Data Science Business Intelligence

R ICHA V ARSHNEY

TYSONS, VA

703-***-**** *****************@*****.*** linkedin.com/in/varshney-richa P ROFESSIONAL P ROFILE

Strategic product leader driving AI and ML-powered business transformation. Expertise in risk intelligence, automated decisioning, and data-driven innovation. Proven ability to scale technology, align teams, and deliver measurable business impact—from revenue growth to operational resilience. Core Competencies:

Product Management and Strategy

• Extensive experience in driving product roadmaps and defining technical requirements to support AI-driven solutions, ensuring alignment with business objectives and customer needs.

• Expertise in optimizing product delivery and enhancing user experience. Leadership and Cross-Functional Collaboration

• Significant experience in leading cross-functional teams (including data science, engineering, and design) and fostering collaboration between engineering, data science, and business stakeholders to deliver impactful products.

• Effective in managing high-visibility projects, gaining executive leadership buy-in, and cultivating a culture of innovation and continuous improvement.

AI and ML and Data-Driven Innovation

• Strong background in leveraging AI and ML techniques, such as neural networks and NLP, to improve predictive analytics.

• Proven ability to innovate with advanced data models and AI-powered solutions, significantly improving accuracy and effectiveness in complex problem spaces.

Data Management and Cloud Transformation

• Deep knowledge of cloud migration, platforms, DevOps, CI/CD, and data modernization, with a focus on enabling data- driven solutions while achieving operational efficiency and cost savings.

• Proficiency in defining comprehensive data management strategies to ensure data quality, compliance, and scalability. Business Intelligence and Reporting

• Expertise in guiding business intelligence efforts and designing reporting frameworks that support executive decision- making and enhance operational insights.

• Proficient in leveraging advanced tools and analytics platforms to drive data-driven insights and improve business outcomes.

W ORK E XPERIENCE

FREDDIE MAC, McLean, VA 12/2018 – 02/2025

Senior Director – Data Curation and Data Products Ownership (03/2023 – 02/2025) Director – Risk Systems Development (12/2018 – 02/2023) Promoted to lead a team of more than 35 data engineers and data scientists, reporting to vice-president (innovation, data engineering, and analytics). Directed modernization of residential mortgage risk (credit, collateral, and capacity) data analytics, modeling, and reporting for digital banking platforms, resulting in improved operational efficiencies and reduction in risk exposure, with an over $20M portfolio annual budget serving more than 20 business units across the enterprise. Customer-Centric Product Strategy

• Translated business needs for credit, capacity, and collateral risk management into actionable product roadmaps, user journeys, and technical requirements.

• Collaborated closely with data scientists, data engineers, and UX designers to deliver AI-driven solutions that enhanced user experiences, ensuring user needs shaped the development of AI (unsupervised and human-in-the-loop) solutions, model implementations, and product launches.

• Led cross-functional product teams through full product lifecycle, from ideation to deployment, ensuring rapid iteration and continuous improvement, prioritized effectively through 90-day planning cycles (using JIRA, Confluence, and Kanban) to achieve on-time and accurate deliveries despite roadblocks, maintaining agility in a fast-paced, design- driven development environment.

RICHA VARSHNEY PAGE 2

AI and ML Innovation and Model Optimization

• Directed cross-functional teams across UX, product, engineering, and data science to design and deliver scalable world- class consumer-grade enterprise AI applications, defining KPIs, executing rapid experimentation cycles, and integrating user research to drive strategic data-informed product decisions.

• Deployed innovative AI and ML techniques, algorithms, models, and predictive analytics, including convolutional neural networks for image analytics and NLP models for data enrichment to solve complex business problems and deliver predictive solutions.

• Enabled data scientists and model risk teams to enhance the explainability and interpretability of AI and ML sub- models for home value estimation using advanced methods, including accumulated local effects (ALE), individual conditional expectation (ICE), and local interpretable model-agnostic explanations (LIME) in compliance with FHFA AI and ML Advisory Bulletin, improving model transparency for end users and regulators.

• Led partnerships with collateral policy, data science, and engineering teams to develop a contextual search solution for Fair Housing compliance, using discriminatory text analysis to flag prohibited keywords in MLS listings, addressing FHFA’s MRA.

• Guided data scientists, geospatial analysts, and model developers to deploy CNN-based computer vision models that identified solar panels in satellite imagery, enhancing collateral classification and supporting ESG-linked Bond issuance.

• Partnered with business leads and NLP engineers to extract enriched features from unstructured appraisal data, improving settlement agent clustering and boosting distressed property valuation accuracy by 30% through advanced predictive modeling.

Data Management and Provisioning

• Led data modernization, implementing cloud-based data ingestion and storage architectures, technologies, and formats (AWS, S3, EC2, EMR, ECS, EKS, Snowflake, MongoDB, Control-M, AutoSys, Kubernetes, Talend, Attinuity, Dremio, Bitbucket, GIT, DynamoDB and Jenkins) to support AI and ML and decision intelligence for single family portfolio involving more than 20 data consumption patterns to drive a $3M-per-year savings in operational expenses while enabling state-of-the-art technology to enable accelerated pathways to advanced analytics for supporting single- family divisions risk data needs.

