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Two Sigma Data Scientist Interview Guide 2026

This guide covers the Two Sigma 2026 Data Scientist interview process, detailing coding assessments, SQL fundamentals, statistics, applied modeling......

Topics: Two Sigma, Data Scientist, interview guide, interview preparation, Two Sigma interview

Author: PracHub

Published: 3/21/2026

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Two Sigma Data Scientist Interview Guide 2026

This guide covers the Two Sigma 2026 Data Scientist interview process, detailing coding assessments, SQL fundamentals, statistics, applied modeling......

5 min readUpdated Jul 1, 202648+ practice questions
48+
Practice Questions
3
Rounds
5
Categories
5 min
Read
Contents
TL;DRSample QuestionsAbout the Interview ProcessWhat to expectInterview roundsOnline assessmentRecruiter or hiring manager screenTechnical phone screenLive coding roundBehavioral interviewFinal interview loopWhat they testHow to stand outHow to Use This Page as a Prep PlanFAQWhat matters most in data interviews?How should I practice SQL?How do I handle ambiguous metrics?
Practice Questions
48+ Two Sigma questions
Two Sigma Data Scientist Interview Guide 2026

TL;DR

Two Sigma’s 2026 Data Scientist interview is usually a rigorous multi-round process that blends coding, statistics, applied modeling, and discussion of your past work. The most distinctive feature is that the process is personalized by team and background, so you should expect the broad structure to be similar across candidates, but the exact sequencing and follow-up depth to vary. Some candidates see an online coding assessment very early, and the process may stop before all rounds if the team decides the fit is not there. You should be ready for a coding-heavy funnel with repeated probing on how you think, not just whether you know the right answer. Mid-stage and final interviews often test whether you can structure messy data problems, defend modeling choices, explain assumptions, and communicate clearly under pressure.

Interview Rounds
OnsiteTake-home ProjectTechnical Screen
Key Topics
Coding & AlgorithmsMachine LearningStatistics & MathML System DesignData Manipulation (SQL/Python)
Practice Bank

48+ questions

Estimated Timeline

2–4 weeks

Browse all Two Sigma questions

Sample Questions

48+ in practice bank
Statistics & Math
1

Answer four core statistics questions

EasyStatistics & Math

Problem set (timed)

Answer the following four questions.

1) Covariance of order statistics

Let (X) and (Y) be independent (\mathrm{Unif}(0,1)). Define:

  • (U = \min(X, Y))
  • (V = \max(X, Y))

Compute (\mathrm{Cov}(U, V)).

2) Why sample variance uses (n-1)

Explain why the usual sample variance [s^2 = \frac{1}{n-1}\sum_{i=1}^n (x_i - \bar x)^2] uses degrees of freedom (n-1) instead of (n).

3) Sample size for a z-test

How do you estimate the required sample size for a z-test to achieve significance level (\alpha) and power (1-\beta) for detecting a difference (\Delta)? State the formula and assumptions.

4) Prove Cantelli’s inequality

Prove the one-sided Chebyshev (Cantelli) inequality: If (X) has mean (\mu) and variance (\sigma^2), then for any (a>0), [\Pr(X-\mu \ge a) \le \frac{\sigma^2}{\sigma^2 + a^2}.]

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2

Explain why the t-statistic helps

HardStatistics & Math

Suppose you estimate an effect size (\hat{\beta}) in a regression model or an A/B test and compute a standard error (SE(\hat{\beta})).

Explain why the t-statistic [ t = \frac{\hat{\beta}}{SE(\hat{\beta})} ] is often a useful summary of evidence. What does it capture that the raw coefficient or mean difference does not?

Discuss how it connects to p-values and confidence intervals, what assumptions are required, and in what situations relying on the t-statistic can be misleading.

