Job Description

The Opportunity

We are looking for a Mid-Level Data Scientist to take real ownership across our healthcare data science practice. Someone who can architect a solution end-to-end, from framing a population health problem through shipping and monitoring a production model, while also raising the bar for the data scientists around them. This is a senior generalist role. You will move fluidly between advanced predictive and risk modelling, rigorous experimentation and causal inference, and applied GenAI and LLM work, all while acting as a technical anchor for the team.

What You'll Do

● Own the design and delivery of predictive and risk models end-to-end, from problem framing through production deployment and monitoring, with a focus on population health and risk stratification

● Architect data science and ML solutions: make deliberate calls on modelling approach, tooling, infrastructure, and MLOps patterns that the rest of the team builds on

● Lead experimentation and causal inference work to rigorously measure the impact of interventions on patient outcomes and cost

● Design and build applied GenAI and LLM solutions, from prompt engineering and RAG pipelines to fine-tuning foundation and foundry models, for use cases like clinical summarization and decision support

● Provide people leadership and mentorship for junior data scientists: pair on hard problems, review code and modelling approaches, and help them grow

● Partner with data engineering and ML engineering to design scalable, production-grade MLOps pipelines: versioning, CI/CD, experiment tracking, and model monitoring

● Write advanced, performant SQL (PostgreSQL) and collaborate on data model and warehouse design decisions

● Translate ambiguous, high-stakes healthcare questions on population health, risk stratification, and cost of care into a clear technical roadmap

● Represent data science in cross-functional conversations with clinical, product, and leadership stakeholders, communicating technical trade-offs in plain language

What You'll Bring

● Master's degree in data science, Statistics, Computer Science, Biostatistics, Bioinformatics, or a related quantitative field (required)

● 3 to 5 years of experience in data science or applied ML, ideally with healthcare, population health, or risk modelling exposure

● Proven track record of owning a model end-to-end, from problem framing through production deployment and monitoring

● Strong Python skills across the ML stack (pandas, NumPy, scikit-learn, plus deep learning frameworks such as TensorFlow or PyTorch)

● Advanced SQL skills, with hands-on PostgreSQL experience

● Deep understanding of MLOps: CI/CD for ML, model versioning, experiment tracking, deployment, and monitoring in production

● Experience designing or leading experimentation: A/B testing, causal inference, or quasi-experimental methods

● Hands-on experience with generative AI and foundation or foundry models (for example AWS Bedrock), including prompt engineering, RAG, or fine-tuning

● Working knowledge of neural networks and deep learning architectures

● Experience mentoring or informally leading other data scientists or analysts

● Comfort with solution architecture: making and defending technical decisions on tooling, infrastructure, and modelling approach

● Excellent communication skills, able to move fluidly between technical depth and clear, plain-language framing for clinical and business stakeholders

Nice to Have (Bonus Points)

● AWS Specialty certification, or equivalent hands-on depth

● Experience owning an MLOps pipeline from design through production, including tooling selection

● Specialization in GenAI or LLM application development: RAG architectures, agentic workflows, or fine-tuning foundation models

● Familiarity with healthcare data standards and interoperability: HL7, FHIR, ICD-10/CPT coding, EHR/EMR systems

Tools & Tech, You'll Work With

Languages: Python, SQL

Databases: PostgreSQL

ML/DS: scikit-learn, pandas, NumPy, TensorFlow or PyTorch

MLOps: Git, MLflow or model registry or W&B

Cloud: AWS

Visualization/BI: Power BI, or Dash