Interview guides · Eversource Energy

Eversource Energy Data Scientist Interview Questions & Guide 2026

Data scientist prep for Eversource Energy candidates: drill A/B design, power and minimum detectable effect on original exercises.

The 60-second brief

A Data Scientist at Eversource Energy plays a vital role in harnessing data to drive strategic decisions and enhance operational efficiencies. This position is crucial in analyzing vast datasets to uncover insights that inform energy management, customer service, and sustainability initiatives. By leveraging advanced analytics, machine learning, and statistical modeling, you will help Eversource Energy optimize its services and deliver better outcomes for customers while aligning with its commitment to renewable energy and innovation.

Seniority shifts the scope more than the words in the title do. Earlier-career loops mostly check that you execute a well-posed analysis correctly; senior loops check that you can decide which question is worth answering and defend what you chose not to do.

Focus areas

Price a service-level change in working capital
Size safety stock under variable lead times
Trace an on-time miss to one node

Overview

This role impacts a variety of products and services that Eversource Energy offers, from enhancing grid reliability to improving customer engagement through personalized services. You will work closely with cross-functional teams, including engineering, operations, and product management, to tackle complex challenges and contribute to the company's mission of providing reliable energy solutions. Expect to engage deeply with data, applying your analytical skills to real-world problems that affect millions of customers, making this position both challenging and rewarding.

The Eversource Energy interview process

Eversource Energy candidates report 3 rounds · ≈ 3-5 weeks. The stages below are what candidates describe, not a published process.

Phone Screening

A 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

In-depth Interviews

An extra round usually exists because something is still open after the standard loop: a skill the earlier interviews did not sample, a level decision, or two interviewers who disagreed. It is rarely a rerun of what you already did well. Ask the recruiter who you are meeting, what function they sit in, and how long the session runs. That is an ordinary scheduling question, and the answer changes what you should prepare. What separates a strong candidate here is treating the round as a fresh evaluation with its own bar, rather than assuming earlier performance carries you through or sinks you.

What to demonstrate

Whether you can answer well on ground the earlier rounds did not cover, without leaning on what you already said to someone else
Consistency of the facts in your stories: the same sample size, timeframe, team size and scope of your own role as in earlier conversations
How you handle an unfamiliar format live, including whether you ask what kind of answer is wanted before producing one

How to prepare

Ask the recruiter for the interviewer's function, the length, and whether to expect a coding surface, a discussion, or a presentation. Preparing for a 30 minute conversation with a partner team is not the same work as preparing for a 60 minute technical block.
Write out what each earlier round actually covered, then list the two or three areas nobody probed. That gap is the most likely subject of the extra round.
Re-read the numbers in the project stories you have already told, so a second telling does not quietly contradict the first.

Panel Interview

Where a loop ends with a senior leader, that conversation is rarely another skills test. The technical signal already exists by then, so the questions tend to open up: what you would look at first, where a metric you have heard about could mislead, what you would push back on. The decision being made is scope, which in practice means level and how much you would be trusted to own unsupervised. Treating it as a formality is the usual mistake. An open question late in the day is still being scored, and a vague answer reads as someone who has not run anything themselves.

What to demonstrate

Whether your view of the business has anything specific behind it, given that you are working only from what is public and are expected to say so
Whether the scope of work you describe owning matches the scope of the role, instead of sitting a level below it
Whether you can disagree with something concrete and stay useful about it, rather than agreeing with everything said in the room
Whether your questions are ones only this person could answer, as opposed to ones the recruiter already covered

How to prepare

Build one view you could defend for two minutes using only public information: what the funnel probably looks like, which metric likely drives decisions, and where that metric could mislead. Being wrong for a stated reason survives this round; having no view does not
Write down the largest piece of work you have owned from question to decision, who else touched it, and what you decided alone, then check that it reads at the level you are interviewing for
Prepare one thing you would want changed if you joined and phrase it as a question rather than a verdict, so it opens a conversation instead of closing one

