Data Scientist, Analytics

Key Responsibilities

• Analytics, Monitoring & Tooling
• Build and maintain dashboards, monitoring systems, and automated reporting to track product, business, and model performance.
• Develop scalable analytics pipelines to surface key metrics, detect anomalies, and support timely issue diagnosis.
• Define and refine KPIs, using structured, hypothesis-driven analysis to understand performance changes and long-term trends.
• Insights Discovery & Model Evaluation
• Analyze large datasets to identify trends, diagnose performance changes, and uncover growth opportunities.
• Conduct exploratory analysis, root-cause investigations, and hypothesis-driven deep dives.
• Support engineering and research science teams in evaluating model performance, including stability, calibration, and long-term health.
• Experimentation & Measurement
• Assist in designing A/B tests, computing key metrics, and interpreting results.
• Apply basic statistical reasoning (e.g., variance, confidence intervals, significance testing) to support decision-making.
• Cross-Functional Collaboration
• Partner with engineering to integrate new data sources, refine data structures, and enable scalable analytics.
• Work with product and business teams to understand analytical needs and translate them into actionable solutions.

Requirements & Tech Stack

• Proficiency with SQL
• Proficiency with at least one analytical programming language (Python preferred)
• Ability to work with large datasets
• Ability to translate findings into actionable recommendations
• Ability to translate business questions into analytical frameworks
• Ability to communicate insights effectively to both technical and non-technical audiences

Qualifications

• Minimum Qualifications
• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Economics, Engineering, or another quantitative discipline.
• Basic understanding of core statistical concepts and introductory modeling techniques, with the ability to apply them in practical analysis.
• 0–3 years of experience in data analytics or data science (internships or projects count).
• Curious, proactive mindset with a desire to learn quickly and contribute meaningfully.
• Preferred Qualifications
• Master’s degree in Data Science or a related quantitative discipline.
• Experience with BI tools such as Looker, Tableau, Superset, Metabase, or similar.
• Familiarity with cloud data warehouses (e.g., BigQuery, Snowflake) and workflow orchestration tools such as Airflow.
• Exposure to A/B testing, experiment design, or statistical evaluation.
• Understanding of digital advertising, performance marketing metrics, or online marketplace dynamics.
• Experience collaborating with cross-functional teams (engineering, product, business).