• Act as the Subject Matter Expert (SME), drive the end-to-end building and execution of our Risk & Underwriting AI models, directly translating actuarial and healthcare financial risk expertise into high-performance AI models and rigorous evaluation frameworks. • Set the technical roadmap and lead the development of ML models focused on healthcare financial risk stratification and financial underwriting. • Establish best practices for model building, model validation, and rigorous back testing/benchmarking across the data science organization to drive high performance models, while maintaining stability and alignment of models. • Mentor junior data scientists, fostering a culture of rigorous research, high-quality engineering, and innovation. • Partner with clinicians to translate medical knowledge into data-driven hypotheses and with Product/Executive teams to align technical capabilities with market opportunities. • Work with Engineering to design the next generation of our platform’s quality and extensibility, ensuring it remains performant for millions of patients. • Represent Prealize Health’s technical expertise to external strategic partners and stay at the forefront of literature in ML, Actuarial Science, and Biostatistics.

• Pre-processing - Demonstrate the skills and experience to collect, clean, and prepare data sets for input into a computational model using Python • Strong candidates will explain various methods you have applied using common pre-processing functions such as stop word removal, stemming, lemmatization, and tokenization • Feature Engineering and Attribute Evaluation - Candidate must demonstrate experience with NLP feature engineering methods such as TF-IDF, word2vec, GloVe, and FastText identifying the key determinants for modeling that exist in the business process and within existing data sets as well as selecting evaluation protocols (model techniques) • Modeling - Candidates will have practiced skills and experience selecting classification modeling techniques to fit the business problem. Examples will include techniques such as machine learning (ML) supervised and unsupervised learning, regression, neural networks and deep learning, natural language processing, etc. • Validation - Strong candidates will describe their experience with investigating, reporting, and justifying model results • Visualization- Experience in presenting the results of their modeling activities, depicting the insights realized, and explaining the relevance of their results to the organization’s business challenges

• Advance Root’s performance marketing capabilities through improvements in bidding systems, automation, and applied machine learning • Lead and develop a multidisciplinary team responsible for the deployment, optimization, and performance of a nine-figure annual marketing budget • Drive fast, disciplined experimentation across channels and partners • Develop strategic relationships with marketing and technology partners • Align priorities across Marketing, Analytics, Engineering, and Product

• Serve as the primary finance lead for assigned markets/focus areas • Own end‑to‑end financial support for IDN and PCP risk negotiations • Lead complex scenario analysis and large‑scale model adjustments • Translate enterprise models into market‑specific financial narratives • Partner with market presidents, contracting, and network strategy leaders • Set analytic priorities and manage multi‑deal demand • Provide direction, review, and coaching to support analysts and associates • Represent finance in market‑level strategy discussions and escalations