Where candidates lose points

Attributing demand variability to the node where it is observed

Order variance amplifies as it moves upstream: batching to a truckload, minimum order quantities, forecast-driven ordering and promotional pull all convert smooth end demand into lumpy replenishment orders, so a plant can see a coefficient of variation several times that of the underlying consumption. Diagnosing the plant's schedule instability as a plant problem then produces interventions that cannot work, because the generating process sits one or two echelons downstream. Measure the bullwhip ratio explicitly (variance of orders placed by a node over variance of demand it received) at each echelon, and fix the ordering rule at the node where the ratio jumps rather than the node where the pain is felt.

Treating shipped units as demand

Shipments are censored at available inventory: on a stockout day the recorded quantity is a supply ceiling, not customer intent, and orders that were never placed because the item showed as unavailable leave no row at all. A forecast fitted on that history learns the constraint, under-forecasts the fast movers that stock out most often, and produces the replenishment that causes the next stockout, so the error compounds in one direction rather than averaging out. The fix is to model demand_qty rather than shipped_qty, flag stockout days with stockout_flag and treat them as censored (fit with a censored likelihood, or estimate unconstrained demand from uncensored periods and comparable locations), and to report how much of the history was censored alongside any accuracy number.

Averaging per-user rates to produce a population rate

Decide which quantity you want: the mean of per-user ratios and the ratio of summed numerator to summed denominator are different estimands, and heavy users dominate one but not the other. For a ratio metric, aggregate numerator and denominator separately and use the delta method for its variance.

Answering a product-sense question with a list of features

Answer with a decision and the measurement that would settle it: the hypothesis, the primary metric, the guardrails, and the result that would make you not ship. A feature brainstorm cannot be wrong, which is exactly why it earns no points.

Questions & practice

Write a function to calculate the correlation between two variables in a dataset.

Medium · statistics and probability

Approach

Translate the result into the decision it informs, in one plain sentence.
Quantify uncertainty explicitly rather than reporting a point estimate alone.
Sanity-check the answer against a simple bound or a simulated case.

Follow-up

How would you explain this result to someone who does not know statistics?
What sample size would you need to detect an effect half this size?

What machine learning algorithms are you most familiar with, and in what scenarios would you use them?

Medium · machine learning and modelling

Approach

Say how the offline result would be validated online before it is trusted.
Pick an evaluation metric that matches the cost of each error type, not a default.
Check what information would not exist at prediction time, and exclude it.

Follow-up

Where could label leakage enter this setup?
What would you monitor after launch to know the model is still valid?

Simulate service levels under a variable inbound lead time

Medium · safety stock · monte carlo · service level

Daily demand for one SKU at one node is Poisson with mean 40. Replenishment lead time is 7 days with probability 0.7, 10 days with 0.2, and 14 days with 0.1, drawn independently per order. The policy is continuous review: order Q = 400 whenever the inventory position drops to the reorder point. Size safety stock two ways for a nominal 95 percent target, once as z times sigma_D times sqrt(mean lead time) and once with the variable-lead-time term. Simulate both and report achieved cycle service level and achieved unit fill rate.

Approach

Compute both safety stock numbers analytically before simulating, so the simulation is a check on arithmetic you already understand rather than the source of the answer.
Track inventory position (on hand plus on order) for the reorder trigger and on hand separately for the stockout test. Triggering off on hand alone re-orders while stock is already in transit and silently changes the policy you are measuring.
Define the two outcomes precisely and separately: cycle service level is the fraction of replenishment cycles containing at least one unit of unmet demand, while fill rate is total units shipped from stock over total units demanded. They answer different questions and z targets only the first.
Before running anything, bound the achievable service level by hand from the lead-time mixture. Each lead-time branch is a separate Poisson demand, so cycle service level is the probability-weighted sum of P(Poisson(40 x L) <= reorder point) across the three branches, and a reorder point that cannot cover the 14-day branch caps the policy at 0.9 no matter how the simulation is tuned.
Run enough cycles that Monte Carlo error is small against the gap you are trying to see. The standard error of a service level is sqrt(p*(1-p)/n_cycles), about 0.005 at p = 0.7 with 10,000 cycles, so the 20-point gap between the two policies is unambiguous and a 1-point one is not.
Let unmet demand be lost rather than backordered, state that choice, and note that switching to full backordering raises measured fill rate for the same policy.

