULL OR first_error > completed). Note the last clause encodes 'in between' literally, so an error surfaced after a successful completion does not disqualify the attempt.
- Bucket by started::date, surface and app_version. Splitting by app_version is not decoration: a completion-rate regression is nearly always confined to one client build, and a pooled daily rate hides it behind the installed base.
- Count NULL flow_instance_id rows as a separate coverage figure. They cannot be attributed to an attempt at all, so they belong in neither numerator nor denominator, and their share tells you how much of the rate is unmeasurable.
- For any roll-up to week or to all surfaces, re-sum the numerator and denominator rather than averaging the daily rates.
Worked solution 25 min
- Write the per-instance collapse CTE and confirm COUNT(*) equals COUNT(DISTINCT flow_instance_id).
- Add the three predicates one at a time, recording how many instances each removes.
- Aggregate by day, surface and app_version with FILTER on the clean-completion flag.
- Compute the NULL-instance coverage count as a separate select and report it alongside.
- Pick the worst day-surface-version cell and pull ten raw instances to confirm the disqualification reason.
Follow-up
- occurred_at_utc is client-supplied. What do you do with an instance whose completion timestamp precedes its start?
- flow_instance_id is missing on 4 percent of starts for one app_version. What can and cannot you conclude about that build?
- An instance starts at 23:55 and completes at 00:04. Which day owns it, and what does the alternative do to the daily series?
Attribute account MRR to first-touch channel without seat fan-out
dim_user holds user_id, account_id, first_touch_channel, account_created_at_utc. dim_account holds account_id, lifecycle_status. fct_subscription_period holds account_id, period_start_utc, period_end_utc, period_status, mrr_cents_constant_fx. For a given as-of date, report active MRR by the first-touch channel of the account's earliest-created user. An account has many users and many periods. The channel totals must sum exactly to total active MRR on that date, and accounts whose label is NULL must appear as their own bucket.
Approach
- Reduce the label side to one row per account before any join: ROW_NUMBER() OVER (PARTITION BY account_id ORDER BY account_created_at_utc, user_id) = 1 over dim_user, carrying first_touch_channel. Joining dim_user to the period table directly repeats each revenue row once per seat.
- Reduce the money side to one row per account too: the period where period_start_utc <= :as_of AND period_end_utc > :as_of AND period_status IN ('active','past_due'). If more than one row survives that filter the account holds overlapping subscriptions, which must be deduped or deliberately summed, and either way the choice is stated rather than left to the join.
- Join the two one-row-per-account CTEs and SUM(mrr_cents_constant_fx). Use the constant-FX column, since the same query run in two quarters otherwise reports currency moves as channel performance.
- Bucket NULL first_touch_channel as 'unattributed' with COALESCE instead of letting it drop. Roughly the share of users with no observed touch is not small, and dropping it understates the total and inflates every named channel's share.
- Reconcile: the SUM over the joined result must equal the SUM over the period CTE alone. That single equality check catches fan-out immediately, which is why it is worth writing before presenting the number.
Follow-up
- Which user should own the account's label: earliest created, the 'owner' role, or the one who converted? What does each choice systematically favour?
- What does this table entitle you to say about the value of a channel, and what does it not?
- Some accounts have a parent_account_id. How does rolling those up change both the numerator and the channel mix?
Given a dataset with various features, how would you determine which f…
Given a dataset with various features, how would you determine which features are most important for prediction?
Approach
- Restate the decision this analysis has to support, and who acts on the answer.
- Name one primary metric, then the guardrail that stops it being gamed.
- Decompose the metric into the rates that drive it, and say which one you would check first.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- Which segment would you cut first, and what would that rule out?
How do you prioritize tasks when managing multiple projects?
How do you prioritize tasks when managing multiple projects?
Approach
- Decompose the metric into the rates that drive it, and say which one you would check first.
- 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
- What would you do if the primary metric and the guardrail moved in opposite directions?
- How would you detect that the metric is being gamed rather than genuinely improving?
Describe your thought process when faced with an ambiguous problem.
Describe your thought process when faced with an ambiguous problem.
Approach
- 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.
- Restate the decision this analysis has to support, and who acts on the answer.
Follow-up
- What would you do if the primary metric and the guardrail moved in opposite directions?
- Which segment would you cut first, and what would that rule out?
If you had to design an experiment to test a hypothesis, what steps wo…
If you had to design an experiment to test a hypothesis, what steps would you take?
Approach
- State the primary metric and the minimum effect worth shipping, then size the test.
- Decide the analysis before seeing data, including how long it runs and when you look.