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Data Manipulation (SQL/Python)
3

Analyze NYC taxi trips efficiently over last 7 days

MediumData Manipulation (SQL/Python)Coding

Use today = 2025-09-01. Consider NYC taxi trip data over the last 7 days inclusive (2025-08-26 to 2025-09-01, America/New_York). You receive two datasets and must write a fast, vectorized analysis (no Python for-loops over rows). Data schema and tiny samples:

trips(id, taxi_id, pickup_ts, dropoff_ts, pickup_zone_id, dropoff_zone_id, distance_miles, fare_amount) 1 | 101 | 2025-08-26 08:15 | 2025-08-26 08:45 | 1 | 3 | 6.0 | 18.50 2 | 102 | 2025-08-26 00:20 | 2025-08-26 00:50 | 2 | 1 | 4.0 | 14.00 3 | 101 | 2025-08-28 01:10 | 2025-08-28 01:40 | 1 | 1 | 3.0 | 12.00 4 | 103 | 2025-08-30 17:05 | 2025-08-30 17:25 | 4 | 2 | 2.5 | 9.50 5 | 104 | 2025-09-01 02:30 | 2025-09-01 03:20 | 1 | 4 | 10.0 | 30.00 6 | 102 | 2025-08-31 23:50 | 2025-09-01 00:10 | 3 | 3 | 5.0 | 16.00

zones(zone_id, borough) 1 | Manhattan 2 | Brooklyn 3 | Queens 4 | Bronx

Tasks:

  1. After joining trips with zones on pickup_zone_id, compute per (borough, hour_of_day from pickup_ts) the median trip speed in mph, where speed = distance_miles / duration_hours. Filter trips to 1 ≤ duration_minutes ≤ 120 and 1 ≤ speed ≤ 80. Return the top 3 (borough, hour) pairs by median speed; break ties by borough asc, then hour asc. Report the exact (borough, hour, median_speed_mph) triples. 2) For trips with pickup borough = 'Manhattan' and pickup time between 00:00 and 05:00 inclusive, identify the 3 taxi_id with the largest 95th percentile of trip duration (minutes) over the same date range; break ties by taxi_id asc. Clearly define how you compute the 95th percentile (e.g., pandas/numpy method) and use a stable, vectorized approach. 3) Provide pandas code (or SQL) that runs in O(n log n) or better due to grouping/quantile operations, avoids per-row loops, and uses: parsed datetime dtypes; one-to-many join performed once; categorical dtype for borough; appropriate indexing on pickup_ts for time filtering. 4) Briefly justify two memory/performance optimizations you employ (e.g., downcasting floats/ints, using groupby-agg with quantile in a single pass, avoiding intermediate copies).
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Machine Learning
4

Predict Bike Dock Demand

HardMachine LearningPremium

You are working on a docked bike-sharing system. Build a model that predicts how many bikes will be checked out from a specific dock in the next hour.

Assume you have access to:

  • trips(trip_id, start_time, start_station_id, end_station_id, user_type)
  • station_status(station_id, ts, bikes_available, docks_available, capacity)
  • weather(ts, temperature, precipitation, wind_speed)
  • calendar(date, is_holiday, is_weekend, special_event)

Discuss:

  1. How you would define the prediction target and unit of analysis.
  2. What features you would engineer without leaking future information.
  3. What model family you would start with and why.
  4. Which evaluation metrics you would use, and how your choice changes if the business cares more about stock-outs than raw count error.
  5. How you would split train and validation data for a time-series problem.
  6. How you would prevent overfitting and handle cold-start stations, missing data, and distribution shifts such as severe weather or holidays.
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5

Perform no-intercept linear regression from two datasets

MediumMachine Learning

You are given two pandas datasets to fit an OLS model without an intercept (through origin). Dataset A (features): df_X(user_id, clicks, impressions). Dataset B (target): df_y(user_id, conversions). Tiny samples:

df_X A | 10 | 100 B | 5 | 40 C | 0 | 20

(df_X columns: user_id, clicks, impressions)

df_y A | 4 B | 2 C | 1

(df_y columns: user_id, conversions)

Tasks:

  1. Inner-join on user_id to build X ∈ R^{n×p} with columns [clicks, impressions] and y ∈ R^{n}. Fit the no-intercept model y = Xβ via: (i) normal equations β̂ = (XᵀX)^{-1}Xᵀy; and (ii) a numerically stable factorization (QR or SVD). Show the intermediate matrices (XᵀX, Xᵀy) and the final β̂. 2) Compute R² for the no-intercept model using the correct definition (with TSS = ∑ y_i^2). Explain why this R² can be negative and compare against the standard with an intercept. 3) Discuss when omitting the intercept is appropriate; verify by mean-centering X and y and refitting both with- and without-intercept, commenting on coefficient changes. 4) If XᵀX is singular or ill-conditioned (e.g., clicks collinear with impressions), detect this and refit using ridge regression with λ = 1e−3; give the closed-form β̂_ridge = (XᵀX + λI)^{-1}Xᵀy and compute it on the sample. 5) State how you would validate the model (residual diagnostics, cross-validation) and guard against data leakage when constructing df_X and df_y.
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Coding & Algorithms
6

Evaluate piecewise linear function at x

MediumCoding & AlgorithmsCoding

You are given a polyline defined by n 2D points ((x_i, y_i)). Connecting consecutive points with straight line segments forms a piecewise linear function.