Worked solution (35 min)

Compute mean lead time 8.3 days, variance of lead time 5.01, sigma_D = sqrt(40) = 6.32, and mean demand 40 per day.
Naive safety stock: 1.645 * 6.32 * sqrt(8.3) = 30 units, giving a reorder point of 332 + 30 = 362. Correct safety stock: 1.645 * sqrt(8.3 * 40 + 40^2 * 5.01) = 1.645 * 91.4 = 150 units, giving a reorder point of 482.
Write one simulation loop parameterised by safety stock: draw daily Poisson demand, decrement on hand, record shortfall, trigger an order of 400 when position hits the reorder point, and schedule the receipt after a sampled lead time.
Run 10,000 cycles per policy with a burn-in of at least two lead times discarded so the starting position does not flatter the first cycles.
Report achieved cycle service level, achieved fill rate and the average on-hand position for each of the two safety stock settings.

Expected result: Naive safety stock is 30 units and the variable-lead-time figure is 150 units, a factor of five. At Q = 400 with lost sales, the naive policy achieves a cycle service level of about 0.70 and the correct policy about 0.90, so the naive policy misses the nominal 0.95 by roughly 25 points and the correct one by 5. The naive ceiling is arithmetic, not simulation noise: with a reorder point of 362 the 7-day branch is essentially always covered, the 10-day branch survives with probability P(Poisson(400) <= 362) = 0.029, and the 14-day branch never does, so cycle service level cannot exceed 0.7 + 0.20.029 + 0.10 = 0.706, and reorder-point undershoot of about 20 units under daily demand arrivals pulls it to roughly 0.70. The correct policy tops out near 0.90 for the same structural reason: a reorder point of 482 covers the 10-day branch almost completely (P = 0.99997) but not the 14-day one (P = 0.0004), so that 0.1 of probability mass is near-pure loss. Fill rate is higher than cycle service level under both, about 0.92 against 0.70 and about 0.98 against 0.90, because expected units short per cycle (roughly 33 and 10) are small against Q = 400.

Verify it yourself

Set the lead time to a constant 8.3 days and confirm the two formulas return an identical safety stock, since the lead-time variance term vanishes.
Compare the naive policy's simulated cycle service level against its analytic ceiling, 0.7 + 0.2P(Poisson(400) <= 362) + 0.1P(Poisson(560) <= 362) = 0.706 before undershoot. A simulated figure above that is a bug, usually triggering off on hand instead of inventory position, and three seeds should land a little below it and agree within about one point, consistent with a standard error of about 0.005 at 10,000 cycles.
Confirm fill rate exceeds cycle service level at Q = 400 under both policies, and treat that ordering as a consequence of Q rather than a law: expected units short per cycle depends on the reorder point and the lead-time demand distribution, not on Q, so holding the reorder point fixed and halving Q leaves cycle service level roughly unchanged while fill rate falls by about the same shortfall over a smaller denominator.
Confirm simulated demand over the lead time has mean near 332 units and standard deviation near 91.4, which is exactly the quantity the correct formula multiplies by z.

Follow-up

With Q = 100 against lead-time demand of 332 units, three or four orders are open at once. What happens to the meaning of a replenishment cycle, and to each of the two service measures?
Lead time and demand are correlated because the supplier is capacity constrained in peak weeks. What does that do to the formula you used?
How would you convert the extra 120 units of safety stock into an annual cost the business can weigh against the service gain?

Describe how you would optimize a slow-running query in a database.

