Job Title: Data Scientist
Company: Lily AI
Location: SF Bay Area
This is a full-time, 100% on-site role requiring presence 5 days a week at our Mountain View, CA office. Please note we are currently not in a position to offer any relocation assistance for this role; candidates must be local to the SF Bay Area or willing to relocate entirely at their own expense.
Department: Data Science & Analytics
About Lily AI
At Lily AI, we are bridging the gap between how retailers describe their products ("merchant-speak") and how real people actually search for them ("customer-speak"). We are an AI-powered e-commerce product discovery platform trusted by top-tier global brands in fashion, home, and beauty. By automatically extracting rich product details and enriching metadata, we optimize search, discovery, and conversion across on-site search, SEO, Generative Engine Optimization (GEO), and paid ads.
Backed by an unmatched blend of retailer, public, and proprietary data—featuring over 1 billion data points and 1 million manually labeled training products—we are scaling rapidly and looking for an exceptional Data Scientist to help us build the next generation of e-commerce AI.
The Role
As a Data Scientist at Lily AI, you will play a critical role in building and refining the core intelligence of our platform. You will build, fine-tune, and deploy advanced NLP systems—blending Large Language Models (LLMs) with high-efficiency traditional ML. You'll work on everything from fine-tuning foundational models for proprietary text generation to building lightning-fast classifiers and semantic search algorithms that can process billions of data points with minimal latency, directly impacting the bottom line for the world's biggest brands.
Key Responsibilities
Advanced Predictive Modeling: Design, develop, and evaluate rigorous statistical and machine learning models to solve complex, unstructured business problems. Move beyond out-of-the-box algorithms by designing custom loss functions, conducting rigorous A/B testing, and validating model assumptions to ensure robust mathematical validity.
Modern NLP & LLM Development: Fine-tune foundational open-source models (e.g., Llama 3, Mistral) and build robust RAG (Retrieval-Augmented Generation) pipelines to extract granular product attributes and generate conversion-driving copy.
Scalable Machine Learning: Design systems that balance state-of-the-art accuracy with real-time e-commerce constraints. You will know exactly when to deploy a heavy LLM and when a lightweight classifier, custom embedding model, or specialized vector search is the right tool for latency and cost.
Semantic Search & Information Retrieval: Implement and optimize vector-based search and retrieval systems to map "customer-speak" to "merchant-speak" with lightning-fast execution.
Production Optimization & Evaluation: Monitor model performance in production, implement robust evaluation frameworks for generative outputs (mitigating hallucinations in retail taxonomy), and optimize inference for high-traffic environments.
Cross-Functional AI Strategy: Partner closely with data engineering, ML ops, and product teams to transition cutting-edge generative prototypes into highly scalable, revenue-driving production endpoints.
What You Bring to the Table
Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Mathematics, Statistics, or a related quantitative field.
Experience: 2–5 years of hands-on experience building, deploying, and maintaining machine learning models in a production environment.
Statistical & Modeling Foundations: Deep understanding of exploratory data analysis, hypothesis testing, regression frameworks, and experimental design. Proven ability to translate abstract product questions into formal mathematical formulations and predictive frameworks.
Modern AI Technical Stack:
Strong proficiency in Python and SQL.
Deep experience with modern deep learning and NLP ecosystems (PyTorch, Hugging Face, transformers).
Familiarity with LLM orchestration and deployment (e.g., vLLM, LangChain, fine-tuning techniques like LoRA/QLoRA).
Experience with Vector Databases (e.g., Pinecone, Milvus, Weaviate) and embedding models.
Architectural Pragmatism: A highly analytical mindset with a proven track record of deciding when to use Generative AI versus traditional ML based on cost, latency, and determinism.
Bonus Points: Experience in e-commerce, search relevance algorithms, recommendation systems, or high-scale retail data environments.
Why Join Us?
Scale & Impact: Work with a proprietary dataset of over 1 billion data points that you can't find anywhere else.
Growth: We are a fast-growing, recognized leader in retail AI (recently named to the Deloitte Technology Fast 500).
Culture: Join a collaborative, innovative team that values experimentation, diversity of thought, and building products that solve real-world problems.
Benefits: Competitive salary, equity, comprehensive healthcare, and flexible PTO.
Salary:
$160,000 per year
How to Apply:
Ready to build the future of retail AI? Apply directly here: https://forms.gle/Lc1UC1dL6vLGyaRn7
Lily AI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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