Sr. Clinical Data Scientist - Applied Intelligence Solutions

Truveta•Seattle, WA

6h•$155,000 - $170,000•Hybrid

About The Position

Senior Clinical Data Scientist - Applied Intelligence Solutions Truveta is the world’s first health provider led data platform with a vision of Saving Lives with Data. Our mission is to enable researchers to find cures faster, empower every clinician to be an expert, and help families make the most informed decisions about their care. Achieving Truveta’ s ambitious vision requires an incredible team of talented and inspired people with a special combination of health, software and big data experience who share our company values. Truveta was born in the Pacific Northwest, but we have employees who live across the country. Our team enjoys the flexibility of a hybrid model and working from anywhere. In person attendance is required at least once per year for an onsite meeting. For overall team productivity, we optimize meeting hours in the pacific time zone. We avoid scheduling recurring meetings that start after 3pm PT, however, ad hoc meetings occur between 8am-6pm Pacific time. #LI-remote Who We Need Truveta is rapidly building a talented and diverse team to tackle complex health and technical challenges. Beyond core capabilities, we are seeking problem solvers, passionate and collaborative teammates, and those willing to roll up their sleeves while making a difference. If you are interested in the opportunity to pursue purposeful work, join a mission-driven team, and build a rewarding career while having fun, Truveta may be the perfect fit for you. This Opportunity We are seeking a Senior Clinical Data Scientist to join the Catalysts team, focused on applied intelligence solutions for Truveta. This role focuses on building the concrete intelligence assets that intelligence uses to solve defined healthcare problems correctly and safely. You will work on concrete problem types such as safety monitoring, cohort feasibility, HEOR analyses, clinical trial workflows, and operational oversight, and be responsible for directly creating the knowledge assets, examples, and guardrails that intelligence uses to support these problems effectively and safely. A core part of the role is creating and maintaining practical intelligence assets that intelligence directly uses to support specific, repeatable solutions. These assets are contributed into a shared knowledge base maintained by the broader Catalysts team. You will also influence how intelligence consumes and applies knowledge by producing clear, reusable assets that translate complex technical and domain concepts into forms usable by both intelligence systems and non technical stakeholders. Out of scope: This role is not focused on model training, infrastructure, or large scale implementation. You are expected to be technically fluent and able to prototype, while deeper execution is handled collaboratively with peers who specialize in speed and scale.

Requirements

Education: Bachelor’s degree or equivalent experience in a quantitative or technical field such as computer science, data science, statistics, engineering, or a related discipline.
Applied intelligence and LLM expertise: Strong understanding of how large language models consume, retrieve, and reason over knowledge, and how to structure information so it can be used efficiently and reliably by intelligence systems.
Healthcare domain knowledge: Solid understanding of healthcare problem spaces such as clinical research, HEOR, clinical trials, and healthcare operations, and the types of questions and decisions users need support with.
Generative AI fluency: Hands on experience using generative AI tools to synthesize, generate, and refine knowledge assets including guidance, templates, prompts, and structured explanations.
Applied solution design: Ability to design and prototype applied intelligence solutions for specific problem types by combining domain knowledge, templates, prompts, guardrails, and workflows.
Technical fluency: Comfortable writing and reviewing production quality analysis code (e.g., SQL, Python, or R) to prototype analyses, validate logic, and create reusable examples that intelligence and teammates can reuse. Deep engineering expertise is not required, but hands on technical fluency is expected.
Knowledge creation and translation: Create clear, reusable intelligence assets by synthesizing expert input or authoring directly, translate technical and domain specific concepts into guidance that is understandable and actionable for a wide range of audiences, and explicitly define exclusions and limitations where intelligence should not provide answers.

Nice To Haves

Healthcare and EHR exposure: Experience working with healthcare or EHR data, including awareness of common data quality issues, biases, and clinical context.
Decision support systems: Experience designing, evaluating, or contributing to decision support systems, recommendations, or intelligent assistants.
Applied research background: Background in product analytics, applied research, or applied machine learning in support of real world decision making.
Advanced technical depth: Experience building more complex analytical workflows, reusable code libraries, or data products that others rely on, even if this is not the primary focus of the role.
Knowledge formalization: Experience translating expert knowledge into structured guidance, documentation, schemas, or training materials.
Teaching and enablement: Teaching, mentoring, or enablement experience with analysts, researchers, or cross functional teams.

Responsibilities

Intelligence solution construction: Independently build small scale, concrete intelligence solutions for specific healthcare problems by creating the required knowledge assets, examples, reference analyses, and guardrails that allow intelligence to answer the right questions and respect clear boundaries, within current platform capabilities.
Domain problem breakdown: Break down healthcare problems in areas such as clinical research, HEOR, and clinical trials into the concrete assumptions, decision points, and data considerations that intelligence needs to handle correctly.
Knowledge and guidance definition: Create and maintain the domain knowledge, guidelines, templates, and decision logic that intelligence systems need in order to perform effectively in each problem domain.
Agent and workflow shaping: Shape how intelligence agents operate at a practical level by defining task breakdowns, decision logic, and required knowledge, working within current platform constraints.
Knowledge extraction and transformation: Work with domain experts and users to extract assumptions, heuristics, and expertise, and convert them into structured inputs that intelligence systems can reliably consume.
Generative AI reasoning: Apply an understanding of how generative AI systems retrieve and reason over knowledge to improve correctness, safety, and consistency of intelligence outputs, using concrete examples and test questions.
Validation and guardrails: Define and validate constraints, exclusions, and limitations to ensure intelligence outputs are appropriate, trustworthy, and aligned with real world healthcare use.
Collaboration and facilitation: Work closely with colleagues across the Catalysts team, contributing domain expertise, intelligence design, and reusable assets into a shared knowledge base, and collaborating as peers on intelligence solutions rather than owning a separate execution pipeline.