Task

Given a target value x, return the corresponding function value y(x):

  • If x equals some x_i, return y_i.
  • If x lies strictly between x_i and x_{i+1}, linearly interpolate on the segment between ((x_i, y_i)) and ((x_{i+1}, y_{i+1})): [ y(x)=y_i + (y_{i+1}-y_i)\cdot\frac{x-x_i}{x_{i+1}-x_i} ]
  • If x < x_0 or x > x_{n-1}, return null (or a sentinel) because the function is undefined outside the polyline.

Input

  • points: list of n pairs (x, y)
  • x: target x-coordinate

Assumptions / Constraints

  • n >= 2
  • Points are given sorted by strictly increasing x (i.e., x0 < x1 < ... < x(n-1)).

Output

  • A numeric value y(x) (float), or null if out of range.
View full question
7

Match readings with latest same-city humidity

EasyCoding & Algorithms

Problem

You are given two time-sorted lists of sensor readings:

  • Temperature records: (time, city, reading)
  • Humidity records: (time, city, reading)

Example:

Temperature

[(1,  'NYC', 12),
 (5,  'SFO', 20),
 (7,  'NYC', 23),
 (16, 'SFO', 10)]

Humidity

[(0,  'SFO', 12),
 (3,  'SFO', 2),
 (6,  'NYC', 2),
 (19, 'SFO', 9)]

Task

For each temperature record, match it with the most recent humidity record from the same city whose time <= temperature_time.

  • If there is no earlier humidity record for that city, return null (or None) for the humidity reading.

Output

Return a list aligned to the temperature records, where each element includes the temperature record and the matched humidity reading (or null).

Constraints

  • Both input lists are individually sorted by time ascending.
  • Cities may appear in any order within the global time ordering.
View full question
ML System Design
8

Predicting Stock Prices from Twitter Data

HardML System Design

Suppose you have access to a large stream of Twitter data — tweets with their text, timestamps, and author metadata — along with historical market data (prices and trading volumes) for the stocks you care about. How would you use the Twitter data to predict stock prices?

Walk the interviewer through your end-to-end approach: how you would frame the prediction problem, what signal you would extract from tweets and how, what model you would build and train, and — critically — how you would convince yourself the model has real predictive power rather than just fitting noise.

Don't jump straight to a model. First pin down the *target*: predicting a raw price level is much harder and less useful than predicting **returns** or the **direction of movement** over a fixed horizon (next hour, next day). Pick a horizon and a stock universe, and define the label as the forward return computed only from information timestamped *after* the prediction time.

The raw text stream is not a feature. Think in layers: (1) map each tweet to tickers/companies (cashtags like `$AAPL`, entity linking), (2) score each tweet (sentiment, embeddings), and (3) aggregate per stock per time bucket — mean sentiment, tweet **volume** and its deviation from normal, author-credibility weighting. Abnormal tweet volume is often as informative as sentiment polarity.

Financial time series make it very easy to fool yourself. Watch for **look-ahead bias** (using any data timestamped after prediction time), evaluate with **walk-forward, time-ordered splits** (never random shuffles), and always compare against a market-data-only baseline. Expect a tiny edge — directional accuracy barely above 50% can already be meaningful, and claims of 80% accuracy are a red flag.

Constraints & Assumptions

  • Assume a high-volume tweet stream (historically on the order of hundreds of millions of tweets per day) with text, timestamp, and author metadata (followers, account age).
  • Assume you can obtain aligned historical market data (prices, volumes) for your stock universe.
  • The signal-to-noise ratio is extremely low: markets are close to efficient, so any real predictive edge from public tweets will be small.
  • You may scope the problem down (e.g., liquid US large-cap stocks, a daily prediction horizon) as long as you state the assumption.
  • This was asked as an open-ended discussion question: the interviewer cares about how you structure the problem and reason about validity, not about naming one specific model.

