Senior Data Scientist

Hollstadt Overview

Hollstadt Consulting is a management and technology consulting firm dedicated to placing professionals at engagements where they will excel. When you work with us, you'll work with a refreshingly real company led and staffed by seasoned experts who are also down-to-earth, good people. We're committed to treating you with respect and helping you achieve your career aspirations.

Since 1990, Hollstadt has been a trusted partner to more than 150 domestic and global companies and has successfully completed over 2,000 projects. Our continued growth has created challenging and rewarding opportunities for accomplished IT and Business Consultants. Hollstadt Consulting is an equal opportunity employer including disability/veteran.

Job Description

This role entails the development of predictive and prescriptive models using extensive datasets to address diverse business challenges. It involves applying advanced multi-variate statistical modeling, machine learning, and data mining techniques. The ideal candidate will operate both independently and collaboratively, pushing the boundaries of conventional analytics to provide insights and actionable outcomes. The primary focus is on supporting Star improvement and Risk Adjustment Optimization initiatives, enhancing the quality of care, and effectively managing member risk.

Collaboration with the ERA and STAR Analytics team, as well as other units within the Center of Excellence, is crucial. The role necessitates a deep understanding of interventions, defining technical requirements, crafting analytic solutions, and constructing statistical models for risk and outcome studies. Working alongside a team of data analysts and scientists, the successful candidate will deliver results promptly.

Success in this position demands a dedication to descriptive/predictive modeling expertise and a proactive engagement in a matrixed environment. The goal is to promote a data-driven culture that contributes to both Stars improvement and Enterprise Risk Adjustment efforts. The role requires a comprehensive grasp of health services research, applied economics, the health insurance industry, and risk adjustment. Additionally, proficiency in the technical intricacies of machine learning, artificial intelligence, natural language processing, predictive reasoning, and traditional econometric methods is essential for addressing both large and small-scale data challenges.

Required Skills and Experiences:

• Master’s degree with a quantitative emphasis, including business, actuarial science, quantitative social science, mathematics, statistics, or computer science required, combined with a minimum 5 years of advanced industry experience in utilizing data analysis methods and tools , OR a PhD and minimum of two years of advanced industry experience utilizing data analysis methods and tools
• Demonstrated ability to evaluate quantitative data from multiple sources using statistical modeling, analytical methods, and critical thinking skills.
• Demonstrated experience with statistical software suites (e.g. Python, R, SAS).
• Strong understanding of database structure, relational database concepts, big data platforms, Cloud environment (AWS, Azure or GCP), and data architecture.
• Exposure to Unix environments.
• Ability to define problems, collect data, establish facts, and infer valid conclusions.
• Strong problem-solving skills exhibited by the ability to approach complex, ambiguous business issues with creative ideas and solution.
• Demonstrated experience in collaboration, teamwork, and cross-functional communication.
• Effective, concise, and professional written, verbal and presentation skills.
• Flexible, self-motivated, and continuously seeking ways to improve.

Nice to Have:

• PhD degree in health services research or statistics or related area.
• Knowledge of healthcare industry including familiarity with health policy, health insurance, benefit plans and product features, provider contracting approaches, reimbursement approaches and health management approaches.
• Knowledge of Risk Adjustment and Stat quality programs is strongly preferred.
• Experience identifying disparities in outcomes and designing strategies to close quality care gaps and combat racial health inequity.
• Interpreting and applying the NCQA Healthcare Effectiveness Data & Information Set™ (HEDIS®).