• Defined the end-to-end data management strategy for single family risk end-user-computing (EUC) environment to enable accelerated pathways to advanced analytics (e.g., automated verification of credit, capacity [income and employment] and collateral and appraisal data [PRD, UAD, UPD, HVE, and CCE]).

• Owned the center of excellence for automated testing of a range of big data business solutions using test tools.

• Integrated semi-structured Uniform Loan Application Dataset (ULAD) with legacy structured data while maintaining high data quality across more than 2K attributes so that business stakeholders could unlock new loan origination risk assessment insights; over 150 end-users (credit, capacity, collateral, fraud, enterprise risk, and office of the chief economist teams) leverage this data.

• Collaborated with technical and business partners from cross-functional teams to drive innovation in developing scalable data provisioning frameworks using Snowflake and AWS technologies for enabling accelerated pathways to advanced analytics.

• Managed data architecture and data modeling using Erwin Studio to design, visualize, and implement complex data structures, ensuring data integrity, consistency, and performance optimization. Business Intelligence and Insights

• Guided business intelligence imperatives for executive reporting using Tableau, MicroStrategy, Power BI, SAS, SQL, R, Alteryx, Python, GraphQL and Microsoft Excel, improving executive decision-making through predictive dashboards and automated risk assessments, resulting in 20% faster decision-making for risk mitigation. Business Rules and Decision Management

• Directed design and optimization of condo projects eligibility business rules engine for automating risk assessments for nation-wide condominium projects, enabling lenders to determine pre-funding eligibility before loan delivery to Freddie Mac. Refined rule logic to secure FHFA approval for broad roll-out to more than 2K lenders and expanding access to affordable lending.

Data and Model Governance and Compliance

• Addressed several Matters Requiring Attention (MRAs) to comply with FHFA regulatory and internal audit requirements.

• Evangelized consistent implementation of data governance, quality, and data movement controls in all data pipelines.

• Utilized Collibra for enterprise-grade Master Data Management, enhancing data governance policies and data quality. RICHA VARSHNEY PAGE 3

PENTAGON FEDERAL CREDIT UNION, Alexandria, VA 05/2008 – 11/2018 Director – Credit Systems (05/2015 – 11/2018)

Manager – Credit Systems (05/2008 – 03/2015)

Recruited and later promoted to lead enterprise-wide credit platform modernization, replacing legacy mainframe-based Hogan loan origination system and Strata Decision Management System with a modern FICO’s Originations and Decision Manager platform, unlocking over $50M in potential benefits over five years, reporting directly to the Chief Risk Officer. Enterprise Originations Platform Modernization

• Led full-stack replacement of Hogan LOS and Strata decision management with FICO Originations Manager and Decision Manager, enhancing underwriting automation, credit decisioning, and member experience.

• Designed intuitive LOS UI/UX across borrower, supervisor, underwriter, and loan officer personas for credit cards, auto loans, student loans, personal loans and lines, fixed equity, HELOCs originations. Automated Underwriting

• Managed design and implementation of credit policy updates for direct and indirect consumer loans on CGI's Strata decision management system for multiple consumer lending products for a $9B lending portfolio and implemented risk-based pricing for enhanced decisioning speed, mitigating $500M in risk while boosting NPS by over 20 points. Data Science and Analytics

• Spearheaded initiatives across modeling, portfolio, originations, collections, fraud, and loss mitigation, driving 300% growth in consumer lending assets through actionable insights and predictive analytics. Risk Management and Regulatory Compliance

• Directed credit data architecture, enterprise data warehouse development, and model-ready data pipelines for CECL, DFAST, NCUA, and CFPB reporting—enhancing capital planning and compliance posture.

• Spearheaded originations data modernization, ensuring data accuracy and integrity to optimize performance, streamline processing, and enable data virtualization using Denodo, improving mainframe data accessibility.

• Managed regulatory compliance, credit risk management, portfolio insights, financial risk modeling, and operational risk reporting for all consumer products, first mortgages and closed-ended seconds, and HELOCs. Vendor and Contract Negotiations

• Managed RFP issuance, evaluation, and award processes. Negotiated vendor contracts with major bureaus and originations platform vendors (FICO and CGI) and software (SAS and Tableau), achieving over $7M in savings and 30% cost reductions in credit services.

A DDITIONAL E XPERIENCE

CAPITAL ONE, Mclean, VA

Senior Operations Analyst – Global Lending Operations Data Analyst – Mainstreet Customer Management

Promoted to senior data analyst and recruited for managing consumer loan collections and recoveries strategies. ESGUERRA & EDELINE, LLC, Sterling, VA

Financial and Enterprise Analyst

ABB LIMITED, New Delhi, India

Senior Project Analyst

E DUCATION

OWEN GRADUATE SCHOOL OF MANAGEMENT, VANDERBILT UNIVERSITY, Nashville, TN Master of Business Administration (M.B.A.), Finance and Information Technology

• Edna B. Morris and John B. Morris Scholarship recipient MOTILAL NEHRU REGIONAL ENGINEERING COLLEGE, UNIVERSITY OF ALLAHABAD, Prayagraj, Uttar Pradesh, India Bachelor of Engineering (B.E.), Electronics Engineering

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