Lead Data Scientist - Healthcare

Job Description Lead Data Scientist - Hybrid - C2H Location: Columbia, MD (Hybrid) Employment Type: W2 Basis (Contract to Full Time) Purpose Leads proliferation of machine learning and artificial intelligence throughout the enterprise. Identifies and solves business problems using numerical techniques, algorithms, and models in statistical modeling, machine learning, operations research, and data mining. Leads interactions between analytics, business units, and other departments. Must have previous Provider data experience working with leadership on provider data issues, accuracy, and provider data interactions with claims and claims platforms. Knowledge of Salesforce and Facets is required. Previous demonstrable experience with reporting to leadership and working remotely with teams is preferred. Essential Functions • 20% Leads all data mining and extraction activities and applies algorithms to derive insights. • 15% Synthesizes analytical findings for consumption by teams and senior executives. • 15% Leads proliferation of machine learning and artificial intelligence solutions. • 15% Applies artificial intelligence techniques to achieve concrete business goals while managing limited resources and constraints around data. • 15% Mentors and develops junior data scientists for advanced data analysis. • 10% Translates business priorities and creates data science deliverables. • 10% Leads implementation of ML/AI/DS best practices for new data products and builds robust and scalable software. Qualifications • Education Level: Bachelor's Degree in Statistics, Mathematics, Computer Science or related field. • Experience: 8 years of relevant work experience. • In Lieu of Education: In lieu of a Bachelor's degree, an additional 4 years of relevant work experience is required in addition to the required work experience. Preferred Qualifications • Ability to communicate effectively and document objectives and procedures (Expert). • Ability to leverage a wide variety of data science tools and frameworks (Expert). • Ability to support data exploration and data analysis tasks (Expert). • Knowledge in model evaluation, tuning, performance, operationalization, and scalability of scientific techniques (Expert). • Proficiency in statistical modeling applications (Expert). • Proficiency in advanced SQL in multiple syntaxes (Expert).