Getting hands dirty, building from ground upDefining and building the future of AI in finance with LLM -based innovation

Led multi-year strategy and roadmap for AI/MLOps initiatives.Key highlights and accomplishments:• Led AI/MLOps engineering vision and strategy for Gap’s enterprise MLOps platform, overseeing technical decisions, technology choices, and architecture for a wide range of AI products, capabilities, and platforms in domain such as inventory, supply chain, and customer analytics.• Designed and developed streamlined approach to feature store that supports agility, co-creation and accelerate speed-to-market of new models.• Architected a global AI/ML monitoring and measurement solution to support automated monitoring of health of our AI/ML eco-system across the company, facilitate prediction explainability and root cause analysis, and provide an unified measurement framework with ability to simulate impact of changes to the business outcomes.• Led and mentored a team of 20+ people including directors, technical managers, architects and product managers.• Members of Gap Enterprise Architecture Leadership

Led multi-year strategy and roadmap for Gap Data Platform.Key highlights and accomplishments:• Led global data strategy, encompassing vision, roadmap, architecture, data platform, and data governance. Partner closely with leaders across the organization to identify the right technology capabilities, architecture patterns and blueprints, defining standards, guiding principles, governance paradigm, and operating model.• Members of Gap Enterprise Architecture Leadership, led and mentored a team of 50+ people including directors and architects. Go-to leader and expert, effective in influencing and working with senior leaders (VP-level and above) on initiatives that impact 85+ product teams comprising 500+ engineers.

Built a sophisticated AI-powered recruiting platform used by 700+ companies such as Facebook, Apple, Google, Amazon, and Goldman Sachs.Key highlights and accomplishments:• Built the machine learning platform from scratch to augment 450M+ profiles with deep analytical insights, surfacing key data points not found in traditional social footprints, such as career highlights, company fit, explicit and implicit skill mastery, and estimated salary. These insights, in turn, power all downstream systems, including search engines, candidate email targeting systems, and recruiting automation platforms.• Built other models in production, including job title clustering, profile segmentation and targeting, algorithmic approach to job leveling, anomaly detection against overqualified candidates, entity recognition and resolution to disambiguate similar profiles, Likely-to-Move indicators, and email sentiment analysis.• Evolved Entelo's data practice from ad-hoc, monolithic, and complicated handoff to a streamlined, systematic, and reproducible data platform that seamlessly integrates to online production systems.• Grew the data science organization to a team of 5+ seasoned machine learning engineers, applied data scientists, and data engineers.

Led a multi-year technical strategy and architecture roadmap for supply chain analytics that is foundational to Gap's next-generation Inventory Management, Merchandising, and Planning solution.Key highlights and accomplishments:• Stood up a team of 20+ engineers and data scientists to deliver on advanced analytics capabilities such as size profiles, demand forecasting, pricing, and promo optimization.• Architected and implemented a robust data engineering and advanced analytics platform including early adoption of Apache Spark, simplifying the data pipeline logic from heterogeneous and complex ETL toolchain to a fully composable, reusable, and testable ecosystem that involves both offline model construction and online model serving.• Led architectural due diligence and technical deep dive on vendor-based technologies in inventory and planning analytics, including demand forecasting and optimizations (pricing, promo, and markdown) in partnership with key business and IT stakeholders to align strategies and define roadmaps for the successful delivery of capabilities.

10+ years of experience in leading and building complex distributed data, analytics, and machine learning systems with deep expertise in the areas of alpha research, portfolio construction, risk and attribution analytics, portfolio optimization algorithms, and tools.Key highlights and accomplishments:• Co-founded and developed the world's first ETF pre-trade system from scratch with an initial target of $50 billion in client trades. The real-time analytics solution was built on sophisticated proprietary machine learning models, capable of pricing 2600+ portfolios in seconds (1M+ ticks per second, 0.5M+ underlying securities, 79 foreign currencies). The project started off as a 2-person side hustle to what became a newly founded team of 10+ engineers, scientists, and financial experts.• Developed a proprietary quantitative investment system to support the US Active Equity business unit. The platform monitors both offline and online evaluation and business metrics of 1000+ investment models and strategies. Overhauled the original system from monolithic, tightly coupled, and rigid architecture to a fully distributed, fault-tolerant, and recoverable solution, improving the overall SLA by >85%.• Developed company’s first comprehensive fixed income/credit analytics platform from scratch within an aggressive timeline of a year to capture the explosive market and growth opportunity. Central to the analytics platform, I was responsible for building the distributed numerical pricing and valuation engine based on open-source Quantlib and other sophisticated proprietary algorithms and models.