Manager / Senior Manager of Data Science
About the Team
At Typeform, the Data & Insights team drives strategic decision-making and product innovation by combining world-class data infrastructure, advanced analytics, and applied machine learning. Our Data Science & ML Engineering function powers AI-first experiences across our platform—from recommendation systems to form intelligence—while also building scalable infrastructure, experimentation frameworks, and customer insights that fuel our PLG motion.
About the Role We’re looking for a Manager or Senior Manager of Data Science, and we’re flexible on level for the right candidate. If you’re a senior manager with deep leadership experience—or a manager ready for that next big step, we’d love to connect. As the leader of Data Science & Machine Learning Engineering, you’ll lead a team of 4 ICs across data science and ML engineering while remaining hands-on with technical strategy and delivery. You’ll be responsible for defining the team’s roadmap, maturing our AI capabilities, and partnering closely with Product, Engineering, and Analytics to bring intelligent features to life. Your remit will span everything from experimentation design to ML model deployment, all while fostering a high-performance, collaborative team environment.
What You’ll Do
Strategic Leadership & Team Enablement (40-50%)
Lead, mentor, and develop a team of Data Scientists and ML Engineers—fostering a growth mindset, shared ownership, and deep technical curiosity.
Define and drive the roadmap for applied ML and AI initiatives across core product areas, balancing quick wins with foundational investments.
Partner cross-functionally with Product, Engineering, and Analytics to identify and prioritize high-impact opportunities for ML and experimentation.
Collaborate with Data Engineering and Analytics Engineering to ensure scalable data pipelines, model monitoring, and deployment infrastructure.
Champion a culture of reproducibility, documentation, and scientific rigor.
Technical Execution & IC Contribution (40%)
Contribute to modeling strategy, experimentation frameworks, and architecture decisions—reviewing code, shaping methodology, and guiding best practices.
Support the development and deployment of machine learning models that power intelligent product features and internal automations.
Guide experimentation design and causal inference approaches to validate product impact and customer behavior hypotheses.
Partner with stakeholders to translate ambiguous business problems into data science opportunities with clear success criteria.
Org-Wide Impact & Thought Leadership (10-20%)
Help define our long-term AI/ML strategy and tooling roadmap—including model observability, feature stores, and governance practices.
Represent the Data Science & ML Engineering function in strategic planning discussions, technical design reviews, and cross-functional working groups.
Advocate for ethical and responsible AI practices, ensuring fairness, transparency, and explainability in our ML systems.
Support hiring, onboarding, and career development for technical talent within the team.
What You Bring
6+ years of experience in data science, machine learning, or a related field, with at least 1–2 years in a leadership or technical mentorship role.
Proven track record of delivering ML-powered product features or decision-support systems in a production environment.
Strong foundation in statistics, experimentation, and causal inference—plus deep experience with model development and lifecycle management.
Proficiency in Python (and libraries such as pandas, scikit-learn, PyTorch, or TensorFlow) and SQL; familiarity with ML orchestration tools and cloud platforms.
Excellent communication skills—able to translate technical work into business outcomes and influence stakeholders at all levels.
Extra awesome:
Experience managing hybrid teams that include both Data Scientists and ML Engineers
Exposure to modern MLOps tooling (e.g. MLflow, Feature Store, SageMaker, Vertex AI)
Familiarity with unstructured data modeling (e.g. NLP, embeddings, LLMs) and GenAI product patterns
Experience working in product-led or B2B SaaS environments
You bring a coaching mindset and love growing talent as much as shipping great models
About the job
Apply for this position
Manager / Senior Manager of Data Science
About the Team
At Typeform, the Data & Insights team drives strategic decision-making and product innovation by combining world-class data infrastructure, advanced analytics, and applied machine learning. Our Data Science & ML Engineering function powers AI-first experiences across our platform—from recommendation systems to form intelligence—while also building scalable infrastructure, experimentation frameworks, and customer insights that fuel our PLG motion.
About the Role We’re looking for a Manager or Senior Manager of Data Science, and we’re flexible on level for the right candidate. If you’re a senior manager with deep leadership experience—or a manager ready for that next big step, we’d love to connect. As the leader of Data Science & Machine Learning Engineering, you’ll lead a team of 4 ICs across data science and ML engineering while remaining hands-on with technical strategy and delivery. You’ll be responsible for defining the team’s roadmap, maturing our AI capabilities, and partnering closely with Product, Engineering, and Analytics to bring intelligent features to life. Your remit will span everything from experimentation design to ML model deployment, all while fostering a high-performance, collaborative team environment.
What You’ll Do
Strategic Leadership & Team Enablement (40-50%)
Lead, mentor, and develop a team of Data Scientists and ML Engineers—fostering a growth mindset, shared ownership, and deep technical curiosity.
Define and drive the roadmap for applied ML and AI initiatives across core product areas, balancing quick wins with foundational investments.
Partner cross-functionally with Product, Engineering, and Analytics to identify and prioritize high-impact opportunities for ML and experimentation.
Collaborate with Data Engineering and Analytics Engineering to ensure scalable data pipelines, model monitoring, and deployment infrastructure.
Champion a culture of reproducibility, documentation, and scientific rigor.
Technical Execution & IC Contribution (40%)
Contribute to modeling strategy, experimentation frameworks, and architecture decisions—reviewing code, shaping methodology, and guiding best practices.
Support the development and deployment of machine learning models that power intelligent product features and internal automations.
Guide experimentation design and causal inference approaches to validate product impact and customer behavior hypotheses.
Partner with stakeholders to translate ambiguous business problems into data science opportunities with clear success criteria.
Org-Wide Impact & Thought Leadership (10-20%)
Help define our long-term AI/ML strategy and tooling roadmap—including model observability, feature stores, and governance practices.
Represent the Data Science & ML Engineering function in strategic planning discussions, technical design reviews, and cross-functional working groups.
Advocate for ethical and responsible AI practices, ensuring fairness, transparency, and explainability in our ML systems.
Support hiring, onboarding, and career development for technical talent within the team.
What You Bring
6+ years of experience in data science, machine learning, or a related field, with at least 1–2 years in a leadership or technical mentorship role.
Proven track record of delivering ML-powered product features or decision-support systems in a production environment.
Strong foundation in statistics, experimentation, and causal inference—plus deep experience with model development and lifecycle management.
Proficiency in Python (and libraries such as pandas, scikit-learn, PyTorch, or TensorFlow) and SQL; familiarity with ML orchestration tools and cloud platforms.
Excellent communication skills—able to translate technical work into business outcomes and influence stakeholders at all levels.
Extra awesome:
Experience managing hybrid teams that include both Data Scientists and ML Engineers
Exposure to modern MLOps tooling (e.g. MLflow, Feature Store, SageMaker, Vertex AI)
Familiarity with unstructured data modeling (e.g. NLP, embeddings, LLMs) and GenAI product patterns
Experience working in product-led or B2B SaaS environments
You bring a coaching mindset and love growing talent as much as shipping great models