Answer common data science video interview prompts
Company: Transunion
Role: Data Scientist
Category: Behavioral & Leadership
Difficulty: medium
Interview Round: Technical Screen
You are completing an asynchronous video interview for a Data Scientist Intern role. You will be shown several prompts and must record a ~2-minute answer for each.
Prepare strong answers for the following prompts:
1. **Python/R problem solving:** Describe a problem you solved using Python and/or R. What was the problem, what did you do, and what was the outcome?
2. **SQL usage:** Describe a situation where you used SQL. Why did you use it, what queries/techniques did you rely on, and what did you deliver?
3. **Ambiguous/low-context project:** Tell us about a project where you had little context or unclear requirements. How did you proceed?
4. **Explaining to non-technical audiences:** Pick one of your projects and explain it to someone without a technical background.
5. **Keeping a team aligned:** In a team project, how do you keep everyone aligned and moving in the same direction?
Constraints/format:
- ~2 minutes per prompt.
- Focus on clear structure, concrete actions, and measurable impact.
- Assume an interviewer cares about analytical thinking, communication, and collaboration.
Quick Answer: This question set evaluates technical proficiency with Python/R and SQL, analytical problem solving, the ability to explain complex analyses to non-technical audiences, handling of ambiguous requirements, and collaboration and leadership in team settings.
Solution
## General approach (what the interviewer is evaluating)
Across all 5 prompts, they’re looking for evidence of:
- **Ownership:** you can define problems, not just execute tasks.
- **Rigor:** you validate data, check assumptions, and measure outcomes.
- **Communication:** you can tailor detail to technical vs non-technical audiences.
- **Collaboration:** you coordinate, document, and handle tradeoffs.
A reliable structure for 2-minute answers is **STAR + Metrics**:
- **S/T (20–30s):** Situation + your specific task.
- **A (60–70s):** What you *personally* did (tools, decisions, tradeoffs).
- **R (20–30s):** Result with numbers or concrete deliverable.
- **Reflection (10s):** What you’d do next / what you learned.
## 1) “What problem did you solve with Python/R?”
### What to cover
- Problem statement and why it mattered.
- Your pipeline: data ingestion → cleaning → analysis/model → validation.
- Key libraries/tools (pandas, scikit-learn, statsmodels, tidyverse, ggplot2, etc.).
- Validation and impact (accuracy lift, time saved, error reduced).
### Strong answer template
- **S/T:** “We needed to ___ because ___.”
- **A:** “I collected data from ___, cleaned ___, engineered features ___, tried ___ models/approaches, evaluated with ___, and iterated.”
- **R:** “Improved ___ by X% / reduced manual work by Y hours/week / shipped a dashboard/report used by ___.”
- **Reflection:** “Next I would ___ to handle ___ edge cases.”
### Pitfalls to avoid
- Listing libraries without explaining decisions.
- No evaluation method (train/test split, cross-val, backtesting, sanity checks).
## 2) “Describe a scenario where you used SQL.”
### What to cover
- The business question and the data sources (tables, grain).
- How you ensured correctness: joins, filters, deduping, handling NULLs.
- Performance awareness: indexes/partition filters, avoiding expensive patterns.
- Deliverable: dataset for modeling, KPI definition, report.
### Mention practical SQL techniques (choose what’s true)
- **Joins:** inner/left, join keys, handling many-to-many.
- **Aggregations:** GROUP BY, distinct counts, cohorting.
- **Window functions:** ROW_NUMBER for dedupe, LAG for retention, rolling metrics.
- **CTEs:** readability and staged logic.
- **Data quality checks:** row counts, uniqueness, reconciliation vs source.
### Pitfalls
- Not stating the table grain (user-day vs transaction-level).
- Confusing LEFT JOIN semantics or double-counting.
## 3) “A project with little context—how did you proceed?”
### What to cover (this is about ambiguity handling)
1. **Clarify goal:** define success metric and stakeholders.
2. **Ask targeted questions:** constraints, timeline, data availability, risk.
3. **Propose an MVP:** smallest useful deliverable.
4. **Iterate with feedback:** short checkpoints, documented assumptions.
5. **De-risk early:** data audit, feasibility test, baseline.
### Example phrasing
- “I started by aligning on *what decision* this analysis would support.”
- “I listed assumptions explicitly and validated them with a quick exploratory analysis.”
- “I built a baseline first, then improved it in iterations.”
### Pitfalls
- Acting without stakeholder alignment.
- Overbuilding a complex model before confirming the metric/data.
## 4) “Explain your project to someone non-technical.”
### What to do
Use a **three-layer explanation**:
1. **Why:** business problem in plain language.
2. **What:** solution conceptually (no jargon).
3. **So what:** impact and how it changes decisions.
### Helpful techniques
- **Analogy:** e.g., “We’re predicting risk like a credit score, but for ___.”
- **Avoid jargon:** say “past behavior signals” instead of “features,” “checked accuracy” instead of “AUC.”
- **One metric only:** pick a single easy metric (time saved, fewer errors, higher approval rate).
### Pitfalls
- Diving into model details (hyperparameters, architectures) too early.
- Not explaining how someone *uses* the output.
## 5) “How do you keep everyone aligned in a team project?”
### What to cover
- **Shared goals:** define scope, timeline, definition of done.
- **Clear ownership:** RACI-like clarity (who owns what).
- **Communication cadence:** standups, weekly updates, async notes.
- **Documentation:** decision log, requirements, data definitions.
- **Risk management:** surface blockers early, negotiate tradeoffs.
- **Conflict handling:** align on objectives, propose options, decide and commit.
### Practical playbook
- Kickoff: goals, milestones, roles.
- Weekly: progress vs plan + risks.
- Artifacts: shared doc, Jira/Trello, dashboard, meeting notes.
- After: retro—what worked, what to change.
## How to maximize score in the 2-minute format
- Open with a **one-sentence headline**: “I built X to solve Y, resulting in Z.”
- Use **numbers** (even approximate): “cut processing time from 2 hours to 10 minutes.”
- Make your role explicit: “I owned the data pipeline and evaluation.”
- End with **reflection**: “I learned __; next time I’d __.”
## Quick checklist before recording
- 1–2 stories you can reuse across prompts (technical + teamwork).
- Quantified impact prepared.
- One non-technical explanation practiced.
- Keep it tight: problem → action → result → learning.