Senior Data Scientist, Dynamic Promo - Quick Commerce
Core
Building scalable causal inference and time-series models to optimize dynamic promotions, affordability interventions, and user targeting in quick commerce.
Role type
Senior IC data scientist (causal inference & time series)
Builds
Production-ready uplift models, forecasting systems, and adaptive incentive deployment architectures for non-food quick commerce.
Domain
Quick commerce / consumer retail / econometrics
Deliverable
production ML models
Required skills
causal inference, uplift modeling, time series analysis, A/B testing design, Python, large-scale experimentation, statistical foundations, production ML deployment
Preferred skills
multi-market personalization scaling, cloud platforms (GCP/AWS), open-source contributions, team mentoring
Technologies
Python, GCP, AWS
Responsibilities
Design and deploy classical ML and uplift models to improve affordability perception; lead end-to-end data science initiatives from design to stakeholder communication; formulate hypotheses and analyze A/B tests for targeting; apply time series and causal techniques to forecast supply and demand of incentives; leverage live user signals for adaptive interventions; collaborate with MLEs and engineers for scalable production solutions; mentor peers in causal inference and personalization.
Seniority
Senior, hands-on IC with mentorship responsibilities
