Lead Data Scientist, Inference

This job was selected by the SpringCube team to help AI, Data and Cloud Engineers discover relevant San Francisco Bay Area employers. Sign up to view the full employer details and apply directly with the hiring company.

Company Overview

A leading global fitness and activity platform connects more than 200 million athletes across more than 185 countries. The platform enables people to track workouts, discover new activities, connect with communities, set goals, and celebrate personal achievements across a wide range of sports and fitness experiences.

The organization is focused on motivating people to live active lives through movement, connection, and shared experiences. Its Data Science team works across the business to solve complex, high-impact challenges by applying machine learning, causal inference, and measurement systems to transform unique data assets into actionable models, metrics, and recommendations.

The organization is seeking a Lead Data Scientist, Inference to develop advanced measurement strategies and causal frameworks that connect product and marketing initiatives to measurable business and user outcomes. This is a strategic individual contributor role focused on solving complex analytical problems and improving how the organization measures performance and makes data-informed decisions.

The successful candidate will bring deep expertise in causal inference, experimental and quasi-experimental methods, statistical analysis, and data science. The role will also serve as a domain expert in inference, helping establish high standards for causal evidence across data science, analytics, and cross-functional teams.

The position follows a flexible hybrid work model, with more than half of the working time expected to be spent onsite at the San Francisco office, approximately three days per week.

Key Responsibilities

• Design measurement strategies for complex business and product initiatives using experimental and quasi-experimental methodologies.
• Apply methods such as geographic testing, difference-in-differences, instrumental variables, and synthetic controls to quantify business outcomes.
• Expand understanding of the relationship between user experiences and business performance.
• Develop and evolve organization-wide measurement and metric strategies.
• Connect product development and marketing initiatives to high-quality, measurable outcomes.
• Lead deep root-cause investigations into business and product performance.
• Develop innovative analytical approaches for complex problems without established methodologies.
• Serve as a domain expert in causal inference across the data organization.
• Review measurement designs and provide guidance on the quality and credibility of causal evidence.
• Collaborate with Data Science, Analytics, Product, Marketing, Finance, and senior leadership teams.
• Translate complex quantitative findings into clear narratives that support strategic decision-making.
• Develop causal frameworks and statistical models that enable teams to evaluate business and product performance.
• Help establish measurement practices that guide organizational efforts toward high-impact outcomes.

Required Qualifications

• 5+ years of experience in data science or a related quantitative field.
• Demonstrated experience owning measurement strategies and applying experimental and quasi-experimental methods.
• Deep knowledge of causal inference methodologies and their practical limitations.
• Strong judgment in determining when different causal inference approaches are appropriate and credible.
• Strong SQL proficiency for data analysis and statistical processing.
• Strong Python skills for statistical data processing and analysis.
• Experience writing production-quality Python code for statistical analysis and experimentation tools.
• Ability to conduct complex quantitative investigations and develop novel analytical approaches.
• Strong communication skills with the ability to explain quantitative findings clearly to both technical and non-technical audiences.
• Ability to collaborate effectively with product, finance, analytics, data science, and senior leadership teams.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

1.No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.**