Data Scientist

Ryan Mahoney

Why this role is hard · Ryan Mahoney

Stop looking for wizards who never make mistakes. Most candidates can train a model in a notebook, but few can ship it to production without breaking things. We need builders who own the mess and admit when a feature engineering choice tanks a metric instead of hiding it. The interview should feel like a code review rather than a trivia game, so ask them about a time they deleted their own model because it was not working.

Core Evaluation

Critical questions for this role

The competency and attitude questions below are where the hiring decision is made. They run in the live interview rounds and are calibrated to the level selected above.

20 Competency Questions

1 of 20

  1. Discipline

    Analytics, Experimentation & Business Intelligence

  2. Job requirement

    Business Intelligence & Dashboard Development

    Designs custom dashboards based on stakeholder requirements and optimizes query performance for recurring reports.

  3. Expected at Mid

    At the DS II level, independent dashboard development is essential for translating stakeholder requirements into actionable insights. Without this capability, misaligned expectations and delayed decision-making erode trust in data products. A proficiency of 3 ensures the data scientist can autonomously design, optimize, and maintain recurring reports that directly support stakeholder satisfaction and business alignment.

Interview round: Hiring Manager Technical

Walk me through a dashboard or report you designed for a non-technical team.

Positive indicators

  • Mentions stakeholder interviews during design
  • Tracks usage metrics post-launch
  • Simplifies complex data for clarity

Negative indicators

  • Built in isolation without feedback
  • No follow-up on usage or utility
  • Overly technical visualizations

15 Attitude Questions

1 of 15

Accountability Mindset

The consistent willingness to accept responsibility for data integrity, model performance, and ethical implications, characterized by transparent communication of limitations and proactive remediation of errors.

Interview round: Hiring Manager Technical

A model you deployed starts performing poorly in production. What steps do you take?

Positive indicators

  • Mentions monitoring alerts
  • Describes incident response process
  • Notes post-mortem analysis

Negative indicators

  • Waits for someone else to notice
  • Tries to tweak results manually
  • Ignores minor performance dips

Supporting Evaluation

How candidates earn the selection conversation

The goal is to reduce effort for everyone by collecting more useful signal before adding more interviews. Lightweight application prompts and structured screens help the panel focus live time on the candidates most likely to succeed.

Stage 1 · Application

Filter at the door

Runs the moment a candidate hits Submit. Disqualifying answers end the application; everything else is captured for review.

Knock-out Questions

1 of 2

Application Screen: Knock-out

How many years of professional experience do you have deploying machine learning models or APIs to production environments using Python?

Less than 1 year
Auto-decline
1-2 years
Qualifies
3-5 years
Qualifies
5+ years
Qualifies

Video-Response Questions

1 of 3

Application Screen: Video Response

You've run an A/B test that shows a promising lift in retention, but the results are not yet statistically significant due to low traffic. A product manager is pushing to launch the feature immediately to meet a quarterly goal. Describe exactly how you would communicate the risks and next steps to them in a 10-minute meeting.

Candidate experience

REC
0:42 / 2:00
1Record
2Review
3Submit

Response time

2 min

Format

Recorded video

Stage 2 · Resume Screening

Read the resume against fixed criteria

Reviewers score every application that clears the door against the same criteria. Stronger reviews advance to live interviews; weaker ones are archived without further screening.

Resume Review Criteria

8 criteria
Designs, trains, and productionizes supervised learning models, implementing versioning, API serving, and performance monitoring.
Implements variance reduction, cohort analysis, and causal inference techniques to evaluate product features and guide roadmap decisions.
Builds retrieval-augmented generation systems or integrates LLM APIs, focusing on prompt engineering, latency optimization, and evaluation frameworks.
Establishes and enforces data quality standards, schema validation, and testing protocols to prevent pipeline failures and ensure downstream reliability.

Does the cover letter or personal statement convey clear relevance and familiarity with the job?

Does the resume indicate required academic credentials, relevant certifications, or necessary training?

Is the resume complete, well-organized, and free from formatting, spelling, and grammar mistakes?

Does the resume show relevant prior work experience?

Stage 3 · During Interviews

Where the hire is decided

Interview rounds use the competency and attitude questions outlined above, then add tests, work simulations, and presentations that reveal deeper evidence about how the candidate thinks and works.

Coding Test

1 of 2

Live Interview · Coding Test

Without AI

Outline your model architecture, feature engineering pipeline, evaluation metrics, and deployment monitoring plan. Provide a Python class structure for training and inference.

Design a churn prediction model for our AI developer workflow platform. Specify your target definition, feature engineering strategy (including behavioral and usage signals), model choice justification, evaluation metrics, and how you will measure post-deployment business impact. Provide a Python class skeleton for the training pipeline.

With AI

Use AI to draft pipeline structure or suggest features, but critically evaluate leakage risks, metric appropriateness, and deployment constraints. Document AI validation steps.

