Decision Scientist – Medicare Data Analysis & Forecast Interpretation

Decision Scientist – Medicare Data Analysis & Forecast Interpretation

Overview:

We are looking for an analytical and technically proficient Decision Scientist to join our data insights team. This role requires deep SQL expertise, solid Python coding capabilities, and strong proficiency in Excel and PowerPoint. The ideal candidate will have a strong analytical mindset, the ability to interpret forecasting or machine learning model outputs, and a background in healthcare or Medicare analytics is highly desirable.

Key Responsibilities:

• Perform complex data extraction and analysis using advanced SQL techniques.
• Conduct data exploration, root cause analysis, and hypothesis-driven problem solving.
• Develop code in Python to support analytical models and reporting workflows.
• Work extensively in Excel to manipulate data, build pivot tables, and create summary insights.
• Interpret outputs from forecasting and machine learning models to derive actionable business insights.
• Support the development of presentations and visual narratives in PowerPoint to communicate findings.
• Collaborate with cross-functional teams to understand business problems and deliver data-driven recommendations.

Preferred Qualifications:

• Bachelor’s or Master’s degree in Data Science, Statistics, Economics, Business Analytics, or a related field.
• Proficiency in SQL (required), with strong Python skills (approximately 75% relative to a Data Scientist level).
• Advanced skills in Microsoft Excel (formulas, pivot tables, data manipulation).
• Intermediate proficiency in PowerPoint to present insights clearly and effectively.
• Experience interpreting the outputs of forecasting or ML models.
• Strong analytical skills, including root cause analysis and pattern recognition.
• Experience in healthcare analytics, particularly with Medicare data, is highly desirable.

Ideal Candidate Profile:

You are a data-savvy problem solver who thrives in translating complex data into actionable insights. Your technical skills in SQL, Python, Excel, and visualization tools are complemented by strong critical thinking and communication abilities. You are particularly adept at working across large datasets, identifying patterns, and supporting strategic decision-making.

FAQ

1. What is the primary focus of a Decision Scientist in this role?
This role focuses on analyzing Medicare data to generate insights and support business decisions. It involves interpreting forecasts, identifying trends, and translating complex data into actionable recommendations. The goal is to improve outcomes related to healthcare operations, cost management, and member experience.

2. What types of data are typically analyzed in this position?
The role works with Medicare-related datasets such as claims data, enrollment data, provider performance, and cost/utilization metrics. It may also include risk adjustment data and population health indicators. Handling large, structured datasets is a key part of the job.

3. How are forecasts used and interpreted in this role?
Forecasts are used to predict trends such as healthcare costs, utilization, and membership changes. The Decision Scientist evaluates model outputs, validates assumptions, and explains results to stakeholders. Clear interpretation ensures forecasts are actionable and aligned with business strategy.

4. What tools and technologies are commonly used?
Common tools include SQL, Python, and data visualization platforms like Tableau or Power BI. Statistical and forecasting techniques are applied using libraries such as pandas, scikit-learn, or similar tools. Experience with cloud data platforms may also be required.

5. How does this role collaborate with business and technical teams?
The Decision Scientist works closely with business leaders, analysts, and data engineering teams. Collaboration ensures data is accurate, insights are relevant, and recommendations are actionable. The role often acts as a bridge between technical analysis and business decision-making.