- Say whether units interfere with each other, and switch design if they do.
Follow-up
- What would you conclude if the result is positive but the test is underpowered?
- What would you do if you could not randomise at all?
Explain the difference between supervised and unsupervised learning.
Explain the difference between supervised and unsupervised learning.
Approach
- Say what you would check first and why it is the highest-information step.
- State your assumptions explicitly before working the problem.
- Work from the decision backwards to the evidence you would need.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
A test that won on day four and lost by day twelve
A two-week test was checked every morning. On day four the primary metric crossed p = 0.03 and the team shipped. By day twelve, with logging still running, the estimate had fallen to a 0.2% lift at p = 0.61. The day-four deck also reported a significant 9% lift among paid_search visitors on mobile_web, one of twenty segments inspected. Explain both patterns quantitatively, say what the day-four evidence was actually worth, and specify the monitoring rule that prevents a repeat.
Approach
- Price the peeking. A fixed-sample test controls type I error at one pre-declared look. Ten daily looks at a nominal 5% raise the false positive rate to roughly 0.15 to 0.20, because the running test statistic takes a random walk that will eventually cross the boundary under the null.
- Price the winner's curse separately, because it is a different error. Conditioning an estimate on having crossed the threshold biases it away from zero, by a factor of roughly 3 when true power is 20% and only about 1.1 at 80% power. Day four sits at roughly 29% of planned information, which is exactly the low-power case.
- Read day twelve as the better estimate rather than as a contradiction. Nothing regressed; the day-four value was an extreme draw that was selected precisely for being extreme.
- Price the segment claim. Across twenty segments at a nominal 5%, at least one crosses about 64% of the time under a global null, since 1 - 0.95^20 = 0.64, and the slice that surfaces is by construction the one with the most favourable noise.
- Specify the rule instead of scolding. Either fix the horizon and look once, or use group-sequential boundaries such as O'Brien-Fleming that spend almost no alpha early, or use always-valid confidence sequences that stay correct under continuous monitoring. Pre-register the small set of segments, correct across the ones tested, and treat anything found afterwards as a hypothesis needing its own powered test.
Worked solution 25 min
- Count the looks actually taken, twelve daily, and state the inflated alpha band of 0.15 to 0.20 for ten looks, rising with more.
- Place day four on the information fraction, about 4/14 = 29% of planned sample, and apply the exaggeration factor for low power.
- Compute 1 - 0.95^k for the number of segments actually inspected; with k = 20 this is 0.64.
- Report the day-twelve interval as the estimate and say plainly that the experiment supports no effect at any size the team would ship for.
- Write the rule into the experiment template: horizon and segments declared at launch, sequential boundaries if interim looks are needed at all.
Follow-up
- Under O'Brien-Fleming with five planned looks, roughly what nominal p-value would the first look have needed to stop?
- The feature already shipped. What analysis can you still run, and what can it not tell you?
- If mobile_web paid_search were genuinely the only affected segment, how would you design the confirmatory test and how large would it have to be?
Separate a definition edit from a behaviour change
Seven-day activation rate is documented as core actions on at least 2 distinct UTC dates inside the first seven days after account_created_at_utc, and every cohort published through last week was computed on that definition. The dashboard now reads 38% for the signup cohort that started Monday, against 31% for the cohort before it. Two things landed in that week: an onboarding change, and a commit to the metric's SQL that you have not read yet and that came with no documentation update. You have dim_user, fct_event and the metric's version history. Deliver two numbers: how many of the 7 points are behaviour and how many are the definition. Then say which published cohorts are no longer comparable.
Approach
- Diff the metric SQL across the commit boundary and enumerate every changed predicate individually: the distinct-day threshold, the is_internal exclusion, the is_core_action source, the window bounds, and the identity key. A single commit routinely changes more than one, and each has its own sign.
- Dual-run both definitions over the same 26 cohort weeks. The definition effect is the level shift between the two series computed on identical data, which isolates it from anything that happened in the world.
- Read the behaviour effect only from the old-definition series, comparing the new cohort to its own trailing cohorts. The new-definition series cannot answer the behaviour question because it has no pre-period.
- Explain the mechanism of the changed predicate rather than only its size. Lowering a distinct-day threshold from 2 to 1 admits every user who acted once in a single session, which is a large and low-intent population, so a several-point jump is the expected magnitude and not a surprise.
- State the restatement plan: pick one definition, backfill the full history on it, and annotate the break date. A series carrying two definitions is not a time series.
