Business Analytics Advisor, Payment Integrity - Cigna Healthcare - Remote

Cigna Healthcare•St. Louis, MO

5d•$98,200 - $163,600•Remote

About The Position

We are seeking a highly motivated, forward-thinking, and innovative analytics and data science leader to join Cigna's Payment Integrity team as a Business Analytics Advisor. This role plays a critical leadership function in advancing AI-enabled analytics, automation, enterprise data initiatives, and technical modernization efforts to improve claim accuracy, scalability, and operational efficiency. The ideal candidate brings deep expertise in data strategy, data mapping, automation, AI-assisted analytics, predictive modeling, and enterprise technology, with a strong ability to lead complex initiatives, reduce technical debt, and drive process maturity through structured, well-documented, and scalable solutions. This role will help shape the future of Payment Integrity through advanced analytics, artificial intelligence, machine learning, automation, enterprise data strategy, and technical modernization initiatives that improve payment accuracy, reduce waste, prevent fraud, and deliver measurable business outcomes. This role is part of Cigna's Payment Integrity organization, which is focused on ensuring accurate, efficient, and compliant claim adjudication while reducing unnecessary medical spend across the enterprise. Payment Integrity delivers value through pre-pay and post-pay editing, advanced claim analytics, fraud, waste and abuse prevention, and recovery optimization—all supported by a rapidly evolving data and technology ecosystem. The successful candidate will serve as a strategic data science partner, applying advanced analytical techniques, statistical modeling, predictive analytics, and AI-enabled methodologies to identify opportunities for improved payment accuracy, operational efficiency, fraud and waste detection, and claim editing optimization. This role requires the ability to transform complex healthcare data into scalable business solutions while partnering closely with analytics, engineering, product, and business stakeholders to accelerate innovation and measurable enterprise value. This position will support enterprise initiatives focused on improving claims accuracy, reducing waste and fraud, increasing transparency, and scaling impact through automation, AI-driven insights, and reusable data assets to deliver measurable financial and operational outcomes.

Requirements

Demonstrated advanced proficiency with at least 3 years of hands-on experience in data mining, analysis, and processing using tools and languages such as SAS, Altair, SQL, TOAD, Python, R, or comparable technologies.
Bachelor's degree in Business Analytics, Data Science, Information Systems, Computer Science, Statistics, Applied Mathematics, Healthcare Informatics, or a related field preferred.
7+ years of experience in data analytics, data science, business intelligence, or related analytical disciplines, preferably within healthcare or claims processing.
Proven ability to extract actionable insights using advanced data mining techniques and business intelligence tools to support strategic decision-making.
Strong analytical skills with the ability to translate complex data into meaningful, actionable insights.
Adept at leveraging analytical tools to explore and interpret complex healthcare data, conduct root cause analyses, and deliver actionable insights through clear, data-driven reporting.
Experience applying statistical analysis, predictive modeling, and quantitative problem-solving techniques to complex business challenges.
Strong understanding of machine learning concepts, model development lifecycle, feature engineering, model evaluation, and performance monitoring.
Experience working with large-scale structured and unstructured datasets to identify trends, patterns, risks, and opportunities.
Strong knowledge of data visualization and storytelling techniques utilizing Power BI, Tableau, Python visualization libraries, or similar tools.
Experience in healthcare or managed care environments with direct responsibility for data analysis, data management, and relational database concepts.
Deep understanding of healthcare data and the health delivery system.
Ability to translate analytical findings into business recommendations and influence strategic decision-making.
Excellent communication, presentation, and collaboration skills with the ability to work effectively across technical and business teams.

Nice To Haves

Master's degree in Data Science, Statistics, Computer Science, Applied Mathematics, Analytics, Healthcare Informatics, or a related quantitative discipline.
Experience with rule-based editing platforms and healthcare claims editing technologies.
Familiarity with healthcare claims data and Payment Integrity principles.
Advanced knowledge of provider reimbursement methodologies.
In-depth understanding of Cigna data sources and integration strategies.
Experience applying machine learning, predictive analytics, artificial intelligence, natural language processing (NLP), or advanced statistical methodologies within healthcare environments.
Familiarity with cloud-based analytics platforms such as Databricks, Azure, AWS, Snowflake, or similar technologies.
Experience developing, validating, deploying, and monitoring analytical models in production environments.
Knowledge of healthcare fraud, waste, and abuse detection methodologies and predictive claim analytics.
Experience supporting AI governance, model risk management, data quality, and responsible AI initiatives.
Experience working with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), AI assistants, or intelligent automation solutions.
Familiarity with healthcare interoperability standards, enterprise healthcare data ecosystems, and modern data architecture principles.

Responsibilities

Expand team expertise in external data sources and claim editing integrations to support scalable solutions.
Standardize and format data for optimal use within claim editing programs, ensuring consistency and efficiency.
Collaborate with the Data Analytics team to develop complex editing logic and proof-of-concept models.
Interpret query languages using data dictionaries and facilitate translation through newly implemented editing tools and data sources.
Lead research and updates for the Unified Claim Record (UCR) and associated data streams.
Partner with cross-functional teams to migrate SAS-based edits to ARM (Claim Editing Platform), driving improved outcomes.
Implement structured, data-driven processes to promote consistent and transparent claim editing practices.
Analyze edit performance to reduce false positives and enhance claim capture accuracy.
Document enhancement needs for claim editing and provide timely input to Scrum technical and product teams.
Support the development and refinement of business rules and editing logic to align with evolving business needs.
Collaborate with matrixed business partners to define data requirements, identify improvement opportunities, and communicate analytical findings and solutions effectively.
Drive enterprise data initiatives focused on improving data quality, governance, traceability, and operational effectiveness.
Support technical modernization efforts through automation, reusable assets, process standardization, and improved analytical capabilities.
Develop and apply statistical models, predictive analytics, and machine learning techniques to identify patterns, trends, anomalies, and opportunities within healthcare claims data.
Design, evaluate, and operationalize AI-enabled and data science solutions that improve payment accuracy, reduce false positives, and enhance claim editing performance.
Perform exploratory data analysis on complex healthcare datasets to uncover actionable insights and develop data-driven recommendations.
Collaborate with data engineering and technology teams to build scalable analytical solutions and reusable data assets.
Evaluate emerging AI, machine learning, and healthcare analytics technologies to identify opportunities for innovation within Payment Integrity.
Support development of proof-of-concept models, pilot initiatives, and advanced analytical frameworks to validate business value and scalability.
Design and execute model validation, performance monitoring, and outcome measurement approaches to ensure analytical solutions deliver expected results.
Develop dashboards, visualizations, and executive-level reporting that communicate analytical findings to both technical and non-technical audiences.
Partner with business stakeholders to transform operational challenges into analytical hypotheses and measurable data science initiatives.
Promote responsible AI practices through governance, data quality management, transparency, model explainability, and compliance-focused solution design.
Leverage AI-assisted analytics, natural language processing, and emerging technologies to improve operational efficiency and decision support.
Identify opportunities to automate manual processes, reduce operational complexity, and accelerate analytical insight generation.