Medium · sql and data manipulation

Approach

Check whether any join is one-to-many before aggregating, or the sums inflate.
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.

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?

Weekly unit fill rate by ship-from node

Easy · ratio aggregation · service metrics · conditional aggregation

fct_order_line holds one row per customer order line per ship-from node in its terminal state, with ordered_qty, shipped_qty, requested_ship_date, actual_ship_at_utc, line_status and ship_from_location_id; dim_location carries location_code. Produce weekly first-pass unit fill rate by node. Numerator is shipped_qty on lines shipped on or before requested_ship_date; denominator is ordered_qty on every line requested that week except line_status = 'cancelled_by_customer'. Return node, week, numerator, denominator and rate, plus one network total row. State in the output how substituted lines are counted.

Approach

Anchor the query at order-line grain filtered on requested_ship_date, never at shipments: a line that was never filled has no shipment row, and starting from shipments deletes exactly the failures the metric exists to count.
Build the numerator as a conditional SUM over the same row set, SUM(CASE WHEN actual_ship_at_utc IS NOT NULL AND its ship date <= requested_ship_date THEN shipped_qty ELSE 0 END), so short and backordered lines stay in the denominator instead of vanishing behind a WHERE clause.
Exclude only line_status = 'cancelled_by_customer'. A line cancelled for lack of supply is a service failure and belongs in the denominator at full ordered_qty.
Group by node and by DATE_TRUNC('week', requested_ship_date), then build the network row by re-summing numerator and denominator across nodes, not by averaging the node rates.
Declare the substitution rule explicitly in one column or one comment: lines with substituted_sku_id NOT NULL count toward the numerator only if substitution is an accepted fill in the service definition.

Worked solution (20 min)

Filter fct_order_line to requested_ship_date inside the window and line_status <> 'cancelled_by_customer'; count the rows removed by that filter and keep the count.
Compute num = SUM(CASE WHEN actual_ship_at_utc::date <= requested_ship_date THEN shipped_qty ELSE 0 END) and den = SUM(ordered_qty) in the same SELECT.
GROUP BY ship_from_location_id, DATE_TRUNC('week', requested_ship_date) and join dim_location for location_code.
Add the network row from a second aggregate over the same base set: SUM(num) / SUM(den).
Report the excluded-line count and the substitution rule beside the rate so the number is auditable.

Expected result: One row per node-week with numerator, denominator and rate = numerator / denominator, plus a network row whose rate equals SUM(numerator) / SUM(denominator) across nodes and which will generally differ from the mean of the node rates.

Verify it yourself

Rate never exceeds 1. If it does, shipped_qty is being counted on lines whose ordered_qty was filtered out of the denominator.
Denominator per node-week matches a separate plain SUM(ordered_qty) with no CASE logic in it.
Dropping the largest node moves the network rate. If it does not move, the total is an average of rates rather than a re-summed ratio.

Follow-up

actual_ship_at_utc is UTC but requested_ship_date is a local calendar date at the node. How does the comparison change for a node at UTC+9, and which direction does the error run?
Fill rate rose two points while a node's short_reason_code mix moved from 'no_stock' toward 'credit_hold'. Is that a supply improvement?
How would you hold SKU mix fixed so the network number is comparable month over month?

If you found an anomaly in the data, what steps would you take to investigate it?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
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.

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?

How do you prioritize tasks when you have multiple deadlines?

Medium · product sense and metrics

Approach

Fix the population and the time window before naming any metric.
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?

Given a dataset with various features, how would you determine which features are most important?

Medium · product sense and metrics

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.
Fix the population and the time window before naming any metric.

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?

What metrics would you use to evaluate the success of a data-driven project?

Medium · product sense and metrics

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

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?

Discuss how you would design an experiment to test a new energy-saving initiative.

Medium · a/b testing and experimentation

Approach

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.
Name the guardrails that would stop a launch even on a positive primary result.

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.

