• Collaborate in the development and maintenance of documentation of data definitions and abstraction procedures • Efficiently identify and abstract pathology, treatment and other clinical data from medical records that is thorough and accurate • Maintain REDCap database for disease area(s) • Think critically to identify potential problems affecting the abstraction process for improvement and quality control • Perform quality review on work completed by other data abstractors • Review data quality dashboards and make corrections • Address data quality questions from a multidisciplinary team of data scientists, bioinformaticians, and clinical scientists • Manage process documentation • Translation of healthcare and clinical concepts for data engineering work • Coordination with clinicians for clarification on abstraction procedures and clinical concepts • Support the multidisciplinary team in automated abstraction activities

• Develop, analyze, evaluate, and deliver actionable analytics that inform key supply chain decisions, optimize performance, and support the successful execution of strategic initiatives • Lead the development, optimization, and continuous improvement of databases, data pipelines, and analytics infrastructure to ensure data integrity, accessibility, and performance • Own the design of Power BI dashboard to support the business needs and the ability to build tabular and/or visualization reports as needed • Develop, implement, and maintain databases, data collection systems, data analytics and other strategies that optimize statistical efficiency and quality with data from primary or secondary data sources. • Lead the identification, analysis, and interpretation of trends or patterns in complex data sets using statistical techniques • Perform extensive data validation/quality assurance analysis within large datasets • Share compelling, insightful storytelling so the business results/needs are understood

• Define and build predictive models from scratch, starting with: • Churn prediction — identify clients at risk of reducing activity or withdrawing assets, early enough for the sales team to act • Upsell / cross-sell propensity — score clients by their likelihood to increase assets under custody, and surface the right opportunities • Client Lifetime Value (CLV) — estimate forward-looking client value to guide resource allocation and relationship management prioritization • Own the full modeling lifecycle: • Work with raw trading, transactional, and behavioral data from our data warehouse • Define target variables and operationalize business concepts (e.g., what constitutes "churn" in a brokerage context) into measurable ML targets • Engineer features from client activity, trading patterns, market conditions, and engagement signals • Select, train, validate, and iterate on models — starting simple, increasing complexity where it earns its keep • Design monitoring for model performance, data drift, and degradation over time • Deliver daily client-level scores that integrate into CRM workflows and sales processes • Bridge data science and business: • Translate model outputs into actionable insights for non-technical sales managers • Work with sales leadership to design interventions around model predictions • Present results, assumptions, limitations, and recommendations to senior stakeholders.

• Leading experimentation and analytical strategy across product, growth, monetization, marketing, and operational initiatives, often simultaneously • Applying causal inference methodologies (difference-in-differences, synthetic controls, propensity methods, Bayesian experimentation) to real business questions where traditional A/B testing may not be feasible • Partnering directly with directors and senior leaders to frame complex, cross-functional challenges and translate findings into clear recommendations that drive action • Improving experimentation frameworks, metrics strategy, and analytical rigor across teams — developing scalable approaches for measurement, segmentation, forecasting, and incrementality • Influencing roadmap prioritization and business strategy through rigorous analysis and precise communication of tradeoffs and uncertainty • Mentoring and upskilling data scientists and analysts across the organization on experimentation, causal inference, and statistical best practices • Partnering with Engineering, Product, and ML teams on instrumentation, data infrastructure, and analytics tooling • Integrating generative AI tools into daily workflows to automate tasks, foster innovation, and maximize analytical productivity