• Act in a consultative capacity alongside business teams, understanding pain points and proposing data-driven solutions; • Lead end-to-end data science projects (from problem definition to production delivery); • Translate business problems into analytical approaches (predictive models, segmentations, optimizations, etc.); • Communicate results and insights clearly to technical and non-technical stakeholders; • Ensure the quality, reproducibility, and governance of developed models; • Communicate technical points, decisions, and trade-offs to peers and stakeholders;

• Work alongside our data migration and engineering teams on live client projects, contributing to the day-to-day delivery of healthcare data work. • Identify inefficiencies, repetitive tasks, and fragile processes - and build the scripts, tools, and small systems that fix them. • Write and maintain code, primarily in Python & SQL, to automate workflows, process data, and support internal operations. • Apply and experiment with AI tooling - LLMs, embeddings, classifiers - to problems across the business where they add real value, and be honest about where they don’t. • Communicate your work clearly, in writing and in person, to colleagues who are not data scientists - so that your improvements get adopted rather than sidelined. • Over time, contribute to Stalis’s emerging data science and machine learning capability, including schema mapping, clinical data analytics, and data products.

• Represent the customer in product evolution with a strong understanding of client priorities, challenges and opportunities • Work closely with health plan customer counterparts in various subject matter areas like clinical, product, data, intervention, and finance • Identify opportunities to improve existing algorithms and build new ones that better surface patient risk • Create a framework for algorithm development, implementation, and measurement to guide end-to-end evolution • Design, build, and execute in close collaboration with an internal team comprised of product managers, data scientists, ML engineers, clinicians, dev ops engineers, and software engineers • Remain hands on and closely aligned with internal working team across the full lifecycle of model development: from defining the problem and acquiring data to tuning performance and launching into production • Translate model outputs into clear risk identification/stratification/prioritization, optimal intervention modality assignment, outcomes measurements, etc. • Establish a clear approach to measure and continuously improve product features • Serve as the primary product partner for clients, building strong, trusted relationships to deliver consistent value • Lead implementation of Evio Compass across health plans and health system clients to ensure smooth rollout and strong adoption • Work with customers to understand their priorities, risks, and goals, and collaborate with internal Evio team to tailor the product to meet customer needs • Guide clients on how to act on identified risks to drive the best possible outcomes • Strategize with client partners on optimal modalities to maximize outcomes from risk intervened • Drive client conversations and own client relationships with the primary goal of exceeding client goals and delivering consistent value • Be the tactical thought leader to optimally use Evio resources to drive progress for customers • Develop new processes and capabilities to support and scale customer success; including tracking success metrics such as business impact and client satisfaction

• Design and develop advanced ML algorithms for image, text, audio, and tabular data • Develop and optimize pipelines in Azure to train models across various domains • Implement end-to-end applications in Python, including unit and integration tests • Continuously monitor system performance and troubleshoot issues • Collaborate with cross-functional teams to produce solutions to complex problems