Design a churn prediction model for our AI developer workflow platform. Specify your target definition, feature engineering strategy (including behavioral and usage signals), model choice justification, evaluation metrics, and how you will measure post-deployment business impact. Provide a Python class skeleton for the training pipeline. If you use AI, show how you validated feature leakage risks and metric suitability.

Response time

20 min

Positive indicators

  • Clear target definition with business-aligned time window
  • Thoughtful feature selection addressing leakage and latency
  • Appropriate evaluation metrics (precision-recall, calibration)
  • Post-deployment impact measurement via A/B test or uplift modeling
  • AI used for scaffolding, not architecture
  • Explicit identification and mitigation of AI-suggested feature leakage
  • Critical evaluation of AI metric recommendations
  • Clear documentation of AI validation process

Negative indicators

  • Vague target definitions or lookahead leakage
  • Ignoring feature latency or real-time constraints
  • Overreliance on accuracy for imbalanced data
  • No plan for measuring business impact post-launch
  • Uncritical adoption of AI feature lists without leakage checks
  • Accepting AI-suggested metrics without business context
  • Missing validation of AI pipeline structure
  • No critique of AI assumptions

Presentation Prompt

Walk us through a past project where you navigated ambiguous feature requirements to define success metrics and guardrails before engineering build began. Discuss how you aligned cross-functional stakeholders, iterated on metrics, and handled scope changes.

Format

deck-and-walkthrough · 20 min · ~2 hr prep

Audience

Product Manager, Senior Data Scientist, and Engineering Lead

What to prepare

  • A 3-5 slide deck summarizing the project context, your approach to metric definition, stakeholder alignment, and outcomes
  • Focus your narrative on your decision-making process rather than just the final results

Deliverables

  • A structured deck-and-walkthrough presentation
  • Interactive Q&A on trade-offs and stakeholder management

Ground rules

  • Use only work you are permitted to share; anonymize proprietary data or internal metrics if necessary
  • Focus on your reasoning, process, and adaptation, not just the final artifact or outcome

Scoring anchors

Exceeds
Demonstrates exceptional clarity in translating ambiguity into actionable metrics, shows strong cross-functional leadership, and articulates nuanced trade-offs with clear business impact.
Meets
Presents a coherent project walkthrough with clear metric definitions, stakeholder alignment, and reasonable handling of scope changes.
Below
Struggles to explain metric rationale, lacks stakeholder alignment evidence, or presents a rigid approach that ignores ambiguity and constraints.

Response time

20 min

Positive indicators

  • Clearly articulates how ambiguous requirements were translated into measurable success criteria
  • Demonstrates active stakeholder alignment and iterative feedback loops
  • Surfaces trade-offs between metric rigor and engineering feasibility
  • Handles scope changes with structured boundary-setting and prioritization
  • Connects data work directly to business outcomes and user impact

Negative indicators

  • Presents metrics without explaining the rationale or stakeholder input
  • Ignores engineering constraints or cross-functional friction
  • Lacks clarity on how guardrails were defined or enforced during development
  • Fails to discuss iteration or adaptation when requirements shifted
  • Over-indexes on technical details while omitting business context or product sense

Work Simulation Scenario

Scenario. You are a Data Scientist II tasked with designing an A/B testing strategy for a new AI-powered code recommendation feature. The product team wants to launch a pilot, but success metrics are currently vague ('improve developer productivity'), and there are strong cross-functional concerns about latency, user trust, and experiment contamination. You must lead a discovery conversation to define precise primary and guardrail metrics, determine the experimental unit, address potential interference, and establish a rollout plan that satisfies both product and engineering stakeholders.

Problem to solve. Frame the experimentation strategy by asking targeted questions about metric definitions, statistical power, interference risks, and rollout constraints to produce a clear test design.

Format

discovery-interview · 40 min · ~2 hr prep

Success criteria

  • Defines primary, secondary, and guardrail metrics with clear operational definitions
  • Identifies experiment design risks (e.g., network effects, contamination) and proposes mitigations
  • Aligns on sample size, duration, and decision criteria

What to review beforehand

  • A/B testing fundamentals and common pitfalls
  • Metric hierarchy and guardrail design
  • Experimentation platform capabilities at Series B scaleups

Ground rules

  • Drive the conversation to uncover constraints and definitions
  • Focus on experimental design and metric alignment, not implementation details
  • Do not produce a formal document during the call

Roles in scenario

Jordan Lee, Product Manager (informed_partner, played by cross_functional)

Motivation. Wants to validate that AI recommendations increase developer engagement without degrading IDE performance or causing user frustration.

