Data Scientist

Ryan Mahoney

Ryan Mahoney

Director of Product, FirstWho

“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.”

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Competency Questions

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Analytics, Experimentation & Business Intelligence

Covers product analytics, experimentation design, business intelligence dashboards, and financial analytics to drive data-informed decision-making. At DS II level, emphasizes independent experiment design, custom dashboard creation, and metric definition for product impact.

Business Intelligence & Dashboard Development

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

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

Attitude Questions

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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

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.