Warren Hearnes, Phd Email & Phone Number

Educate executives on what is possible. Create and implement mathematical optimization, machine learning, and artificial intelligence solutions for business.

Focus on using data visualization, machine learning, and AI to deliver clear, actionable insights for sports professionals. Our customized analytics help agents and industry insiders make smarter decisions, whether in MLB, NBA, or NFL. From player projections to contract negotiations, we reveal hidden strengths and future performance through predictive models and AI-driven tools. Our interactive solutions support key moments like arbitration, free agency, and draft strategy, setting a new standard for data-driven success in sports.

Richfield, Minnesota, Us

Recruited to Best Buy's Chief Data Scientist role, responsible for AI/ML vision and strategy. Lead a team of 45+ data scientists that elevate the use of AI, ML, and optimization across the enterprise. Focus the right resources on the right problems by implementing a new operating model for better coordination between the business, analytics, and engineering. Serve as Executive Co-Sponsor of Military ERG.AI STRATEGY: Created the company's AI Strategy, setting guiding principles and high-level roadmap for implementing all types of AI, ML, and optimization to solve business problems.LEAD ATLANTA HUB: Best Buy Technology & Analytics Center leverages ATL's high-quality tech talent and makes Best Buy an employer-of-choice in the data science community.KEY PROJECTS: Support diverse projects, each demonstrating the application of AI/ML in solving specific business challenges, improving margins, and driving revenue growth. Examples include:Operational Efficiency: We improved Install/Delivery capacity by integrating GPS data into our ML models, optimizing service scheduling and efficiency. Our Labor Optimization models are designed to improve the customer experience in each store, based on demand within each market and the store footprint.Cost Savings and Improved Decision Making: Our innovative work in media mix optimization led to significant cost savings and increased model transparency. Work in merchandising forecasting has gained significant traction in category planning.Targeted Engagement: Customer segmentation and the development of a more accurate customer lifetime value model provides deeper insights into customer behavior for more effective marketing and personalization strategies.Standardizing Analytics Deliverables: Implemented an Analytics Product Lifecycle Framework for creating scalable and repeatable analytics solutions. This initiative has clarified roles, improved project focus, and facilitated the deployment of analytics solutions.

Atlanta, Ga, Us

Chief Analytics Officer role as Head of Analytics, Optimization, & Data Science at CDLX. Set and executed analytics/data science strategy that helped propel CDLX from start-up to multi-billion dollar public company. Responsible for all revenue-generating algorithmic development, measurement, and scalable solutions.Promoted and added responsibility for BI, Merchant Cleaning, and Market Level Reporting functions to the team so that analytics and data science could be used for significant advantage by CDLX in those areas as well. Grew team to 75+ analysts/data scientists responsible for answering analytical questions, cleaning/QA of internal data, BI, & data science for core products. NEXT-GENERATION PLATFORM: Developed state-of-the-art AI/ML approaches that serve as the foundation for the next-generation CDLX platform called Ads Manager + Ad Server.GROWTH VIA AI/ML: Focused on continued growth and scale required some fundamental changes in the ways that CDLX approached long-standing problems. These solutions required very close coordination with Product and Engineering in order to set the best foundation for the future. AGILE/RAPID PROTOTYPING: Identified a special SWAT Team to work on problems identified by the Executive Team that were not being addressed quickly enough via the normal CDLX processes. The team set the example for transparency, collaborating with stakeholders, and documenting solutions as they churned out deliverables that helped CDLX generate significant revenue.ADAPTABILITY: The pivot around COVID insights and challenges by the Analytics & Data Science team was widely recognized as the primary key for navigating CDLX through 2020.FURTHER CONSOLIDATION: As the Analytics & Data Science team's successes grew, CDLX added more functions and responsibilities to the team. COMPANY-WIDE LEADERSHIP: Led the coordination & collaboration of the Senior Leadership Team - the senior leaders at CDLX below the Executive Team - to meet company goals.

Atlanta, Ga, Us

Chief Analytics Officer role as Head of Analytics, Optimization, & Data Science at CDLX. Set and executed analytics & data science strategy to drive revenue as the company prepared for IPO. Led consolidated team of 35+ analysts/data scientists to support reporting, modeling, & insights for campaign inventory & advertiser/financial institution partners, handling requests for Cardlytics' core business utilizing SAS, R, & Python.CONSOLIDATION OF ANALYTICS & DATA SCIENCE: Responsible for the analytics/data science support of CDLX pre-IPO strategy to focus on our core strength -- cash-back rewards via banks' mobile and web platforms. Consolidated previously siloed functions in order to maintain focus and use resources most efficiently.IMPROVEMENT & AUTOMATION: Continued to focus on iterative ways to improve key deliverables while maintaining service levels for existing business. Applied learnings and experience from Data Science team to augment the processes for client-facing teams, eliminating bottlenecks.

