• Lead project teams of world-class data scientists developing and applying robust, generalizable solutions to core scientific problems. • Work with a diverse set of stakeholders to develop project plans that support program needs, develop alignment between functional teams and stakeholders, and prioritize. • Work with an array of data types alongside genetic sequencing data, including electronic medical records, multi-omics data, and other data modalities using R or Python in cloud environments. • Be comfortable with scientific uncertainty and embrace curiosity and creative solutions. • Collaborate with drug discovery and clinical development teams to help ensure the relevance and impact of the insights generated by you and your teammates. • Be a dynamic and active team member, championing and adopting shared coding standards, participating in code review, and providing regular updates of your work and input into the work of your colleagues

• Design, build, and productionize machine learning models focused on: • Knowledge extraction from unstructured data (e.g., NER, entity linking) • Graph-based learning and inference • Entity resolution and relationship discovery • Evaluate and leverage existing ML models and frameworks to solve real-world problems efficiently • Partner closely with backend and graph engineers to integrate ML models into production services and APIs • Contribute to the design and evolution of knowledge graphs and ontologies • Perform exploratory data analysis (EDA) to inform modeling decisions and system design • Own ML components end-to-end, including experimentation, evaluation, deployment, and iteration • Help shape best practices for applied ML within the Knowledge Layer team

• Lead end-to-end Data Science projects using agile methodologies with a strong focus on delivering business value. • Perform exploratory data analysis (EDA), identify patterns and trends, and generate actionable insights to support decision-making. • Develop, evaluate, and implement statistical, predictive, and prescriptive models (both supervised and unsupervised). • Act in a consultative manner to understand client pain points and needs throughout the project. • Present results, analyses, and recommendations to technical and non-technical stakeholders with clarity and storytelling. • Actively collaborate with the team, sharing knowledge, best practices, and new technical approaches. • Support internal requests and strategic initiatives as needed.

• Enable our product teams to think strategically with key stakeholders about how customers engage with Webflow, identifying and executing on high impact growth opportunities with actionable insights • Define product and business success metrics, building tools for monitoring and surfacing drivers of KPIs (Key Performance Indicators) • Lead technical and non-technical team members with processes and technical expertise for executing experiments, answering open ended business questions with data, and researching levers for improving our product. • Develop our data foundation, experimentation and machine learning capabilities, collaborating on our strategic roadmap for building new pipelines and tools that contribute to our connected, complete understanding of our customer journey and improve our customer experience. We currently use Snowflake, DBT, Tableau, and Fivetran and are working with our Data Engineering collaborators on scaling our platform capabilities with a number of other tools!