Senior Data Scientist, Consumer
New
United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3–5+ years
- Required Skills
- PythonSQLMachine LearningProduct AnalyticsData scienceA/B testingR
Requirements
- Advanced degree (Master’s or PhD) in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, Engineering, or related discipline.
- 3–5+ years of experience in applied data science, product analytics, or experimentation-focused roles.
- Strong expertise in statistical modeling, causal inference, A/B testing, and experimental design.
- Proficiency in Python or R and SQL, with experience working on large-scale datasets.
- Solid understanding of machine learning techniques and their application to real-world product problems.
- Experience working cross-functionally with product and engineering teams in a consumer tech environment.
- Strong communication skills with the ability to translate technical findings into business impact.
- Ability to thrive in ambiguous, fast-paced environments with multiple competing priorities.
- Strong product intuition and curiosity about user behavior and digital engagement.
Responsibilities
- Drive data science initiatives focused on consumer behavior, engagement, and product performance across large-scale digital platforms.
- Design and analyze experiments (A/B tests and causal inference models) to evaluate product changes and user experience improvements.
- Develop and maintain scalable models and metrics to understand user behavior, retention, and growth dynamics.
- Partner with product managers, engineers, and analysts to define success metrics and guide data-informed decision-making.
- Translate complex datasets into clear insights, recommendations, and storytelling for technical and non-technical stakeholders.
- Build and optimize analytical frameworks to support experimentation, forecasting, and user segmentation.
- Identify opportunities to improve user experience through data-driven hypotheses and experimentation roadmaps.
- Ensure data quality, integrity, and reproducibility across analyses and reporting systems.
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