Data Scientist

Assured is transforming the infrastructure of U.S. healthcare using intelligent automation. We’re building an AI-native system of action for provider operations to automate the most painful parts of healthcare - credentialing, licensing, and payer enrollment. These are slow, error-prone processes that cost the healthcare system billions and delay patient care.

We’re backed by top Silicon Valley investors and trusted by the most innovative provider groups and health systems. This is a rare opportunity to join an elite team reimagining one of the most broken parts of healthcare - using cutting-edge ML in the real world, at scale.

We’re looking for a full-stack Data Scientist to join us as our first dedicated data science hire. You'll partner with our AI/ML engineers and product/engineering teams to build, deploy, and scale machine learning solutions that automate key pieces of the healthcare provider lifecycle.

This role is ideal for someone who thrives in early-stage environments, enjoys owning things end to end, and wants their work to have a measurable impact on an industry that desperately needs modern infrastructure.

ML Innovation & Research

• 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, working on real problems. Designing retrieval + LLM pipelines to interpret ambiguous state license rules and payer policy text.

• Scaling 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

Healthcare Data Integration & Insight

• 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

Stakeholder-Facing & Thought Leadership

• 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

Team-Building & Mentorship

• 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
  

Must-Haves

• 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

Preferred

• 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

You’ll Love This Role If You

• Want to lead ML projects from idea to deployment

• Thrive in a 0-to-1 environment and like building from scratch

• Care about real-world impact, especially in healthcare

• Enjoy building systems—not just training models

• Believe great ML products come from close collaboration with product, engineering, and users
  

• High-impact work - Tackle bottlenecks that slow down provider access to patients

• Real-world AI - Work on meaningful applications of LLMs and applied ML in compliance, forms, automation, and document intelligence

• Cross-functional exposure - Collaborate with customers, clinical ops, engineers, and founders

• Early-stage upside - Equity, early influence, and a high-growth trajectory

• People-first culture - Remote flexibility, mental health time, and a focus on outcomes, not hours