Follow-up
- The onboarding team wants the 1-day threshold kept because it is easier to move. What is your argument, and what would change your mind?
- How would you make a definition change visible to every consumer of this metric without relying on people reading a changelog?
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 ↗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 ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Have two ready. In one, the data was on your side and you had to move someone who outranked you. In the other, the pushback was correct and you changed position. The second is the harder story and it lands better, because it shows you separate being right from being attached to an answer. Name the person's actual objection.
What motivates you to work in data science, particularly at Sandia?
What motivates you to work in data science, particularly at Sandia?
Approach
- Pick a story where you drove the decision, not one where you observed it.
- Name the disagreement or constraint, and how you resolved it with evidence.
- Close with what you would do differently, concretely.
Follow-up
- What did you decide not to do, and why?
- What would you do differently if you ran that project again?
Describe a challenging problem you faced and how you overcame it.
Describe a challenging problem you faced and how you overcame it.
Approach
- Quantify the outcome, including what you would not claim credit for.
- Name the disagreement or constraint, and how you resolved it with evidence.
- Close with what you would do differently, concretely.
Follow-up
- What would you do differently if you ran that project again?
- What did you decide not to do, and why?
Choose between three teams' requests with one analyst-week
You have one analyst-week. Three requests land the same morning. A growth team wants a paid-channel readout before a Friday spend decision. A billing team wants gross monthly revenue churn rebuilt, because the current figure recognises cancellation at canceled_at_utc rather than period_end_utc and is therefore wrong. A product team wants a dashboard for a feature launching in six weeks. All three sponsors are peers of your manager. Deliver your ranking, the explicit rule that produced it, and the message you send to the two teams you defer.
Approach
- Recognise what is being probed: whether you prioritise on decision value and reversibility or on who asked most recently and most loudly. The generic answer sorts by importance; the strong one states a rule, applies it, and accepts the ranking it produces even where that is uncomfortable.
- Score each request on three statable things: the decision it unblocks and the date that decision is made, the cost of being wrong in the meantime, and whether the work is one-off or compounding. A wrong published churn figure compounds, because it is quoted downstream and enters forecasts; the channel readout has a fixed date that cannot move; the dashboard has six weeks of slack.
- Notice the tension between value and urgency rather than resolving it by feel. The churn defect is the most valuable item and the least urgent one, which is exactly the shape of work that never gets done.
- Break the churn item in two. A one-hour severity check, sizing the gap between the two recognition points in MRR, is cheap enough to do before ranking anything and may promote the item outright. Do that first, then rank.
- Make the deferrals concrete. Each deferred team gets a date, a reason expressed as another team's decision deadline rather than as relative importance, and the smallest thing you can hand them immediately.
Follow-up
- The dashboard team escalates to your manager. What do you say in that conversation?
- Your severity check shows churn is overstated by 15%. Does the ranking change, and does anybody need to be told today regardless of the ranking?
- A fourth request arrives Wednesday with a Thursday deadline. What comes off the list, and who do you tell first?
- 01
What motivates you to work in data science, particularly at Sandia?
- 02
Describe a challenging problem you faced and how you overcame it.
- 03
You have one analyst-week. Three requests land the same morning. A growth team wants a paid-channel readout before a Friday spend decision. A billing team wants gross monthly revenue churn rebuilt, because the current figure recognises cancellation at canceled_at_utc rather than period_end_utc and is therefore wrong. A product team wants a dashboard for a feature launching in six weeks. All three sponsors are peers of your manager. Deliver your ranking, the explicit rule that produced it, and the message you send to the two teams you defer.
Is this an official Sandia National Laboratories interview guide?
No. It is PracHub's own research and practice material for the Data Scientist role at Sandia National Laboratories. Rounds and questions reflect what candidates have reported, not a process Sandia National Laboratories has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗What is the interview difficulty level, and how much preparation time is typical?
The interview process is generally considered average in difficulty, with candidates usually spending several weeks preparing. Focus on both technical skills and behavioral aspects, as both are integral to the evaluation.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates demonstrate a strong technical foundation, effective problem-solving skills, and the ability to communicate complex concepts clearly. They also align well with Sandia's collaborative culture.
PracHub interview research ↗What is the typical timeline from the initial screen to the job offer?
The timeline can vary, but candidates can expect a response within a few weeks after their initial interview. The overall process may take a month or more, depending on scheduling and the number of candidates being considered.
PracHub interview research ↗Is remote work an option for this role?
While many positions at Sandia are onsite due to the nature of the work, some flexibility may exist. Candidates should inquire about specific arrangements during the interview process.
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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