Clarifying Questions to Ask

  • What is the prediction horizon — minutes, hours, or days? Is the output meant to drive trading, or is it a research/analysis signal?
  • Which universe of stocks — a handful of heavily tweeted large caps, or thousands of tickers including illiquid names?
  • Besides the tweet stream, what data is available — historical prices, any labeled sentiment data, existing NLP tooling?
  • Are there latency constraints (real-time scoring of the stream), or is offline/daily prediction acceptable?
  • How will the prediction be consumed — as a standalone forecast, or as one signal among many in a larger model?

What a Strong Answer Covers

  • Problem framing: predicting forward returns or direction over an explicit horizon for a defined stock universe (not raw price levels), with a precise label definition that cannot leak future information.
  • Data and feature pipeline: mapping tweets to tickers, filtering bots/spam/retweets, scoring text (sentiment or embeddings), and aggregating into per-stock, per-time-bucket features including volume and velocity signals.
  • Modeling judgment: starting from simple, regularized baselines (linear/logistic models, gradient-boosted trees on aggregated features) and adding complexity only when it demonstrably helps; combining tweet features with market features
View full question
9

Predict Citi Bike Demand at a Specific NYC Station

HardML System Design

Citi Bike is New York City's bike-share system: riders pick up a bike at one docked station and drop it off at another. Operations needs to know how many bikes to stock at each station, so you are asked to build a model that predicts demand — the number of bike pickups — at one specific station.

Walk through, end to end, how you would frame the prediction problem, what data and features you would use, what models you would try, and how you would evaluate whether the model is good enough to drive stocking decisions.

The trip logs record *completed pickups*. When the station has zero bikes available, observed pickups are zero no matter how many people wanted a bike — the demand signal is censored. Decide how your target handles this before modeling.

A seasonal-naive baseline (e.g., "same hour last week", or the historical mean for that hour-of-day and day-of-week) is surprisingly strong for this kind of series. Build it first; then justify anything fancier — lag features plus a gradient-boosted regressor is a strong tabular default — by beating it in backtests.

Random train/test splits leak the future into training. Use a rolling-origin (time-ordered) backtest and report error per forecast horizon against the naive baseline.

Constraints & Assumptions

  • Several years of historical trip records are available (start/end station, start/end timestamps).
  • Weather observations and forecasts, plus calendar data (holidays, day of week), can be joined.
  • Unless the interviewer redirects, assume the operational need is an hourly pickup forecast for the next 24 hours, refreshed at least daily, to plan overnight rebalancing.
  • The model targets one station, but you may use data from other stations for features or training signal.

Clarifying Questions to Ask

  • What decision does this forecast drive — rebalancing trucks, dock sizing, pricing? That determines the horizon, granularity, and which errors are costly.
  • Is "demand" pickups only, or do we also care about dropoffs / net flow at the station?
  • Should the target be observed pickups, or true demand corrected for hours when the station was empty?
  • How far ahead must the forecast go, and how often is it refreshed?
  • Is under-prediction (stockouts, lost rides) more costly than over-prediction (idle bikes), i.e., do we need quantiles rather than a point forecast?
  • Is this a one-off for a single station, or a pilot for all stations in the system?

What a Strong Answer Covers

  • Framing: a precise target (e.g., pickups per hour at station $s$, horizons 1–24h), chosen granularity tied to the operational decision, and a loss aligned with asymmetric stockout costs.
  • Data and features: temporal features (hour-of-day, day-of-week, holidays — with cyclical encodings where appropriate), weather (temperature, precipitation, wind; forecast values for future horizons), autoregressive signal (lags at 1h/24h/168h, rolling means), and station context (nearby-station activity, subway proximity, seasonal trend).
  • Censoring awareness: recognizing that empty-station hours understate demand and proposing at least one mitigation (mask/downweight censored hours, impute from neighboring stations, or a censored-likelihood model).
  • Model progression: naive and seasonal baselines → regularized linear / Poisson regression → gradient-boosted trees on lag features (or a classical time-series model with exogenous regressors), with counts handled via a Poisson/negative-binomial objective or a $\log(1+y)$ transform.
  • Evaluation: rolling-origin backtesting, horizon-specific MAE/RMSE (or Poisson deviance), skill relative to the seasonal-naive baseline, and ideally a business-level check such as simulated stockout rate under the implied stocking policy.
  • Operations: retraining cadence, monitoring forecast error drift, and handling regime chan
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About the Interview Process

What to expect

Two Sigma’s 2026 Data Scientist interview is usually a rigorous multi-round process that blends coding, statistics, applied modeling, and discussion of your past work. The most distinctive feature is that the process is personalized by team and background, so you should expect the broad structure to be similar across candidates, but the exact sequencing and follow-up depth to vary. Some candidates see an online coding assessment very early, and the process may stop before all rounds if the team decides the fit is not there.

