Data Scientist@Louisville, KY ( Onsite)

Location: Louisville, KY ( Onsite)

Job Summary: The Data Scientist is responsible for applying advanced analytics, statistical modeling, and machine learning techniques to solve business problems and improve outcomes across. This role partners with business, product, engineering, and architecture teams to develop data-driven solutions that support enterprise strategies while aligning to data security, governance, and architecture standards.

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 client 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 datasets Soft Skills Analytical thinking Problem solving Stakeholder communication Collaboration Documentation discipline Business acumen Compliance and Security Expectations Handle data in accordance with information protection and acceptable use requirements. Use least-privilege access and approved enterprise tools only. Protect confidential and restricted information in analysis, storage, sharing, and reporting. Follow approved enterprise architecture, data-sharing, and platform standards.