Commercial Data Scientist

Summary

Doximity is a clinical AI company focused on improving healthcare through an AI-powered workflow platform for clinicians. The Commercial Data Scientist will work with analysts, engineers, and commercial stakeholders to uncover healthcare data insights, develop machine learning and AI solutions, and create client-facing analyses and reusable data products.

Responsibilities

• Deliver novel and impactful insights for client organizations using machine learning and AI technologies
• Work closely with our Product, Platform Data, Insights & Strategy, and Sales teams to create client-facing analyses
• Showcase your engineering skills by creating data products from scratch and automating code so they can be re-used by other teammates and future projects
• Leverage Doximity's extensive datasets to identify and classify behavioral patterns of medical professionals on our platform
• Embrace the challenge of a fast-paced environment to exceed client expectations

Skills

• At least 4 years of professional experience as a Data Scientist or other related roles working with complex, high-volume, structured and unstructured datasets
• Advanced knowledge of statistical concepts - particularly exploratory data analysis, experimental design, and probability theory
• Deep understanding of modern machine learning techniques
• Proven ability to design, train, and evaluate large-scale production models using frameworks such as PyTorch or TensorFlow
• Advanced SQL proficiency - skilled at writing and optimizing complex queries across multiple tables and data relationships
• Advanced Python skills, including understanding of object-oriented programming, production-grade code design, and modern data science libraries
• Hands-on experience with distributed data processing tools and concepts for scalable analysis and model training
• Excellent data visualization and storytelling abilities, with the capacity to translate technical insights into clear, compelling narratives for internal and client stakeholders
• Curious, fast learner with a demonstrated passion for data, continuous learning, and staying current with advances in machine learning research
• Prior experience in a data science consulting role at firms such as ZS, Trinity, IQVIA, BlueMatter, etc., where you thrived in a fast-paced environment

Qualifications

Must Haves

• At least 4 years of professional experience as a Data Scientist or other related roles working with complex, high-volume, structured and unstructured datasets
• Advanced knowledge of statistical concepts - particularly exploratory data analysis, experimental design, and probability theory
• Deep understanding of modern machine learning techniques
• Proven ability to design, train, and evaluate large-scale production models using frameworks such as PyTorch or TensorFlow
• Advanced SQL proficiency - skilled at writing and optimizing complex queries across multiple tables and data relationships
• Advanced Python skills, including understanding of object-oriented programming, production-grade code design, and modern data science libraries
• Hands-on experience with distributed data processing tools and concepts for scalable analysis and model training
• Excellent data visualization and storytelling abilities, with the capacity to translate technical insights into clear, compelling narratives for internal and client stakeholders
• Curious, fast learner with a demonstrated passion for data, continuous learning, and staying current with advances in machine learning research

Nice to Haves

• Prior experience in a data science consulting role at firms such as ZS, Trinity, IQVIA, BlueMatter, etc., where you thrived in a fast-paced environment

Benefits

• Equity (where applicable)
• Annual bonus or incentive compensation (where applicable)
• Medical, dental, and vision insurance
• 401(k) with company match
• Flexible paid time off and company holidays
• Paid parental leave
• Professional development and learning opportunities
• Wellness and mental health resources
• Remote work support and home office stipend (where applicable)
• Company outings, team lunches, and other social events
• Flexible ways of working
• Mentorship and professional development