Senior Data Scientist

About This Role

Strava is the app for active people. With over 150 million athletes in more than 185 countries, Strava is where connection, motivation, and personal bests thrive. No matter your activity, gear, or goals, we help you find your crew, crush your milestones, and keep moving forward. Start your journey with Strava today.

Our mission is simple: to motivate people to live their best active lives. We believe in the power of movement to connect and drive people forward.

As a Senior Data Scientist, you will play a key role in shaping the future of data-driven decision-making at Strava. You will develop and deploy cutting-edge machine learning models, design rigorous experimentation frameworks, and uncover insights that drive impactful business and product decisions. Working alongside cross-functional teams, you will help harness Strava’s vast dataset to enhance athlete experiences and drive engagement.

We follow a flexible hybrid model that generally translates to around half your time on-site in our San Francisco office—roughly three days per week.

What You’ll Do:

• Develop and enhance machine learning models using supervised, unsupervised, and reinforcement learning techniques to continuously elevate the athlete experience on Strava.
• Design and implement robust experimentation strategies—including A/B testing, causal inference, and advanced techniques such as quasi-experimental designs—to drive data-informed product and business decisions.
• Build and operationalize models for key business and product challenges, including propensity modeling, lifetime value (LTV) prediction, forecasting, and user segmentation.
• Collaborate cross-functionally with product managers, engineers, marketers, and other stakeholders to develop and scale data-driven solutions that improve athlete engagement and business outcomes.
• Apply your expertise in large-scale data analysis to identify behavioral trends, surface growth opportunities, and inform strategic direction.
• Stay current with advances in machine learning and AI—including transformers, LLMs, and deep learning—to explore their application in improving Strava’s data and product capabilities.

You Will Be Successful Here By:

• Demonstrating technical excellence in machine learning, data analysis, and experimentation methodologies.
• Driving collaboration across teams to ensure data science solutions align with business goals.
• Communicating complex data concepts effectively to both technical and non-technical stakeholders.
• Continuously seeking opportunities to optimize and innovate Strava’s data science practices.
• Championing a culture of learning, mentorship, and knowledge sharing within the data science team.

What You’ll Bring to the Team:

• Educational Background: You have an MS degree or equivalent experience in Computer Science, Mathematics, Statistics, or a related quantitative field.
• Experience: You bring 3+ years of experience in data science roles, with a consistent track record of driving impactful solutions.
• Machine Learning Expertise: You have hands-on experience with various ML techniques, including supervised, unsupervised learning, reinforcement learning and are comfortable working with deep learning models such as transformers or LLMs.
• Experimentation & Statistical Analysis: You have a solid grasp of A/B testing, causal inference, and statistical methodologies to support meticulous data-driven decision-making.
• Cloud & Deployment: You have experience deploying ML models in production using cloud platforms like AWS (or equivalent).
• Analytical & Problem-Solving Skills: You excel at taking on complex problems, uncovering insights, and driving data-driven strategies.
• Communication & Collaboration: You are skilled at explaining complex data concepts to diverse audiences and thrive in a cross-functional environment

Compensation Overview:

At Strava, we know our employees are the most important ingredient to our success, and our compensation and total rewards programs reflect that. We take a market-based approach to pay, and pay may vary depending on the department and your location. Salary ranges are categorized into one of three tiers based on a cost of labor index for that geographic area. We will determine the candidate’s starting pay based on job-related skills, experience, qualifications, work location, and market conditions. We may modify these ranges in the future.