You should be ready for a coding-heavy funnel with repeated probing on how you think, not just whether you know the right answer. Mid-stage and final interviews often test whether you can structure messy data problems, defend modeling choices, explain assumptions, and communicate clearly under pressure.

Two Sigma Data Scientist Interview Guide 2026 visual study map Visual study map Screen resume, SQL basics Core skills SQL, stats, product sense Onsite case, metrics, experiments Decision impact and communication Use this map to decide what to practice first, then check each area against the examples in the guide.

Video companion: This verified YouTube video gives a second pass on the same prep area.

Interview rounds

Online assessment

This round is typically a timed online coding test, often in a HackerRank-style environment, and it can arrive soon after you apply. It usually focuses on programming fluency, speed, and correctness under pressure rather than long-form modeling discussion. Expect coding problems that may combine algorithms, data structures, and data-science-style manipulation or statistical reasoning.

Recruiter or hiring manager screen

This is usually a phone or virtual conversation of around 45 minutes. You’ll be asked to walk through your background, explain key projects, and articulate why you want Two Sigma specifically. Interviewers use this round to assess communication, role fit, motivation, and whether you can explain technical work in a clear, structured way.

Technical phone screen

This round is typically a live technical discussion centered on your data science depth rather than pure coding speed. You may be asked to discuss a past project, explain regression or modeling decisions, and justify your methodology under follow-up questioning. The goal is to see whether you understand assumptions, tradeoffs, interpretation, and practical analytical reasoning.

Live coding round

This is a real-time coding interview in a shared environment, usually lasting one standard interview block. You’ll be evaluated on writing working code, choosing efficient approaches, debugging, and narrating your thinking as you go. Two Sigma tends to care about whether you solve the problem and how clearly and methodically you approach it.

Behavioral interview

This is a conversational round focused on collaboration and team fit. You should expect questions about teamwork, disagreement, feedback, and how you communicate technical findings to less technical audiences. The interviewers are looking for evidence that you can work well across functions and operate effectively in an evidence-driven environment.

Final interview loop

The final stage usually consists of several back-to-back interviews, often virtual, covering multiple dimensions of the role. You may face a mix of coding, statistics, modeling, open-ended problem solving, and motivation or culture-fit conversations. This loop tests full-stack fit: technical rigor, analytical judgment, communication, and how well your working style matches the team.

What they test

Two Sigma consistently tests whether you can operate like a practical, rigorous data scientist rather than someone who only knows textbook ML. On the programming side, Python is the main language to prioritize. You should be comfortable writing code live, debugging, using common data structures, and improving solutions when an interviewer asks about optimization. Coding questions can feel algorithmic, but they often still reward data-science intuition, especially when the task involves matching records, processing time-based data, or reasoning about a realistic analytical workflow.

Statistics is one of the clearest recurring themes. You should be ready for OLS and linear regression, hypothesis testing, t-statistics, correlation, missing-data treatment, and questions about inference and bias. It’s not enough to define concepts. You need to explain when assumptions break, what a result means, and how you would respond if the data were messy or incomplete. If you mention a method from a past project, expect follow-ups on why you chose it, what alternatives you considered, and how you validated it.

The modeling side is practical and decision-oriented. Expect discussion of feature design, forecasting, predictive modeling, overfitting, model selection, validation, and preprocessing. Interviewers often care more about whether you can frame an ambiguous problem correctly than whether you can recite advanced theory. You may be asked to turn a vague prompt into an end-to-end analysis plan, define metrics, choose a modeling approach, and explain how you would evaluate success.

Communication is tested in every round, not just behavioral. Two Sigma places a premium on scientific thinking and evidence-based reasoning, so you should be ready to explain your thought process step by step, defend tradeoffs, and connect technical work to a research or business objective. In project discussions, they often probe for depth: what the problem was, what data issues you faced, what assumptions you made, what impact your work had, and what you would change in hindsight.

