Description

Key Responsibilities

• Analyze large, complex datasets to identify trends, risks, opportunities, and actionable insights.
• Build, validate, and deploy statistical, predictive, and machine learning models.
• Translate business problems into analytical approaches and measurable outcomes.
• Partner with Data Engineers, Application Architects, Product Managers, and business stakeholders to design scalable analytics solutions.
• Develop experiments, forecasting models, segmentation methods, and decision-support tools.
• Communicate findings through dashboards, visualizations, presentations, and technical documentation.
• Support model monitoring, performance evaluation, and continuous improvement.
• Ensure solutions align with enterprise data architecture, reference standards, and approved technology patterns.
• Follow Humana policies for information protection, least-privilege access, secure storage, and confidential data handling.
• Contribute to reusable analytics assets, standards, and best practices across teams.

Required Qualifications

• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, or related field.
• Experience in data science, advanced analytics, or machine learning.
• Strong knowledge of:
  • Python and/or R
  • SQL
  • Statistics and hypothesis testing
  • Machine learning methods
  • Data wrangling and feature engineering
• Experience working with cloud or enterprise analytics platforms.
• Ability to communicate technical results to non-technical stakeholders.
• Experience working with cross-functional teams in a regulated or enterprise environment.

Preferred Qualifications

• Master’s or PhD in a quantitative field.
• Experience with Databricks, Spark, or large-scale distributed data platforms.
• Experience in healthcare, payer, claims, member, provider, or clinical analytics.
• Familiarity with MLOps, model governance, and production model deployment.
• Experience with BI and visualization tools such as Power BI or Tableau.
• Understanding of data architecture, cataloging, and enterprise data standards.

Technical Skills

• Programming: Python, R, SQL
• Data Platforms: Databricks, Spark, cloud data platforms
• Modeling: regression, classification, clustering, time series, NLP, experimentation
• Visualization: dashboards, storytelling, exploratory analysis
• Data Management: data dictionaries, data models, schema awareness, governed

Key Skills

Education

Bachelor's or Master's degrees