Typeform
Data Scientist
Job Summary
The Data Scientist at Typeform is responsible for analyzing data, building models, and prototyping AI features to enhance the company's products. The role requires collaboration with teams across Product, R&D, and GTM to scope problems and develop ML-powered solutions. Candidates should have extensive experience in data science, machine learning, and programming in Python with knowledge of ML libraries and large datasets. The position emphasizes innovation, experimentation, and delivering impactful, production-ready AI features.
Required Skills
Job Description
Who we are
Typeform is a refreshingly different form builder. We help over 150,000 businesses collect the data they need with forms, surveys, and quizzes that people enjoy. Designed to look striking and feel effortless to fill out, Typeform drives 500 million responses every year—and integrates with essential tools like Slack, Zapier, and Hubspot.
About the Team:
At Typeform, the Data & Insights team’s charter is to make data actionable through multiple mediums—reports, dashboards, AI/ML-models—that help us fuel growth and drive efficiencies within our business. Within that charter, our Data Science practice focuses on building cutting-edge machine learning capabilities that help revolutionize how we leverage data and empower our customers to collect information in a conversational and personalized way.
About the Role:
As a Data Scientist at Typeform, your mission will be to analyze data, build models, and prototype innovative AI features. You will partner closely with Product, R&D, and GTM teams to scope problems, explore data, and deliver ML-powered solutions that are both technically sound and business-relevant. This is a hands-on role with a strong mandate for innovation, experimentation, and delivering production-ready solutions that drive real impact.
Things you’ll do:
AI Feature Development & Evaluation:
- 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.
Innovation & Research:
- 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.
Ideation & Scoping:
- 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.
Data Exploration & Analysis:
- 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.
What you already bring to the table:
- 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.
Extra awesome:
- 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).
*Typeform drives hundreds of millions of interactions each year, enabling conversational, human-centered experiences across the globe. We move as one team, empowering our collective efforts by valuing each individual’s unique perspective. This fosters strong bonds grounded in respect, transparency, and trust. We champion our diverse customer base by anticipating their needs and addressing their challenges with priority. Committed to excellence, we hold high expectations for ourselves and each other, continuously striving to deliver exceptional results.
We are proud to be an equal-opportunity employer. We celebrate diversity and stand firmly against discrimination and harassment of any kind—whether based on race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or expression, or veteran status. Everyone is welcome here.
Typeform
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