Ling Zhang

I advise enterprise leaders and high-potential Data & AI directors on translating AI ambition into measurable business impact. Focus Areas: 1. Enterprise AI Strategy & ROI Alignment 2. Executive Influence & Leadership Acceleration 3. Scalable AI Operating Models Results Delivered Across Career: * $10M+ in AI-driven business impact * Built and scaled high-performing Data Science & ML teams * Designed enterprise analytics roadmaps * Guided AI maturity transformation Primary Client Profiles: * CDAO / CIO in middle-to-large enterprises * Directors seeking VP-level advancement * Organizations scaling AI beyond experimentation

Lead and manage data science, data & ML Engineering teams, provide both strategic and tactical guidance, and impacted business priorities by effectively communicating with the executive team and stakeholders to drive OKR and revenue generation. Promoted a data-driven culture by developing a compelling data & analytics vision and strategy after understanding the current state of data and analytics solutions, communicating it cross teams. Defined a roadmap to move current analytics maturity to the highest (competitor) level

Build and lead a data science team and develop holistic data driven decision analytics strategies and initiatives to optimize a vehicle life time value by empowering content, data science and AI, and product or services. • Build and lead a high-performing data science team and define roles well aligned with key functions of data science. Delivered three products in 2021 and almost minimized portfolio risk to zero in about two years. • Identify seven data science opportunities around a vehicle’s life cycle and optimize a car’s lifetime value by optimizing residual setting in originating leasing portfolio, outstanding vehicles’ risk management, and used vehicle sales and pricing. • Delivered record profit during pandemic period in 2021 • Build and lead an analytics working group cross Ally with data science and analytics professionals from all line of business to drive pioneering data science initiatives, develop quick POC, evaluate third party analytics tools for acquisition and promoting talent rotation. • Identify, evaluate, support acquiring new data sources or technology to add competitive advantage to the exiting solution or create new solutions

• Take initiative to develop a five-years’ data science road map for HR (employee attrition, performance, and engagement) and drive talent management, employees’ engagement, and performance strategies. • Built a HR data science team and lead the whole life cycle of attrition study, project planning and data science phrases, business metrics, collaborating with people from diverse functional groups including executives’ sponsors, site managers, IT, etc. • Developing voluntary attrition models for predicting who will leave with different machine learning methods and a structure equation model for identifying key drivers of attrition that answers why people leave. The models are currently in business execution phrase. • Automate business processes to improve productivity and reduce time and cost. The new design reduced 90% of codes and improved productivity 400% • Build channel models to optimize sales proceeds according to customers’ behavior preference. And perform Monte Carlo simulation with variant stress testing cases to estimate gain/loss positions • Develop Geo optimization service to optimize vehicles transition based on local market trends and demand/supply for used vehicles as well as customer sentiments.

Managed and led a data science team working on multiple projects cross four business segments, customer acquisition, development, retention management, and risk management with data from multiple data sources, demographic, product usages, customer behavior and events, service and billing on big data platform. • Customer Acquisition: To maximize conversions rate and minimize cost with limited budgets, developed response channel models for acquiring new customers; designed scorecard to help answer key business questions such as whom to target, by which channels, how much acquisition cost and what is ROI comparing with a benchmark model, created personalized customer messaging on campaigns per segments. • Customer Development: To improve customer engagement and experience, built cross or up-sell and recommendation models such as how to turn one product customers into more products’ customers and how to help customers become brand promoters from detractors, etc. Built marketing products by utilizing models to design an optimized metric according to key objectives such as maximizing product usage, or profit or NPS score; built a recommendation engine based on PSEM to recommend customers’ next best action. • Retention Management: Developed models to mitigate churns and designed metrics that help answer questions like who will churn, when will they churn, why will they churn, what is the customer life time value, who can be saved, and how to save them. Built a retention product to guide retention decisions and processes. • Risk Management: Built a collection engine to optimize the treatment of delinquent customers and automate collection process by answering six core business questions: 1) Who will most likely become delinquent; 2) when will they become delinquent; 3) what is the delinquent amount; 4) Why do they become delinquent; 5) how likely will they be cured; 6) who are curable and how to heal these curable customers? The product reduced about 11% delinquency rate

Led and managed overall analytical solution for developing intelligent analytics behavior matrix by analyzing multiple social media contents on Hadoop big data platform with R tool. Initialized product concept, developed predictive models and performed social user profile matching with record linkage technology, natural language processing (NLP) with different text parsing technologies, topic extraction and relationship mining with innovative algorithms and advanced clustering method like LDA, sentiment detection with different machine learning methods like SVM and scoring based on emotional words matrix as well as visualization and applying emotion score to develop marketing models.

• Supervised a team to develop record linkage technology for content enrichment, perform data cleansing/standardization and normalization, data warehouse integration, entity relationship resolution – a patent granted for applying Additive Logistic Regression linking method and a HMM model for law firm names normalization • Performed record or document clustering with KMeans, EM and Hierarchy methods and invented two clustering algorithms, documents classification with SVM, Naïve Bayes and KNN method; applied active learning method to develop quality training dataset; proposed and developed patented meta-ranking system for ranking legal cases based on case citation network and LexisNexis proprietary metadata; conducted research on citation patterns in legal cases; designed statistical methodologies, performed statistical analyses and developed predictive models based on insurance contents, public records and legal cases’ with SAS and R software and applied tons of statistical analysis for diverse contents. • Developed big data solutions in diverse applications with ECL on HPCC, and implemented machine learning algorithms and statistical functions on platform, migrating hundreds of scoring methods in legacy system to the HPCC platform. • Worked closely with product managers and researchers as a consultant to understand business requirements and objectives and translate into technical solutions. Developed technical specifications and led analysis of user needs, system and database design and implementation and test programs; communicated with third parties, analysed and evaluated emerging or existing technologies.

Major in Data Mining, Object Oriented design

statistics, algebra, probability, algorithm design