Sr. Data Scientist - Public Healthcare Fraud Detection & Linked Data Analytics

Integrity Management Services, Inc. (IntegrityM) is a woman-owned small business specializing in assisting government healthcare organizations prevent and detect fraud and abuse in their programs.

At IntegrityM, we offer a culture of opportunity, recognition, and collaboration. We thrive off of these fundamental elements that make IntegrityM a great place to work. We offer the flexibility our employees need to challenge themselves and focus on advancing their professional development and careers. Large company perks. Small company feel.

Location: Remote 
Employment Type: Part-Time, PRN (estimated 40 hours per month)
Reports To: Vice President of PI 

Overview
The Senior Data Scientist handles advanced analytics, AI/ML modeling, and large-scale data integration to lead the development of fraud detection models for federally funded public healthcare programs to include statistical research, policy, and cutting-edge technology to detect, quantify, and mitigate fraud.

Key Responsibilities

• Lead the design, development, and implementation of advanced fraud detection and risk-scoring algorithms leveraging AI, machine learning, and explainable AI techniques, with a focus on linked, cross-agency datasets.
• Architect and oversee secure data pipelines to integrate, clean, and standardize heterogeneous datasets, including federal, state, and open-source data.
• Apply deterministic and probabilistic record linkage methods within a secure environment, ensuring compliance with security protections, and statistical purposes requirements.
• Conduct literature reviews to identify and adapt fraud taxonomies, definitions, measures, and behavioral indicators for healthcare fraud.
• Collaborate closely with stakeholders and other agencies to incorporate subject matter expertise into model design.
• Develop analytical workflows for unstructured data preparation, data quality assessment, and anomaly detection across multi-source linked data.
• Deliver reproducible, open-source analytical outputs, including datasets, codebooks, algorithms, dashboards, and documentation suitable for public release.
• Translate analytical findings into actionable insights for both technical and policy audiences via reports, dashboards, and briefings.
• Provide technical leadership and mentorship to multidisciplinary project teams, ensuring adherence to agile, collaborative, and transparent project practices.

Required Qualifications

• 10+ years of progressively responsible experience in data science, statistical modeling, and advanced analytics, with a proven record of operationalizing AI/ML solutions.
• Demonstrated expertise in fraud detection, anomaly detection, or risk scoring in healthcare, finance, or other regulated sectors.
• Significant experience integrating and linking large-scale, multi-source datasets, including restricted and unstructured data.
• Mastery of Python, R, and similar, with experience in distributed processing frameworks (e.g., Spark) and secure cloud environments.
• Strong understanding of statistical methods, supervised/unsupervised learning, and explainable AI techniques.
• Familiarity with federal data privacy, confidentiality laws, and secure data handling (HIPAA).
• Exceptional written and verbal communication skills, with the ability to produce clear, concise, and actionable deliverables for diverse stakeholders.
• Degree (Master’s or PhD) in Data Science, Statistics, Computer Science, Applied Mathematics, or related discipline.

Preferred Qualifications

• Advanced degree (Master’s or PhD) in Data Science, Statistics, Computer Science, Applied Mathematics, or related discipline.
• Experience working with Medicare, Medicaid, or other large-scale healthcare claims and provider datasets.
• Knowledge of MOU development for data sharing, especially in interagency environments.
• Prior experience with similar secure statistical research environments.
• Track record of publishing or presenting in professional or policy forums.