• Collaborate with clinicians, researchers, and cross-functional teams to design and implement data science solutions that advance clinical research goals. • Conduct real-world evidence studies leveraging EHR data and unstructured clinical text mining. • Analyze large and complex clinical datasets using advanced statistical methods. • Develop predictive models and apply machine learning (ML) techniques to identify patterns, biomarkers, or risk factors in patient data. • Create visualizations and dashboards that effectively communicate complex findings to both technical and non-technical stakeholders. • Conduct data cleaning, preprocessing, and transformation to prepare datasets for analysis. • Apply Natural Language Processing (NLP) to extract insights from unstructured data such as clinical notes and patient narratives. • Work with large-scale healthcare data environments using big data frameworks and cloud computing platforms. • Ensure data integrity, security, and compliance with HIPAA and other relevant regulations. • Stay current with emerging technologies and methodologies in the data science and healthcare domains.

• Collaborate with stakeholders to identify business challenges that can be solved through data analysis. • Gather data from various sources (SQL databases, APIs, web scraping), then clean and "wrangle" it to ensure accuracy for modeling. • Design and implement algorithms and predictive models using machine learning techniques to forecast outcomes or categorize information. • Analyze datasets to uncover hidden patterns, trends, and anomalies. • Translate technical findings into "data stories" using tools like Tableau or Power BI to influence executive decisions.

• Operate as a bridge between the Data team and Risk team - not just to maintain what exists, but to explore what's possible. • Deeply understand Risk's problems, priorities, and workflows rather than delivering models over the fence • Build analytical and machine learning models that drive business decisions across our insurance/actuarial functions - including next-generation approaches that challenge industry conventions • Partner with our risk and insurance team to support loss modeling, pricing analyses, and portfolio performance metrics, while identifying novel data sources that haven't been leveraged in this industry • Design and run experiments (e.g. A/B tests, causal inference analyses) to measure product and pricing impacts • Translate complex analytical findings into clear recommendations for non-technical audiences • Dig into data, identify patterns, and move quickly from question to insight to action

• Design, build, and maintain scalable data pipelines to ensure reliable, timely access to core product and business data. • Define and develop core metrics to measure product performance, feature adoption, user engagement, and strategic initiatives. • Build and maintain interactive dashboards in Tableau (or similar BI tools) to deliver clear, actionable insights to product and business stakeholders. • Design and analyze experiments (A/B tests) to evaluate new features and product changes. • Translate complex data into clear, actionable insights using statistical analysis and visual storytelling. • Collaborate cross-functionally with product managers, engineers, designers, and executives to drive impact through data. • Proactively identify trends, anomalies, and opportunities hidden in user behavior and market dynamics.