Atlanta, Ga, Us

Built data science capability within the growing start-up to rapidly prototype new ideas and products. Selected for internal R&D team to build new lines of business for programmatic display, insights, audiences, & media measurement and create new revenue. Built/led cross-functional data science team with diverse backgrounds in business, programming, stats, & operations research. Ingested large streams of data, delivered analytics, & created insights/predictive models with open-source big data tools such as R, Python, Spark, Drill, & other Hadoop-ecosystem offerings.DATA SCIENCE FROM SCRATCH: Created the first true data science capability within CDLX. Pulled from both internal talent as well as hiring external to allow the company to rapidly prototype potential new products in the measurement space.CUSTOMER IDENTITY GRAPH: Developed and executed the first customer identity graph with CDLX that allowed for a more streamlined and accurate approach to targeting and measurement.PROOFS-OF-CONCEPT: Worked with internal and external stakeholders on various POCs that could extend CDLX capabilities. Responsible for analytical requirements from design through execution. Revenue from POCs accounted for at least 15% of CDLX revenue after the first year.

Atlanta, Ga, Us

Helped scale out client-facing analytics capability as start-up continued to grow. Promoted to lead the growing Advertiser Analytics team, supporting sales teams with insights & analytics needed to win & retain new business. Interacted with sales teams & clients for national brands, answering analytical questions. Built team of client-facing directors & analysts for different sales verticals.TEAM-BUILDING: Hired and trained a new team of client-facing analysts that could handle the growing number of advertisers using the CDLX platform.STREAMLINING & AUTOMATION: Used a rapid-prototyping approach to identify analytic deliverables that connected well with clients, isolate the parts that are common to all advertisers (or within a specific industry vertical), and automate those pieces. Continued to iterate for improvement.

Atlanta, Ga, Us

Recruited to support Sales with consultative marketing analytics in a young startup that would rapidly expand over the next few years. Created the measurement process for incremental return on ad spend for campaigns in order to prove the value of the new advertising channel within bank platforms. Consulted with partner advertisers and conducted in-depth analysis to answer questions regarding share change, opportunities to grow, & other market-/category-level insights. As revenues grew, helped build out a team by hiring superb talent that can deliver analytics & communicate findings.CLIENT-FACING ANALYTICS: Responsible for the analytics consulting and deliverables for approximately 50% of the advertising clients that used the CDLX platform.CAMPAIGN MEASUREMENT: Developed the measurement methodology that is used for measuring the incremental return on campaigns within the CDLX platform.START-UP: Worked closely with engineering teams to identify the best sources of data and to ensure that the pipelines were established and monitored for quality.

Atlanta, Georgia, Us

Recruited to build and lead the in-house CRM analytics team for direct mail and email marketing for The Home Depot. Used data mining and advanced analytics to provide actionable insight on enterprise data. Managed 6 in-house senior SAS analysts that developed analytical models and generated optimal target lists for 1-to-1 personalized communication campaigns. Had dotted-line control of up to 3 on-shore/20 off-shore outsourced SAS analysts for planning, measuring, and reporting of marketing campaigns.TEAM BUILDING & PROCESS MAPPING: Managed CRM Analytics and Campaign Measurement through major transformation from agency-based, outsourced CRM to a state-of-the-art in-house capability. Developed processes for targeting, measuring, and improving campaign communications. Coordinated CRM analytics efforts within THD and with outside agencies.PREDICTIVE MODELING FOR MARKET RESEARCH/CAMPAIGNS: Led the development of advanced analytical models for customer response and uplift after receiving direct mail/email communications, as well as “next recommended product” engines for 1-to-1 communications. Incorporated third-party models for customer churn and customer lifetime value into targeting activities. Applied learnings into subsequent models.CUSTOMER SEGMENTATION MODEL ENHANCEMENT: Led the analytics effort to incorporate primary research for the Pro segment into a model enhancement for more precise Pro vs. Consumer segmentation. The enhancement helped speed the adoption of the model by executive leadership and inform the use of the model in all aspects of marketing communications.CUSTOMER LIFETIME VALUE (CLV): Led an analytics effort to build a CLV model based on statistical methods that estimate the purchase frequency and transaction value for every customer, helping target the most valuable customers and tailor communications to correct segments.

