• Exposing Insights: As a Data Scientist, your goal is not to just display data, but turn it into information. You will produce analysis reports and diagnostic models to try and discover hidden relationships and patterns between our data and metrics of interest. • Evaluate & Produce Quality: Good code is reviewed code. You will be involved in ensuring your and your teammates’ code is free from errors, bias, and is easy to understand. • Data Engineering: We are a newer team at a growing company, and you’ll need to do a lot of your own data engineering. Gather, clean, and preprocess data from various sources, ensuring accuracy and consistency. Perform feature engineering to generate new variables or transform existing ones to improve the quality and usefulness of the dataset. Tie all these tasks together in a pipeline and deploy on cloud based infrastructure. • Predictive Modeling: Develop, validate, and deploy predictive models using machine learning algorithms and statistical techniques, such as regression, classification, clustering, time series forecasting, and optimization. • Generative Modeling: Use a combination of open source and paid technologies to produce abstractions & novel features for other applications. • Continuous Improvement: Data Science is a quickly moving field and you’ll need to keep up to date. You will need to keep abreast of the latest developments in data science, machine learning, and reporting technologies, while incorporating them into your work when appropriate. Participate in knowledge-sharing sessions to contribute to the growth and development of others.

• Own the full analytics lifecycle: requirements discovery, data modeling, transformations, BI delivery, validation, and ongoing optimization • Translate business and clinical questions into scalable, reliable data and analytics solutions • Actively contribute at a technical level: advanced SQL, data modeling, dbt transformations, and analytics workflows • Design, build, and optimize ETL/ELT pipelines using AWS Redshift, dbt, and related tools • Ensure data quality, performance, scalability, and reliability across the analytics stack • Oversee and build customer-facing dashboards and reports that turn complex healthcare data into clear, actionable insights • Act as the primary analytics partner for clients, leading discussions, demos, and presentations • Communicate insights and recommendations to both technical and non-technical stakeholders, including executive audiences • Lead, mentor, and support BI analysts and analytics engineers • Establish best practices for SQL, data modeling, dashboard design, documentation, and code reviews • Champion data governance, security, and quality standards • Identify opportunities to improve metrics, automation, tooling, and analytics processes based on client and business needs

• Lead complex data analysis projects from conception to delivery • Design and develop advanced SQL queries, data models, and automated reporting solutions • Create sophisticated data visualizations and interactive dashboards • Architect and implement Python-based data analysis workflows

• Set the company-wide strategy for product analytics, business metrics, and GTM insights • Define the core measurement frameworks for product usage, activation, retention, expansion, and customer value • Ensure analytics meaningfully influence product roadmap decisions, GTM strategy, and operational planning • Oversee the delivery of trusted dashboards, reporting, and self-serve analytics used by executives and functional leaders • Establish standards for metric definitions, data quality, and analytical rigor across the organization • Lead the data engineering strategy in close partnership with data engineers • Set expectations and priorities for how data engineering supports analytics, product insights, and ML use cases • Ensure the data foundation (ingestion, modeling, storage, governance) scales with the business and product needs • Act as the primary stakeholder for analytics requirements, guiding architectural decisions without owning day-to-day pipeline development • Own the vision and prioritization for customer-facing analytics and insight products (benchmarks, insight cards, modeled datasets, ML/AI features) • Partner with Product and Engineering to ensure these capabilities drive differentiation and customer value • Balance experimentation and innovation with delivery of durable, scalable data products • Lead and scale the analytics and ML organization; define roles, career paths, and team structure • Hire and develop senior talent who can operate autonomously and influence cross-functionally • Create clear operating rhythms, planning processes, and success metrics for the data organization • Foster a culture of accountability, curiosity, and outcome-driven execution • Own the data roadmap and prioritize initiatives based on business impact and strategic goals • Partner closely with Product, Engineering, GTM, Finance, and CS leadership to align on data priorities • Define and track data and analytics KPIs; ensure leadership has visibility into performance and outcomes • Guide vendor strategy and build-vs-buy decisions across analytics and ML tooling