Senior Data Scientist

Job Description

Responsibilities

Key Responsibilities

Data Science & Analytical Problem Solving

• Translate ambiguous business questions into well-defined analytical hypotheses, causal frameworks, and measurable outcomes.
• Own the end-to-end data science lifecycle, including data acquisition, exploration, validation, feature engineering, statistical modeling, causal inference, and results interpretation.
• Apply advanced statistical and machine learning techniques to uncover drivers of performance, behavior, and outcomes.
• Ensure analytical outputs are statistically sound, reproducible, and decision-ready.
  

Experimentation, Causal Inference & Measurement

• Design, implement, and analyze experiments (e.g., A/B tests, quasi-experiments, pilots) to estimate the causal impact of interventions.
• Apply causal inference methods (e.g., matching, regression, difference-in-differences) to address confounding, bias, and incomplete data.
• Establish measurement frameworks that balance rigor with practical constraints in real-world data environments.
  

Data Interpretation & Insight Communication

• Synthesize complex analytical results into clear, actionable insights that influence strategy and product or program decisions.
• Communicate findings through compelling data narratives and visualizations tailored to technical and non-technical stakeholders.
• Collaborate cross-functionally to align on assumptions, metrics definitions, and interpretation of results.
  

Scalable & Reproducible Analytics

• Develop standardized metrics, analytical frameworks, and reusable data science assets.
• Contribute to scalable dashboards and reporting pipelines that support ongoing measurement and experimentation.
• Partner with Data Engineering and Analytics teams to ensure data reliability, consistency, and analytical best practices.
  

Qualifications

Qualifications

• 4+ years of experience in data science or analytics roles, preferably in product, web, customer care, or customer experience analytics.
• Strong proficiency in SQL and experience working with large-scale data platforms (e.g., Spark, Databricks, BigQuery, Redshift).
• Experience using BI and visualization tools (e.g., Tableau, Qlik, Dash) to deliver clear, stakeholder-ready insights.
• Solid experience with experimentation and causal analysis (e.g., A/B/n testing, applied causal methods), with good judgment on when and how to apply them.
• Familiarity with AI/ML and GenAI-enabled analytics, and the ability to reason about implications for measurement, experimentation, and user behavior.
• Strong business acumen, with the ability to translate business questions into testable hypotheses and actionable insights.
• Excellent data storytelling and communication skills, with the ability to influence decisions across technical and non-technical audiences.
• Comfortable working in a fast-paced, ambiguous environment, with flexibility to shift priorities and collaborate across cross-functional teams.

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