Medium

Approach

Work from the decision backwards to the evidence you would need.
Clarify what is being asked and what a complete answer would contain.
State your assumptions explicitly before working the problem.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Success metric for opening one forward-stocking node

Medium · synthetic control · risk pooling · power

The network will open one forward-stocking location at echelon 3, replenished by an existing regional DC. There is one treated node and about thirty candidate comparison nodes. Using fct_order_line (ordered_at_utc, actual_delivery_at_utc, ship_from_location_id, line_status), fct_inventory_daily (on_hand_qty, standard_cost_cents) and fct_shipment_leg (direction, shipped_units, linehaul_cost_cents, accessorial_cost_cents, delivered_at_utc), define the primary metric, the guardrail that catches the working-capital cost of de-pooling, and the readout design. Say what this sample cannot detect.

Approach

Anchor the primary on what the node is for: P90 order cycle time for the lines it serves, taken over the pooled line-level distribution rather than as a mean of daily percentiles, with never-delivered lines reported as a censored count instead of dropped.
Make the inventory guardrail network-wide, not node-local. Splitting one pooled stock into two locations raises total safety stock at the same service level; under independent demand, equal service and equal lead times the square-root law puts the factor at sqrt(2) on the safety component, so measure parent and new node combined at cost.
Add a freight guardrail across both the new inbound interfacility leg and the outbound legs, because the opening adds a movement that any node-local outbound cost metric will miss entirely.
Choose a design suited to one treated unit: a synthetic control or matched-pair comparison built from donor nodes on pre-period cycle time, volume, SKU mix and lane structure, with the donor pool and matching variables frozen before the readout.
Set the burn-in at two full replenishment cycles, and book the fill-and-build inventory into the cost side rather than letting it contaminate the service read.
State the limit rather than implying precision: with one treated unit there is no cross-unit variance, so a placebo distribution over the donors is the only inferential statement available, and effects smaller than the donor spread cannot be distinguished from node noise.

Worked solution (35 min)

Build a weekly node panel covering at least 26 pre-period weeks: P90 cycle time, line volume, fill rate, on-hand at cost, delivered cost per unit.
Screen the donor pool, dropping nodes that opened, closed or changed parent_location_id inside the window, then fit synthetic weights on pre-period P90 and volume only.
Check pre-period fit, then run placebo assignments across every donor to build the distribution of pseudo-effects.
Estimate the post-period effect from the burn-in end, and compute the working-capital series as parent plus new node combined.
Report the effect against the placebo distribution together with the minimum effect this design could have detected.

Expected result: A readout design where the primary is P90 order cycle time for lines shipped from the treated node, estimated against a synthetic control fitted on pre-period P90 and volume; guardrails are combined parent-plus-node on-hand at cost and delivered cost per unit across the added interfacility leg plus outbound; a two-replenishment-cycle burn-in is excluded from the service read and charged to cost; and inference is stated to rest on a placebo distribution over roughly thirty donors, so effects inside the donor spread are declared undetectable.

Verify it yourself

Pre-period fit: the synthetic series tracks the treated node's P90 within the donor-to-donor spread before the opening, otherwise the estimate is not usable and no amount of post-period data repairs it.
The working-capital guardrail is computed on parent plus node combined; computed node-only it always shows an increase and carries no information.
Computing P90 as a mean of daily P90s produces a different and generally flatter series than the pooled line-level percentile; show both once to prove which one the readout uses.

Follow-up

A donor node runs a promotion during the post period. What do you do, and at what point did you have to decide it?
The service gain is real and the cost guardrail is also up. Who decides, and on which number?

Perfect order rate falls only in the newest two weeks

Medium · right censoring · reporting lag · metric maturation

A weekly perfect order chart holds at 94 percent for eleven weeks, then reads 91 percent and 84 percent in the two most recent weeks. The metric requires every line shipped_full, actual_delivery_at_utc on or before original_promised_delivery_date, and no joined shipment leg carrying exception_code IN ('damage','address_issue') or leg_status IN ('refused','lost'). It is published on a 14-day lag. Using fct_order_line and fct_shipment_leg, decide whether service regressed and specify what the chart should show for incomplete weeks.

