Senior Data Scientist

Overview

We are seeking a highly creative problem solver who tackles puzzles for breakfast and scales the solution by dinner. As a Senior Data Scientist you will join a group of data scientists and data analysts to lead and contribute to AI and ML initiatives that push the boundaries of what is possible in healthcare. The team reports directly to the President of the Provider Solutions division, and you should expect to work with and present to executive management often.

This is a senior individual contributor role, not a people management position. The other data scientists do not report to you. You lead through influence by setting technical direction on projects, coaching and raising the bar for your peers, and setting the example on the hardest problems. The ideal candidate brings a strong background in natural language processing (NLP), hands on experience with large language models (LLMs), and fluency with event driven data pipelines. Software engineering experience is a plus because you will work closely with our engineers.

Responsibilities

• Own AI, ML, and NLP initiatives end to end, from problem framing through model development, validation, deployment, monitoring, and long term maintenance in production.
• Apply LLMs to automate complex, high volume workflows. Partner with software architects and engineering to deliver solutions that meet strict platform latency and throughput requirements across millions of transactions, balancing accuracy, response time, and inference cost through model selection, caching, and prompt design.
• Define what good looks like. Build evaluation datasets and automated eval pipelines for LLM and ML systems, measure quality and error rates on clinical and financial content, and catch regressions when prompts, models, or data change.
• Build and own the data foundations behind these models, including production grade pipelines and feature datasets sourced from our PostgreSQL transactional systems and Redshift warehouse, with attention to data quality, lineage, and reliability.
• Partner with clinicians, pharmacists, and other domain experts to define ground truth, adjudicate edge cases, and validate that model behavior holds up against real clinical workflows.
• Ensure solutions are accurate, robust, and compliant with healthcare regulations including HIPAA, with appropriate handling of PHI and monitoring for bias, safety, and performance drift.
• Act as the technical lead on projects. Set direction, define approach, establish best practices, and influence technical strategy on complex, high ambiguity problems.
• Coach and mentor data scientists through pairing, design and code reviews, and constructive feedback. Contribute to hiring by interviewing and helping raise the technical bar of the team.
• Communicate complex analytical findings to technical and non-technical audiences, including executive management, through clear presentations, reports, and visualizations.

Qualifications

Required Qualifications

• Master’s or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
• 5+ years of professional experience in data science, machine learning, and NLP, with a track record of delivering models into production.
• Advanced proficiency in Python and SQL.
• Pipeline development and orchestration. Hands on experience building ELT and ETL workflows with tools such as dbt, Airflow, and AWS data services (Glue, DMS, Lambda, S3), with attention to incremental loads, idempotency, data quality testing, and documentation and lineage.
• Strong software engineering fundamentals, including tested, modular, production quality Python, Git based workflows and code review, containerization (Docker), and CI/CD.
• Experience productionizing and monitoring models on AWS, including model registry, CI/CD for ML, versioning, and post deployment monitoring, with tools such as SageMaker and MLflow.
• Experience building and evaluating LLM powered applications, for example Claude via Amazon Bedrock, including prompt design, retrieval and RAG pipelines, and systematic evaluation of output quality.
• Proven ability to scope ambiguous problems and deliver solutions end to end with minimal supervision.
• Demonstrated ability to lead projects and coach or mentor other data scientists, guiding technical decisions and elevating team output.
• Excellent problem solving and communication skills, and the ability to thrive in a fast paced environment.

Preferred Qualifications

• Experience in the healthcare or insurance industry.
• Healthcare data literacy across FHIR, HL7v2, and X12, and clinical vocabularies including RxNorm, NDC, ICD-10, and SNOMED.
• Demonstrated experience designing and maintaining production data pipelines. This includes modeling data for an analytical warehouse (Amazon Redshift or a comparable columnar MPP platform), extracting from transactional systems (PostgreSQL), and writing performant SQL against large tables, including the ability to read a query plan and fix a slow query rather than escalate it.
• Comfortable packaging and deploying work in containers (Docker) and deploying models to cloud managed serving infrastructure such as SageMaker endpoints, ECS, or Lambda. Familiarity with Kubernetes is a plus.