Senior Staff Data Scientist - Consumer Relevance
New
United StatesFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- For M.S.: 12+ years; For Ph.D.: 8+ years
- Required Skills
- PythonSQLData scienceR
Requirements
- Ph.D. or M.S. in Statistics, Computer Science, Economics, Information Retrieval, or a related quantitative field
- For M.S.: 12+ years of relevant industry experience in data science or relevance/ranking-focused roles
- For Ph.D.: 8+ years of relevant industry experience in applied science or ranking/recommendation systems
- Deep expertise in metrics design and evaluation for ranking, recommendation, or search systems
- Strong background in causal inference, experimental design, and counterfactual evaluation techniques
- Experience working with large-scale user-generated content or social platforms is highly desirable
- Strong proficiency in SQL and programming languages such as Python and/or R
- Ability to influence product and technical strategy through data-driven insights
- Excellent communication skills for explaining complex statistical and ML concepts to diverse stakeholders
- Experience mentoring data scientists and building organizational expertise in experimentation and relevance systems
Responsibilities
- Serve as the technical authority on relevance metrics and evaluation methodologies across feeds, search, and recommendation systems
- Design and develop offline and online evaluation frameworks to measure ranking quality and long-term user outcomes
- Build robust metrics systems to capture content quality, user satisfaction, retention, and community health signals
- Design and analyze large-scale experiments, accounting for network effects, spillovers, and ranking system biases
- Identify opportunities to improve measurement frameworks and unlock previously unmeasurable product insights
- Partner with ML engineers and product teams to translate model performance into user-facing impact and product decisions
- Influence product strategy for consumer relevance through deep analytical insights and experimentation results
- Mentor and guide data scientists on causal inference, experimentation design, and relevance evaluation best practices
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