Approach

Start from the structure of the metric: the denominator is fixed at promise date while the numerator requires an event that may not have happened yet, so any week younger than the full delivery-plus-claims window is mechanically depressed. Establish that before hunting for a cause.
Quantify the incompleteness instead of assuming it. Per promise week, report the share of lines with actual_delivery_at_utc IS NULL and the share of shipments whose legs are still leg_status IN ('tendered','in_transit').
Build the maturation curve from history. For each of the eleven settled weeks, recompute the metric as it would have looked at 3, 7 and 14 days after the promise date, so a recent week can be compared against a healthy week at the same age rather than against a settled one.
Only after age-matching, test whether any residual is real by cutting it by ship_from_location_id, mode and exception_code, and asking whether the loss concentrates where a genuine failure would concentrate.
Fix the chart rather than only the analysis: suppress or grey weeks younger than the publication lag, or publish an age-matched estimate with an interval, and write the rule down so the next reader is not caught by the same shape.

Follow-up

Damage claims can arrive up to thirty days later. Does that argue for a longer lag or for a different numerator?
How would you detect a genuine regression inside the lag window without waiting two weeks for it to settle?
P90 order cycle time shows the same maturation shape but biased the other way. Why, and which metric is more dangerous to read early?

Suggested practice checklist

Instead of guessing where the week should go, day one measures it under a fixed rubric and allocates the remaining hours in proportion to the gaps. The method is deliberately rigid: the allocation is written down before any studying starts and is not renegotiated when a topic turns out to be unpleasant.

Day 1 — Diagnostic, scored before you study anything

Sit a 100-minute timed diagnostic in four blocks: 30 minutes of SQL across three prompts, 25 minutes of short-answer statistics, 25 minutes on one modelling or case prompt, and 20 minutes delivering one behavioural story aloud.
Score each block from 0 to 3 on a fixed rubric where 3 is correct and fluent, 2 is correct but slow or prompted, 1 is partially correct, and 0 is stuck, grading the output rather than how the attempt felt.
Allocate the hours for days two to five roughly in proportion to 3 minus the score in each block, write the allocation down, and commit to not revising it midweek.

Deliverable: A scored rubric and a fixed hour allocation for the rest of the week.

Day 2 — Largest gap: find the boundary rather than the subject

Break the weakest area into five named sub-skills (for query work: grain control, window frames, date arithmetic, set logic with NULLs, and reading a query plan) and rate each one, so the rest of the week targets a sub-skill instead of a subject.
Solve three problems chosen to sit just above where the rating drops off, and for each write the first move you failed to make.
Re-solve one of them from memory four hours later, on paper, with nothing open.

Deliverable: A five-item sub-skill map with the two blocking sub-skills circled.

Day 3 — Largest gap: drill the blocking sub-skill

Do eight short repetitions of the same shape rather than eight different problems, so what you practise is the pattern and not the puzzle.
Write the rule you now hold in one sentence, then test it against a case built to break it: a ranking function over a column with ties, or a two-sample test on observations that are obviously dependent.
Have someone else read your one-sentence rule and find the precondition you left out.

Deliverable: One rule statement with its preconditions attached and one counterexample that would have caught the incomplete version.

Day 4 — Second gap, plus maintenance on your strongest area

Run the same sub-skill map and boundary protocol on the second-largest gap, compressed into half the day.
Spend 25 timed minutes on your strongest area to stop it decaying, choosing the hardest problem you can still finish rather than an easy warm-up.
Compare how the two areas fail: whether you lose time on recall, on setup, or on arithmetic, because the fix differs for each.

Deliverable: A second sub-skill map plus a one-line diagnosis of how each area fails you.

