Building the Compliance Management System that builds itself: www.crosswise.io

• Built and currently leading a multidisciplinary team of XXX professionals in engineering, data/AI, product, design, and marketing. This team is tasked with developing Square’s AI and data applications and infrastructure, as well as the Growth Platform, driving growth through AI-enhanced customer experiences. Growth Platform encompasses Growth Products, Growth Tools, Growth Data, and Growth Marketing. • Created a state-of-the-art data infrastructure, featuring an in-house Customer Data Platform (CDP) and seamlessly integrated third-party CRM. This system supports a wide range of business use cases by offering a comprehensive 360-degree customer view, thereby facilitating the deployment of AI and data driven solutions.• Developed an array of Growth Tools, powered by AI and advanced data infrastructure: Communications and Lifecycle platform, Martech platform, Sales and Account Management tools (including revenue intelligence & lead assignment solutions), Customer Success tools (inducing operational efficiency and customer experience solutions), Experimentation and Measurement Tools, etc. • Accelerated Square’s AI Strategy: Leading Square's AI forum, delivering weekly updates to the CEO. Defined and implemented strategic AI priorities, managed complex generative AI integrations, and led the evolution of the AI/ML discipline and professional community development. Promoted Square at AI events and conferences, established strategic partnerships with AI startups and VCs, and strengthened academic collaborations.• Generated over $XXXM in adjusted revenue during customers' first year with Square.

As the Head of Automation within the Growth Platform team, I took the lead in pioneering AI, automation, and the democratization of data, while directing a dynamic full-stack team. My work in developing critical innovations has been integral to enhancing Square's Growth Platform, reshaping the company's strategy towards AI, data, and automation, and significantly improving operational efficiency.

• Built & led the central AI & Data organization at SoFi, comprising over 100 data professionals, driving all data science, ML/AI, analytics, business insights, and data engineering activities. Member of the CEO's Executive Staff.• Developed next generation cloud-based data and AI/ML infrastructure, building an enterprise data lake from scratch, a core machine learning/analytics platform, and modernizing the data warehouse and data pipelines.• Built essential data science & ML/AI models and capabilities and delivered strong business results ($XXM annual adjusted revenue) in areas such as risk management (credit and fraud), pricing, marketing (including efficiency), customer operations, and product.

• Led machine learning, AI, and big data analytics initiatives within the Risk and Information Management department.• Designed, developed and implemented in production >20 machine learning based risk management & marketing models and solutions which led to more than $XXXM incremental annual pre-tax income for the company.• Designed and developed 10 machine learning and AI based capabilities to maximize efficiency of data creation, evaluation, preparation and modeling. These capabilities have been used by 300 data scientists and reduced modeling effort by 50 to 90%, while significantly improving model performance.• Directed an AI R&D center of excellence at Amex, coupled with leading the ML Advisory Board. This governance body was responsible for overseeing and prioritizing machine learning and big data analytics activities globally within American Express.• Collaborated with American Express Ventures as an AI subject matter expert, contributing to due diligence, investment decision process and improved operations of acquired companies.• Pioneered the scaling of our ML platform to handle terabytes of data and led the migration of modeling capabilities to cloud (AWS), significantly enhancing our computational efficiency and agility.

Led teams to develop models and/or data science solutions for digital acquisition/response modeling/offer personalization, new accounts underwriting, credit risk management and fraud prevention. Oversaw the Machine Learning workgroup that conducts cutting-edge research and strengthens knowledge and education in machine learning and data science.

• Co-founded the company and co-developed the core machine learning-based technology for predictive coding. Built and led a data science team consisting of 7 members. • Delivered predictive coding and data analytics solutions for eDiscovery, governance, regulatory compliance, and document management purposes to major law firms and Fortune 100 companies in the New York City area, with a total contract value of $XM. • Interacted with executive-level customers and led technical sales presentations.

• Co-founded the company and co-developed core machine learning-based solutions for predicting outcomes in emergency rooms. • Built and led a data science team consisting of 5 members. • Interacted with major hospital leaders in the Tri-State Area, led technical sales conversations, and secured a contract worth $XM to build a solution for a major NY State hospital.

• Served as a Professor at NYU, specializing in Artificial Intelligence and Machine Learning, with appointments across various academic departments and centers, including the Center for Health Informatics and Bioinformatics, the Department of Medicine, and the Center for Data Science. • Founded, funded and led the Causal AI Lab (Computational Causal Discovery Laboratory), focusing on developing methods and systems to decipher causality from high-dimensional data. • Held the role of Benchmarking Director at the Best Practices Integrative Informatics Consultation Service, responsible for benchmarking best practices in AI, ML, data science, and informatics.• Led and successfully completed dozens of research and operational projects in AI/ML, developed new algorithms for predictive and causal AI from high-dimensional data. Published 80 peer-reviewed articles in prestigious journals and forums and 6 books.• Awarded prestigious research grants from federal agencies (NIH, NSF, VA) totaling $XXM as a principal investigator or co-investigator. Promoted from Assistant to Associate Professor in 2014.

• Designed and developed two software systems for automated development of supervised ML classification models from high-dimensional data. • Developed a software library implementing state-of-the-art causal discovery and variable/feature selection methods. This library had thousands of users world-wide.• Implemented from scratch, ported, and optimized 100+ machine learning, bioinformatics, and statistical algorithms using various software platforms and programming languages. • Executed large-scale computational causal discovery, feature/variable selection and supervised learning experiments on various high-dimensional datasets using high-performance computing systems.

• Contributed to the development of neural network models for drug absorption and the associated software system. • Provided professional services in applying these models to a major use case for a leading pharmaceutical company.