Principal Data Scientist - Consumer

Role overview A leading quick-delivery convenience platform is hiring a Principal Data Scientist to own consumer personalization end to end. The role sets technical direction for the recommendation, ranking, and personalization models that power search, browse, cart, and CRM surfaces, while leading the build-out of agentic AI experiences that help customers plan and reorder. This is a senior individual-contributor role with significant cross-functional influence across Product, Engineering, and Marketing.

Responsibilities - Define modeling strategy for recommendations, ranking, and personalization across the home feed, search, product pages, cart, and CRM channels - Design candidate generation, retrieval, and learning-to-rank systems that balance relevance, basket size, margin, and live inventory availability - Lead agentic AI efforts, building LLM-powered agents with tool use, retrieval, evaluation, and guardrails for planning, discovery, and reordering flows - Blend classical ML and LLMs, choosing between gradient-boosted models, two-tower networks, or large language models depending on the problem - Design A/B tests and offline evaluation frameworks, tying model improvements to customer and business outcomes - Partner with engineers and product managers on feature pipelines, model serving, latency budgets, and monitoring for drift and quality - Mentor senior and staff data scientists, lead design reviews, and shape the roadmap with Product and Engineering leaders

Requirements - Significant experience as a senior or staff data scientist working on personalization, recommendations, or ranking systems - Track record of building and shipping ML models that move customer-facing metrics at scale - Experience leading technical direction across teams without direct authority and mentoring senior data scientists - Clear communication skills with both technical and non-technical partners, including executives - Ability to design rigorous experimentation and connect modeling choices to business impact

Nice to have - Hands-on experience with Databricks or similar platforms (Spark, MLflow, feature stores) for large-scale training and model management - Background in e-commerce, grocery, quick-commerce, or other marketplaces where inventory and location shape what customers can buy - Experience with real-time or session-based recommendations, contextual bandits, or reinforcement learning - Familiarity with agent frameworks, LLM evaluation tooling, and fine-tuning or distilling models for cost and latency - Experience with dbt, Airflow, or similar data pipeline tools - Publications, patents, or open-source work in recommender systems, information retrieval, or applied LLMs

Benefits and work setup - Medical, dental, and vision insurance - 401(k) retirement savings plan with HSA or FSA eligibility - Long and short-term disability insurance plus group life insurance - Fitness reimbursement program and an employee discount - Flexible paid time off and an employee assistance program - Remote base salary range of $180,000 to $240,000, with eligibility for a discretionary annual cash bonus and equity participation