Staff Data Scientist, AI/ML

About the role

This role guides AI and analytics work that supports medical professionals across a major U.S. The position operates within a distributed, engineering-focused team that owns data products from ingestion to recommendations. Success depends on applying advanced statistics and machine learning to high-volume healthcare datasets.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Build product and client-facing analytics that translate model outputs into actionable insights for clinicians and stakeholders. Shape data team strategy by partnering with product leaders and managers, balancing execution with exploratory planning. Apply advanced SQL to write and optimize complex queries across distributed systems and relational schemas. Use Python and production-grade object-oriented code to build reliable, scalable data solutions. Communicate technical results through clear visualizations and narratives that help stakeholders make informed decisions. Engage with UNIX command-line tools and standard development workflows in a landscape of hundreds of private GitHub repositories.

Requirements

Bring at least 5 years of professional experience as a Data Scientist or in similar roles involving complex, high-volume data. Demonstrate deep knowledge of statistical methods, including exploratory data analysis, experimental design, and probability theory. Show mastery of modern machine learning techniques such as deep learning, reinforcement learning, and LLM fine-tuning methods including SFT, DPO, and RFT. Prove experience designing, training, and evaluating large-scale models using frameworks such as PyTorch or TensorFlow. Exhibit advanced SQL proficiency for writing and optimizing complex queries across tables and relationships. Apply advanced Python skills, including object-oriented design and modern data science libraries. Have hands-on experience with distributed data processing for scalable analysis and model training. Deliver strong data visualization and storytelling to turn technical findings into clear narratives for stakeholders. Show curiosity as a fast learner who stays current with machine learning research and practices.

Practical notes

The position is full-time in the U.S. with compensation banded at $170,000 to $248,000 inclusive of salary and equity. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data scientists in this role work with extensive healthcare datasets and production-scale tools. Modern model training frameworks such as PyTorch and TensorFlow are central to the work. The team operates as a distributed, cross-functional group that values continuous learning. Strong communication skills are essential for translating technical results to non-technical audiences. This role emphasizes impact on the healthcare system through data-driven decisions.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.