Data Scientist

We're hiring a Data Scientist for the Rider Growth charter of LoadShare Product Org. This role goes beyond preparing data, conducting analysis and building dashboards. Your job starts where that ends: figuring out why a metric moved, and turning that into experiments and models that move it.

Your will look at the entire lifecycle of riders - acquisition, activation, retention, revenue and referral. You will own the why behind every growth metric and convert it into experiments and models that move the needle.

Roles & Responsibilities

What You'll Work On

• Causal analysis & RCA: Establish causation, not correlation, for why acquisition/activation/retention metrics move. Use controlled experiments, diff-in-diff, and quasi-experimental methods to move the team from "what happened" to "why, and what to do next."
• Dynamic offers & incentives (ML): Build models for personalized/dynamic offers to address new rider hiring, engagement and reactivation at the right place and right time, balancing uplift in DAU and CAC.
• Experimentation muscle: Design, run, and read rigorous A/B tests; own lift measurement; build the experimentation discipline so launches stop being "assume and ship."
• Segmentation & cohorting: Rider and funnel segmentation, retention/cohort curves, and driver/decision-tree analysis to find the patterns that explain behaviour.
• Forecasting & churn (retention side): Predict supply needs, churn risk, and recovery to inform retention and re-engagement strategy.

What We're Looking For

• 3-5 years in Data Science/ML
• Strong first principles the core bar.
• Python and practical ML: segmentation, decision trees, regression, and basic causal-inference methods.
• Real grounding in experimentation and statistics — A/B testing, controlled experiments, reading lift honestly.
• AI-native: uses Claude/LLM tooling to accelerate analysis
• Strong communication, comfort being embedded with a PM and business teams, and a bias for action.

Bonus Points If You Have

• Growth, activation, or retention analytics or ML modelling experience.
• Experience with dynamic pricing/incentives, geospatial data, or experimentation platforms.