Director, Data Science

Own the end-to-end data science roadmap across Consumer, Delivery, and Ads — translating business priorities into a coherent ML strategy with clear milestones and measurable ROI.

Provide strong technical direction and mentorship to a team of Data Scientists and ML Engineers; establish best practices for model development, evaluation, and deployment.

Partner with C-suite and senior product leadership to influence product strategy and build organizational confidence in ML-driven decision-making.

Drive a culture of experimentation: define measurement frameworks, champion A/B testing rigor, and hold the team accountable to business impact.

Lead the design and continuous improvement of Gopuff's search ranking and query understanding systems, including semantic search and intent modeling.

Build and scale personalization infrastructure that adapts the customer experience in real time — from homepage carousels to dynamic upsell and cross-sell surfaces.

Develop next-generation recommendation models (collaborative filtering, two-tower retrieval, contextual bandits) that drive basket size and repeat purchase.

Partner with Product to define upsell and nudge strategies grounded in behavioral signals and causal inference.

Work day to day with gopuff engineering teams to bring search and recommendation changes to life

Own the predictive models powering ETA accuracy, dynamic dispatch, and driver routing that underpin Gopuff's speed promise.

Apply ML to optimize zone coverage, demand forecasting, and fleet utilization — directly impacting contribution margin.

Partner with Operations to turn model outputs into actionable tooling for fulfillment center and driver teams.

Architect and own Gopuff's ad ranking stack — query-ad relevance scoring, multi-objective ranking (revenue × customer experience), and auction mechanics.

Build CTR/CVR prediction models and closed-loop attribution pipelines for sponsored product, display, and offsite formats.

Define and improve advertiser-facing ML products: bid optimization, budget pacing, audience targeting, and incrementality measurement.

Collaborate with the Ads Product and Sales teams to grow advertiser ROI while protecting the organic shopping experience.

8+ years in applied data science or ML, with at least 3 years managing teams of scientists and engineers in a fast-paced tech or e-commerce environment.

Ad ranking or retrieval systems in e-commerce, marketplace, or search contexts — including relevance modeling and multi-objective optimization. Proven experience building and shipping

two-tower retrieval, transformers, LLMs, contextual bandits, GNNs, and causal/uplift modeling. Deep expertise in modern ML architectures:

Strong product intuition and business acumen — you can connect model improvements to revenue, NPS, and operational metrics and communicate this clearly to executives.

proficient in Python and comfortable diving into model code, experiment pipelines, and production systems. Hands-on coder:

familiar with feature stores, model registries, real-time serving, and experimentation platforms. Experience with large-scale ML infrastructure:

Track record of building and retaining diverse, high-performing data science teams.

Prior leadership at a consumer marketplace, quick-commerce, grocery, or retail media company.

Familiarity with retail media network (RMN) measurement standards and privacy-preserving attribution techniques.

Experience with real-time personalization at scale, including streaming feature pipelines. Familiarity with Databricks and Snowflake is a plus.

Publications or presentations at NeurIPS, KDD, RecSys, SIGIR, or equivalent.