How to stand out

  • Narrate your reasoning continuously in coding and technical rounds. Two Sigma interviewers repeatedly probe how you think, so silence hurts you more here than at companies that only score the final answer.
  • Prepare one or two projects at extreme depth. Be ready to explain the problem framing, feature choices, data quality issues, statistical assumptions, validation strategy, tradeoffs, and measurable impact.
  • Refresh core statistics, especially regression, hypothesis testing, correlation, and missing-data handling. You should be able to move from formulas to interpretation without sounding scripted.
  • Practice turning ambiguous prompts into a concrete analysis plan. Two Sigma often values how you structure messy, real-world problems as much as the final model you choose.
  • Ask clarifying questions before solving. This signals the scientific, evidence-based mindset they value and helps you avoid jumping into a polished but mis-scoped answer.
  • Show practical judgment, not just theory. If you propose a model, explain why it fits the data, what can go wrong, how you would validate it, and when a simpler approach might be better.
  • Tailor your “Why Two Sigma” answer to their culture: scientific reasoning, collaboration, and connecting analytical rigor to meaningful decisions. Generic interest in finance or ML will be less convincing than a clear match to how they work.

How to Use This Page as a Prep Plan

Do not treat this as passive reading. Convert the ideas in this page into a short weekly loop: learn one idea, practice it under interview conditions, then write down what changed. That is the fastest way to turn advice into visible interview behavior.

Prep areaWhat you need to provePractice artifact
Metric framingDefine the unit, window, and denominator.One clear metric contract.
SQL executionUse readable CTEs and test row counts.A query with checks after each join.
StatisticsConnect methods to decision risk.Assumptions, confidence, and caveats.
CommunicationTurn findings into a recommendation.One concise business interpretation.

For Two Sigma Data Scientist Interview Guide 2026, the strongest candidates usually do three things well: they make their assumptions explicit, they use concrete examples instead of vague claims, and they review mistakes quickly enough that the next practice rep is better than the last one.

FAQ

What matters most in data interviews?

Clear assumptions, correct query structure, and the ability to explain what the result means.

How should I practice SQL?

Practice with messy business prompts, then write checks for joins, nulls, duplicates, and time windows.

How do I handle ambiguous metrics?

State a default definition, explain the tradeoff, and ask whether the interviewer wants a different lens.

Frequently Asked Questions

It is definitely on the hard side, mostly because they test both technical depth and how you think through messy real-world problems. I found it less about memorizing tricks and more about being sharp with statistics, experimentation, modeling choices, and communication. The bar feels high because many candidates already have strong math and coding backgrounds. You need to be comfortable under pressure, explain tradeoffs clearly, and stay structured when the problem is open-ended or ambiguous.

The process usually starts with a recruiter screen, then a technical phone or video round. After that, there can be interviews focused on statistics, machine learning, coding, and case-style product or research questions. In my experience, they also cared a lot about how I reasoned through experiments and data issues, not just whether I got to an answer fast. The onsite or final loop often mixes technical depth, problem solving, and behavioral conversations with people from different teams.

For most people, I would say four to eight weeks of steady prep is a good target, assuming you already have a solid base in Python, SQL, probability, and machine learning. If your statistics background is rusty, give yourself longer. What helped me most was doing a little every day instead of trying to cram. I split time between probability review, coding practice, experiment design, and talking through open-ended data science questions out loud until my explanations sounded natural.

The biggest ones are probability, statistics, hypothesis testing, regression, experiment design, machine learning fundamentals, and coding in Python or SQL. I would also spend real time on data cleaning, feature thinking, model evaluation, bias and leakage, and how to choose metrics. They seem to like candidates who can move between theory and practice without getting lost. You should be able to explain why a method makes sense, what can go wrong, and how you would validate results before trusting them.

The biggest mistake is jumping into an answer without setting up assumptions or clarifying the goal. I also saw how easy it is to sound polished but not actually answer the question. Weak fundamentals in probability or statistics get exposed fast. Another common problem is treating every question like a Kaggle problem instead of a business or research problem with tradeoffs. Bad communication hurts too: rambling, hiding uncertainty, not checking edge cases, or failing to explain why your approach beats simpler alternatives.

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