Constraints

  • Feature flag infrastructure has a 5% traffic cap for new experiments
  • Engineering team is concerned about increased API latency from LLM calls
  • Historical data shows high variance in developer session lengths

Tensions to introduce

  • PM initially wants to measure success by 'number of code completions', which may incentivize low-quality suggestions
  • PM is unaware of potential network effects if recommendations spread across teams
  • Engineering wants a strict 2-week timeline, but variance requires longer duration

In-character guidance

  • Provide honest answers about traffic caps, latency limits, and business goals
  • Clarify definitions when pressed, but don't volunteer them upfront
  • Push back gently if the candidate ignores engineering constraints

Do not

  • Do not suggest the correct experimental design or metrics
  • Do not reveal latency caps or traffic limits unless explicitly asked
  • Do not accept vague metric definitions without asking for clarification

Scoring anchors

Exceeds
Rigorously deconstructs vague goals into a statistically sound experiment design, anticipates interference and latency risks, and aligns cross-functional stakeholders on a phased, data-driven rollout.
Meets
Defines clear primary and guardrail metrics, accounts for basic experimental constraints, and proposes a reasonable test duration and sample size strategy.
Below
Relies on vanity metrics, ignores engineering or statistical constraints, proposes an experiment without clear success criteria, or fails to address contamination risks.

Response time

40 min

Positive indicators

  • Asks targeted questions to operationalize vague goals into measurable primary and guardrail metrics
  • Identifies interference, contamination, or network effect risks and proposes structural mitigations
  • Calculates or discusses sample size, duration, and statistical power realistically given traffic constraints
  • Balances product velocity with statistical rigor by proposing phased rollout or sequential testing

Negative indicators

  • Accepts vague metrics like 'productivity' without defining operational proxies
  • Ignores traffic caps, latency constraints, or variance when planning experiment duration
  • Proposes naive randomization without considering unit-of-diversion or contamination risks
  • Fails to establish clear stopping rules or decision criteria for the experiment

Progression Framework

This table shows how competencies evolve across experience levels. Each cell shows competency at that level.

Analytics, Experimentation & Business Intelligence

4 competencies

CompetencyJuniorMidSeniorPrincipal
Business Intelligence & Dashboard Development

Creates and maintains standard dashboards using established templates and follows documented BI development practices.

Designs custom dashboards based on stakeholder requirements and optimizes query performance for recurring reports.

Architects BI solutions across multiple data sources and establishes dashboard governance standards for the organization.

Defines enterprise BI strategy, evaluates new visualization technologies, and mentors teams on analytics best practices.

Experimentation & A/B Testing

Executes predefined A/B tests and produces standard analysis reports following established protocols.

Designs experiment frameworks, calculates sample sizes, and interprets results with statistical significance testing.

Leads experimentation strategy, establishes testing governance, and integrates experiment insights into product roadmaps.

Defines organizational experimentation maturity, advances causal inference methods, and builds experiment platforms.

Financial & Payments Analytics

Produces standard financial reports and monitors payment system metrics using established templates.

Conducts revenue analysis, identifies payment anomalies, and supports financial forecasting activities.

Leads financial analytics strategy, integrates payment data with business metrics, and advises on pricing optimization.

Defines enterprise financial analytics architecture, drives revenue intelligence initiatives, and partners with executive leadership.

Product Analytics & Metrics

Tracks predefined product metrics and generates routine analytics reports for product teams.

Defines new metrics based on product goals and conducts deep-dive analyses to identify user behavior patterns.

Establishes product analytics frameworks, aligns metrics with business objectives, and leads cross-functional analytics initiatives.

Shapes product strategy through advanced analytics, builds predictive user models, and defines organizational metrics standards.

Data Engineering, ML Systems & Governance

5 competencies

CompetencyJuniorMidSeniorPrincipal
AI & LLM Systems Implementation

Uses pre-trained models and APIs to implement AI features following established patterns.

Fine-tunes LLMs for specific use cases, evaluates model outputs, and implements prompt engineering strategies.

Architects AI systems, optimizes model performance and cost, and establishes AI governance practices.

Defines AI strategy, evaluates cutting-edge models, and leads organizational AI transformation initiatives.

Data Governance & Compliance

Follows data governance policies, implements access controls, and documents data lineage.

Conducts privacy impact assessments, manages data classification, and ensures compliance with regulations.

Designs data governance frameworks, leads compliance audits, and establishes data stewardship programs.

Defines enterprise data governance strategy, partners with legal on data policy, and shapes industry standards.

Data Pipeline Development & Operations

Implements predefined data pipelines using established patterns and monitors pipeline health.

Designs data pipelines for new use cases, optimizes performance, and implements data quality checks.

Architects scalable data infrastructure, establishes pipeline governance, and leads data platform initiatives.

Defines enterprise data architecture strategy, evaluates emerging data technologies, and builds data platform teams.

Machine Learning Model Development

Implements standard ML models using established libraries and follows model development workflows.

Selects appropriate algorithms, tunes hyperparameters, and validates model performance against business metrics.

Leads ML model architecture decisions, implements MLOps practices, and mentors junior data scientists.

Defines ML strategy aligned with business goals, advances model innovation, and builds ML engineering capabilities.

Strategic Leadership & Org Enablement

Participates in data strategy discussions and supports cross-functional data initiatives.

Leads data projects across teams, communicates insights to stakeholders, and mentors junior analysts.

Defines data strategy for business units, builds data culture, and partners with leadership on data investments.

Shapes enterprise data vision, drives organizational transformation, and represents data function at executive level.