Lead Data Scientist, Strava

• Educational Background: You have an MS degree or equivalent experience in Computer Science, Mathematics, Statistics, or a related quantitative field
• Experience: You bring 5+ 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 and unsupervised 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

• As a Lead 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
• Develop and enhance machine learning models, leveraging supervised, unsupervised, and reinforcement learning techniques to enhance the athlete experience on Strava continuously
• Design and implement experimentation strategies, such as A/B testing and causal inference, to drive data-informed product and business decisions
• Work closely with product managers, engineers, marketing teams, and other partners to develop data-driven solutions that improve athlete engagement and business outcomes
• Apply expertise in large-scale data analysis to identify trends, uncover opportunities, and inform strategic decision-making
• Stay ahead of the latest industry trends in machine learning, LLMs, transformers, and deep learning, evaluating their potential impact on Strava’s data capabilities
• Mentor other data scientists, contribute to knowledge sharing through blog posts or tech talks, and help uplevel the data science 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

• Competitive base salary & stock options
• 100% company paid benefits for employees and families
• Flexible paid time off
• $2,000 annual professional development stipend
• Paid time off for volunteering
• 401(k) plan - with company matching
• $1000 annual gear stipend
• $500 annual gym or coaching reimbursement
• On-site fitness rooms with showers, lockers, and towel service
• Weekly team workouts – including remote employees
• Safe and secure bike storage on-site
• Weekly all hands meetings
• Employee resource groups
• Anti-racism nonprofit company matching program
• Free lunch on Wednesdays on-site
• Regular team happy hours
• Team offsites and company retreats
• Cell phone reimbursement
• Generous industry discounts on gear and activities
• Race entries to events we sponsor
• Snacks and stocked kitchens on-site
• Strava gear

Total funding: $151.4m

Strava has created an online network where runners, swimmers and cyclists can record fitness activities, and compare and compete with their community. It offers both a free and premium subscription service with added features. The company also partners with local councils and urban planners who purchase valuable journey data from Strava which can be used to map out cities and develop safer cycle paths.