Assured Health is looking for a Data Scientist to join as the first dedicated data science hire. You will partner with AI/ML engineers and product teams to build, deploy, and scale machine learning solutions that automate key pieces of the healthcare provider lifecycle.

What You'll Do

Lead the design, prototyping, and deployment of models across document processing, LLM-based automation, risk prediction, and compliance inference.
Apply foundation models, deep learning, and generative AI to healthcare operational data.
Design retrieval + LLM pipelines to interpret ambiguous state license rules and payer policy text.
Scale intelligent document intake across 100+ formats using foundation models and structured rules.
Collaborate closely with engineering and product to take models from concept to production.
Develop and manage data pipelines using structured and semi-structured data (e.g., provider rosters, credentialing forms, payer rules, licensing board data).
Analyze large-scale customer data to derive insights that guide product decisions and customer strategy.
Use operational and compliance data to surface anomalies, inefficiencies, and automation opportunities.
Interface directly with customers and internal stakeholders to understand use cases and shape the right ML approach.
Share learnings via internal memos, external blogs, or whitepapers to grow Assured’s ML thought leadership.
Champion practices around reproducibility, model governance, and continuous learning.
Mentor engineers and future data science hires; help shape the team’s technical direction.
Establish baseline tooling and processes for experimentation, deployment, and monitoring of ML solutions.
Work closely with leadership to align ML strategy with business objectives.

What We're Looking For

3-5+ years of experience building and shipping ML or deep learning models in production.
Strong Python skills and fluency with ML libraries (e.g., PyTorch, TensorFlow, Hugging Face).
Deep understanding of machine learning algorithms, NLP, and modern data processing workflows.
Ability to design experiments, evaluate models rigorously, and iterate fast.
Comfortable working autonomously in ambiguous, fast-changing environments.
Excellent written and verbal communication for technical and non-technical audiences.

Nice to Have

Graduate degree (MS/PhD) in a quantitative field (e.g., CS, Statistics, Physics, Applied Math).
Experience working with healthcare, insurance, or compliance data.
Familiarity with AWS/GCP and production ML workflows (CI/CD, model monitoring, etc).
Experience with LLMs, GenAI, and tools like LangChain, vector databases, or Retrieval-Augmented Generation.
Publications, blog posts, or open-source contributions in ML or AI.