Atlanta, Ga, Us

Manager in the Data Mining and Advanced Analytics Group, an internal consulting team in the HQ of UPS. Provided quantitative decision-making guidance, developed custom solutions, and managed significant projects through prototype phase. Worked with other corporate resources to achieve success on shared-services projects, including:PREDICTIVE MODELING FOR FRAUD DETECTION: Worked with Corporate Credit to identify risky new accounts opened through UPS.com. Prioritized investigative efforts by impact on revenue. Prevented over $8.5MM of fraud in first year.OPTIMIZATION FOR EMPTY ASSET BALANCING: Worked with corporate Transportation Finance to develop spreadsheet-based tools that minimized costs, conducted “what if?” scenarios, and determined price points of various movement alternatives. Identified several reduced-cost alternatives with an annual savings of $1.5MM in first year. PREDICTIVE MODELING: Generated "target lists" through standard commercial packages or customized internal algorithms for various groups within the corporate HQ to discern which subset of the potential customers are more likely to react positively to a campaign or product. PRICING OPTIMIZATION & REVENUE MANAGEMENT: Worked with stakeholders in Revenue Management to improve domestic target pricing models and helped develop/implement a market rate-based model for bidding on a portfolio of services to international customers. The patented approach improved yield and provided a more consistent pricing strategy, resulting in an estimated $50MM-$100MM revenue improvement.SUSPICIOUS ORDER MONITORING: Worked with UPS Healthcare Logistics to develop a patented approach for identifying orders of controlled substances that are “unusually large or frequent”, as required by federal regulations. In addition to regulatory compliance, the approach increased UPS’s value proposition in the healthcare field and generated at least $1MM in new revenue during the first year.

Promotion to next Technical Staff level. Managed the company’s main resource -- fiber inventory. Other responsibilities included improving manufacturing processes, optimizing systems, and developing decision-making tools. Key results:OPTIMIZATION OF CUSTOMER ORDER FULFILLMENT: Led development of optimization and simulation models that allocated capacity and resources, identified optimal production strategies, and improved manufacturing process flow. Initiated a process to validate current in-process inventory for a major customer and identify “matched” color sets using CPLEX. Maintained 100% on-time shipments to this customer even under unexpected increased customer demand, while decreasing in-process inventory by 40%.SPREADSHEET MODELING FOR PRODUCTION PLANNING: Project leader for fiber production planning using a spreadsheet-based interface to an AMPL/CPLEX optimization model that incorporated hard orders, forecasts, and revenue pricing to create the optimal production plan with respect to end-of-quarter income statements. Resulted in a 55% reduction in raw material inventory.OPTIMIZATION FOR EFFICIENT RECLAMATION OF SCRAP INVENTORY: Applied unused materials in the ribbon reclamation area to new customer orders. Led the Production Control part of that effort to identify potential matches and view the current inventory. Resulted in the use of over $5MM of orphaned resources in new customer orders.

Managed the company’s main resource -- fiber inventory. Other responsibilities included improving manufacturing processes, optimizing systems, and developing decision-making tools. Key results:OPTIMIZATION TO REDUCE SETUP TIMES: Led the operation and improvement of an optimization model that allocated inventory to orders in the most profitable manner. Reduced setup time by 30 hrs/week, reduced handling of fiber spools by 5%, and reduced scrap by 20%, increasing revenue over $5MM annually.INVENTORY VISUALIZATION TOOLS: Identified need for better visibility/management of inventory. Established parameters for “useable” inventory and developed web-based tools for identifying and fixing problems, freeing up over $25MM in stranded inventory.

Atlanta, Georgia , Us

INTENSIVE RESEARCH INTO MACHINE LEARNING DURING ITS EARLY STAGES IN THE 1990s. Developed hierarchical controller that learned through reinforcement from success/failure & generalized learned actions using fuzzy set theory. Conducted original research projects funded by National Science Foundation (NSF), National Aeronautics & Space Administration (NASA), and Electric Power Research Institute (EPRI). Co-authored 9 articles in journals or conference proceedings and 2 book chapters in decision-making with uncertainty using machine learning, reinforcement learning, fuzzy set theory, intelligent control, & optimization.#machinelearning #reinforcementlearning #fuzzysets #intelligentcontrol

Arlington, Virginia, Us

Served in a number of positions as a Field Artillery Officer: Fire Support Officer for A/5-14IN, Fire Direction Officer for F-7FA, and Platoon Leader for F-7FA, all in the 25th ID at Schofield Barracks, HI. (1) Responsible for the training, morale, and welfare of up to 55 soldiers and the maintenance and accountability of $1.5MM in equipment. (2) Integrated a cohort of 25 new soldiers into the platoon. (3) Developed new leaders for each 10-man section. (4) Rewrote battery-level publications to clarify duties and responsibilities of personnel.(5) Earned Honor Graduate (top overall graduate) and Master Gunner (top gunnery graduate) honors at Field Artillery Officer Basic Course, Class 11-89.

Doctor Of Philosophy - Phd, Industrial Engineering

Master Of Science - Ms, Operations Research

Bachelor Of Science - Bs, Mathematics - Operations Research