Data Scientist 2/3, Storefront
Basis Set Ventures (Basis Set) is an AI-native venture capital fund founded in 2017 that invests in early-stage AI companies, positioning itself as the first capital for founders. The firm backs startups across automated workflows, autonomy, intelligent collaboration, and scalable infrastructure, serving early-stage AI founders and startups as its typical clients.
About Basis Set Ventures
Basis Set Ventures is an AI-native venture fund founded in 2017, built on the science of minds and machines, that recently announced its $250M Fund IV. The firm positions itself as the first institutional capital for founders and provides hands-on, 24/7 'founder concierge' support, acting as its portfolio companies' first customer, sales rep, bug reporter, and recruiter. It invests in AI-first startups spanning categories such as automated workflows, autonomy, intelligent collaboration, and scalable infrastructure, with portfolio companies including Quince, Cusp, Path Robotics, OpenArt, Entire, Tigris Data, Mem0, Vizcom, Simular, and Sakana. Basis Set also publishes original research and perspectives on topics like trucking operations, global customs, maritime transport, real estate, agriculture, and AI agent infrastructure, and runs a Basis Set AI Fellows program for individuals to work on hard problems within the firm and its portfolio companies. Its typical clients are early-stage, AI-native startup founders across a range of industries including consumer, infrastructure, robotics, and enterprise software.
Skills
About the Role
You will own end-to-end modeling work across high-priority surfaces spanning Recommendations and Personalization, Search, and AI-powered Shopping Experiences, with contributions that have direct, measurable impact on conversion, discovery, and user satisfaction at scale. You will report to the VP of Engineering and collaborate closely with engineering managers, software engineers, and product managers to move capabilities from traditional models into near real-time, personalized, and Generative AI-driven experiences.
Requirements
- 5+ years of industry experience as a data scientist, with a strong preference for consumer-facing products or e-commerce applications operating at high scale
- MS or PhD in statistics, mathematics, engineering, computer science, or a highly quantitative field
- Exceptional proficiency in Python and SQL, alongside deep production knowledge of standard data science and machine learning libraries
- Hands-on experience developing and deploying production-grade Recommender Systems, Relevance Ranking, or Personalization pipelines
- Proven experience working with Generative AI or LLM application workflows for tasks like text parsing or context-driven query understanding
- Strong model productionalization skills with a customer-first mindset
- Experience replacing or augmenting traditional NLP systems with LLMs for complex text parsing and language understanding
- Familiarity with Business Intelligence tools (e.g., Looker, Tableau) to build internal dashboards for model tracking and metrics visibility
- Experience building data product specifications alongside distributed engineering teams to streamline model evaluation
Responsibilities
- Develop and refine machine learning models that deliver near real-time personalization across app and site surfaces
- Leverage contextual signals to customize text and details for varied buyer intents
- Assist in building an in-house natural language search engine from 0 to 1
- Utilize Large Language Models like Claude and GPT for query understanding, semantic search, and advanced language retrieval
- Design and implement algorithms for interactive app experiences to drive daily customer engagement
- Create new relevance ranking features
- Build automated end-to-end modeling pipelines in Python and SQL
- Deploy ML models into production
- Run rigorous live experiments
- Identify data gaps and write clear data product specifications
- Partner with Engineering to establish real-time tracking and performance monitoring frameworks
- Act as a technical partner to product and business squads
- Translate business targets into actionable data science solutions
- Present complex analytical outcomes to leadership