Day 5 — The gap that is not a skill

Record yourself answering one technical and one behavioural prompt, then count two things in the playback: how many seconds before your first clarifying question, and how many sentences you started without knowing where they ended.
Rewrite your three most-used stock phrases into shorter versions, and practise saying "I do not know, here is how I would find out" without softening it into a guess.
Deliver one answer again with a hard 90-second limit to force structure before detail.

Deliverable: Two recordings with a counted improvement in time-to-first-question.

Day 6 — Retest under day-one conditions

Sit the same 100-minute diagnostic structure with new prompts of comparable difficulty and score it on the identical rubric.
Compare block by block, and for any block that did not move, change the method rather than adding hours: a block stuck at 1 usually means the practice was too varied, not too short.
Write which single block you would still lose the offer on.

Deliverable: A second scored rubric placed next to the first, with one named remaining risk.

Day 7 — Full loop under interview conditions

Run a 60-minute mock covering the two blocks that moved least, with an interviewer instructed to interrupt and change direction.
Write your recovery script for the moment you go blank: restate the question, state your assumption, name the first thing you would check.
Reduce the week to the rule statements you wrote, each with its preconditions attached, then say every one of them out loud without reading it and cut any you cannot state in a single sentence, since a rule you have to reconstruct mid-answer will not survive being interrupted.

Deliverable: A one-page card holding the recovery script and only the rules you could state from memory.

Your part, not the team's

Most data work is done by groups, so an interviewer has to work out which piece was yours. An answer that runs on 'we' for several minutes gets interrupted with a question about what you personally did, and by then the answer sounds defensive even when it is true. Mark your own contribution as you go, and name the parts that belonged to someone else instead of leaving them ambiguous. Keep a few specifics back as well, like the name of the metric or who actually objected, so a probe can be answered with something you had not already said.

Describe a time when you used statistical analysis to solve a problem.

Medium · behavioural and stakeholder questions

Approach

State the situation in two sentences and spend the rest on your reasoning.
Close with what you would do differently, concretely.
Quantify the outcome, including what you would not claim credit for.

Follow-up

What did you decide not to do, and why?
What would you do differently if you ran that project again?

Scope a vague request about rising inventory

Easy · scoping · stakeholder management · inventory turns

A planning director opens with: inventory is up twelve percent on flat shipments, get me an analysis by Friday. You have fct_inventory_daily (inventory_date, sku_id, location_id, on_hand_qty, standard_cost_cents) and fct_order_line (shipped_qty, unit_cogs_cents, requested_ship_date). Nothing else is specified: not the window, not the comparison basis, not whether the twelve percent is units or value. State the three questions you would ask before writing any SQL, name the decision each answer changes, and describe the first cut you would run if nobody answers you before Friday.

Approach

Ask what decision hangs on the answer: a buy stop, a write-off provision, a policy review and a board slide all need different cuts, and the director usually has one of them in mind.
Pin the measurement before the cause: units or value, which two windows are being compared, and whether the denominator is average daily on-hand or a period-end snapshot, since a month-end denominator biases turns high by ten to twenty percent and can manufacture the whole movement.
Ask which part of the network is in scope, because echelon matters: a build at echelon 2 ahead of a promotion and a build of blocked_qty at a plant are different problems with different owners.
Commit to a default if no answer arrives: value at standard cost, averaged across every daily snapshot in both windows, cut by echelon, then by abc_class and xyz_class, then by lifecycle_status to separate phase_out stock from active cover.
Say out loud what the first cut cannot settle, so the director is not surprised: it localises the build but does not attribute it to forecast bias, a lot-size change or a supplier pulling orders in.

Follow-up

The build is concentrated in one product family at two DCs. What are your next two queries, and what would make you stop calling it a planning problem?
The director wants the number by Friday and the cause by Friday. Which do you drop, and how do you say so?

Quantify your own impact on a safety stock change honestly

Hard · impact measurement · confounding · difference in differences

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?