Staff Data Scientist - Experimentation & Causal Inference
New
J
JobgetherB2B SaaS
United StatesFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 9+ years
- Required Skills
- PythonSQLMachine LearningProduct AnalyticsData scienceR
Requirements
- 9+ years of experience in data science, product analytics, applied statistics, or related fields.
- Deep hands-on experience designing, running, and analyzing online controlled experiments at scale.
- Strong foundation in applied statistics, including frequentist methods, Bayesian approaches, power analysis, and variance reduction.
- Practical expertise in causal inference, including matching methods, difference-in-differences, instrumental variables, and synthetic controls.
- Experience working in small-sample, high-velocity, multi-product environments.
- Strong SQL skills.
- Professional experience with Python or R.
- Ability to influence product managers, analysts, engineers, and executives without direct authority.
- Strong communication skills with the ability to explain technical concepts to technical and non-technical audiences.
- Experience building experimentation frameworks or cultures from the ground up (preferred).
- Familiarity with experimentation platforms such as Statsig (preferred).
- Background in B2B SaaS, CRM, product-led growth, or multi-tenant platforms (preferred).
Responsibilities
- Define and establish company-wide experimentation methodologies covering hypothesis development, metrics selection, experiment design, statistical analysis, reporting, and decision-making.
- Create standards and best practices for controlled experiments to improve product development and business strategy.
- Own statistical approaches for experimentation, including significance testing, multiple comparisons, sequential testing, variance reduction techniques, and power analysis.
- Develop frameworks for analyzing complex hierarchical and clustered data structures.
- Apply advanced causal inference techniques such as matching methods, difference-in-differences, instrumental variables, and synthetic controls.
- Design strategies for managing multiple concurrent experiments, including test layering, holdouts, guardrails, and contamination prevention.
- Partner with AI and machine learning teams to design evaluation frameworks for AI-powered features.
- Lead experiment review processes and ensure conclusions are statistically sound and actionable.
- Build educational resources, templates, and training programs to improve experimentation skills across product and analytics teams.
- Collaborate with analytics engineering teams to improve data quality, governance, and experiment readiness.
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