Data Scientist

Summary

Join Typeform's Data & Insights team as a Data Scientist to analyze data, build models, and prototype innovative AI features. Partner with various teams to scope problems and deliver impactful ML-powered solutions. Develop and evaluate AI features, leveraging LLMs and generative AI. Stay current on emerging ML methods and contribute to internal knowledge sharing. Collaborate with stakeholders to define data science projects and conduct exploratory data analysis. Work with other teams to source and validate data for model development. This hands-on role demands innovation, experimentation, and delivering production-ready solutions.

Requirements

• 3–5+ years of experience in data science, ML, or applied statistics in a product or growth-oriented B2B SaaS environment
• Experience collaborating closely with Product, Engineering, or Marketing teams to ship user-facing AI features or data products
• Ability to clearly communicate technical concepts to stakeholders across varying levels of technical fluency
• Strong programming skills in Python and experience with ML libraries like scikit-learn, XGBoost, PyTorch, or TensorFlow
• Demonstrated ability to design, train, and validate models using structured and unstructured data
• Strong analytical skills and experience working with large datasets using SQL, Pandas, and other data tools

Responsibilities

• Partner with Engineering to design and implement AI-powered product features, applying best practices around model selection, system design, optimization, and monitoring
• Evaluate LLM and generative AI features using both quantitative metrics and qualitative techniques—developing benchmarks, stress tests, and custom evaluation pipelines to ensure reliability and performance
• Stay on top of emerging methods and tools in ML, deep learning, NLP, and agentic workflows—bringing new ideas to the table and pushing the envelope of what’s possible
• Contribute to internal knowledge sharing and cross-functional learning through documentation, demos, and collaborative experimentation
• Collaborate with stakeholders across R&D (Product, Engineering, Research, Design) and GTM to frame analytical problems, align on objectives, and define success metrics
• Translate ambiguous business challenges into clearly scoped, feasible data science projects
• Conduct exploratory data analysis (EDA) to surface trends, patterns, outliers, and opportunities for impact
• Work with Analytics Engineering and Data Engineering teams to source and validate high-quality data for model development

Preferred Qualifications

• Experience with LLMs, embeddings, and vector search tools (e.g. LangChain, OpenAI APIs, Pinecone, FAISS)
• Familiarity with agentic AI workflows, prompt engineering, or RAG (Retrieval-Augmented Generation) pipelines
• Experience building evaluation pipelines for ML and AI models
• Previous experience working in cross-functional environments and driving ML/AI projects from ideation to deployment
• Comfort using tools like Jupyter, Looker, GitHub, or cloud platforms (AWS, GCP)