Mid-Level Data Scientist

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

Gen AI: Foundry models, AWS Bedrock, Coding Agents