Senior DS/ML Scientist - Search Science

Summit Health

8d•$115,000 - $170,000•Hybrid

About The Position

As a Senior Data Scientist on the Search Science team at Shipt, you will be responsible for the algorithms that determine what customers see every day. You will be working to re-architect Shipt's search stack to be LLM-native and value-aware. In this role, you will build a state-of-the-art retrieval and ranking system from the ground up. You will partner with Senior Scientists and ML Engineers to push the boundaries of what a search engine can do. Search is the front door of Shipt—improvements you make here are immediately visible to leadership and impactful to the bottom line.

Requirements

Bachelor’s degree in a STEM field (Computer Science, Statistics, etc.) with a focus on machine learning
3–5 years of experience applying Machine Learning to real-world problems
Strong coding fluency in Python and SQL
Deep familiarity with Deep Learning frameworks (PyTorch or TensorFlow) and their application to information retrieval, ranking and/or NLP
A solid grasp of Learning-to-Rank (LTR) concepts including rich feature engineering
Previous experience with Search or Recommender Systems in e-commerce settings
Experience with Multi-Objective Optimization: Understanding of the challenges of optimizing for multiple conflicting metrics (e.g., Relevance vs. Revenue)
Bachelor's Degree or equivalent experience

Nice To Haves

Data Science
Machine Learning (ML)
Search Engines

Responsibilities

Help design and implement multi-task learning (MTL) and multi-objective models that don't just optimize for clicks, but solve for complex trade-offs between organic relevance, ad revenue, and operational efficiency.
Identify opportunities, explore datasets, and report out findings to senior level leaders in the company which will influence our Search roadmaps.
Move beyond generic relevance by integrating rich user signals through things like purchase history, dietary constraints, and real-time intent. Intentionally optimize our search system to predict not just what is relevant, but what is relevant for each customer.
Move beyond keyword matching by integrating Large Language Models into the core search loop. Work on projects such as generative retrieval, query expansion, and semantic understanding to handle complex, intent-driven user queries.
Develop and tune sparse (lexical) and dense (vector) retrieval layers to maximize recall across our massive catalog, ensuring we surface the right products even for long-tail queries. Explore generative retrieval methodologies and their utilization.