Faire is building a B2B e-commerce marketplace to help independent retailers (both brick+mortar and online ones) shop millions of products from brands and makers in a risk-free manner. We are also offering various technology solutions to help our customers streamline their business operations. We believe in using digital transformation of the value chain to empower small business owners & entrepreneurs to thrive and win because it is good for both the economy and the humanity overall.As a unicorn, Faire is backed by Sequoia Capital, Y Combinator, Lightspeed Ventures, Founders Fund, Forerunner Ventures, DST Global, etc. Our GMV is growing ~2X YoY at the moment. We also ranked #8 out of 500 on Forbe's best start-up employers 2021 (https://www.forbes.com/americas-best-startup-employers/#272ed89a6527)The data team at Faire is currently a 70+ person full-stack team including data engineering, analytics engineering, machine learning engineering, and data science. We own data warehouse, ETL pipelines, experimentation platform, AI/ML platform, data catalog & quality tooling, as well as various AI/ ML/ optimization systems related to search & recommendation, paid placement, financial underwriting, lead scoring, marketplace dynamics, incentives & pricing, etc. Members of the team previously worked at top tier companies such as Google, Meta, Amazon, LinkedIn, Uber, Airbnb, Twitter, Square, Pinterest, Instacart, Quora, etc.Check out all the open roles we are currently hiring for here:https://www.faire.com/careersPing me if interested in learning more!

On the side I do angel investment and advise early stage startups in the fields of data, AI, marketplaces, etc. I am also a member of InvestInData (https://www.investindata.ai/) -- an angel investor collective composed of 30+ leading data executives from across the U.S.If you are a startup founder in the above areas and want feedback / inputs on your endeavor, hit me up.

March 2019 - June 2020: Director, Data ScienceAugust 2017 - February 2019: Senior Manager, Data ScienceMarch 2016 - July 2017: Manager II, Data ScienceMarch 2015 - March 2016: Founding Data Scientist At Uber we are building the technology and platform to power the on-demand urban transportation and logistics marketplace, at a global scale, with extreme efficiency, scalability, and reliability. Officially launched in late 2015, Uber Eats is a fast-growing meal & grocery delivery service that currently operates in more than 40 countries and thousands of cities worldwide and serves millions of users, merchants, and delivery partners.As a founding team member of Uber Eats, I built from the ground up, and led the Uber Eats data science & analytics team of 90+ geniuses---data scientists, product analysts, and strategy analysts---across multiple office locations (SF, NYC, Toronto, Hyderabad, etc.). My team powered the Eats business to grow from a few hundred orders per day to over $25 billion annual GMV run rate over 4.5 years. We tackled the following difficult & fun problems associated with Uber's three-sided delivery marketplace:- personalized ranking, search, and recommender systems for restaurants and menu items- meal preparation time, waypoint time, and end-to-end delivery time prediction - dispatch, batching, scheduling, vehicle routing, and general network optimization- demand & supply forecasting, driver positioning, structural & dynamic pricing, delivery zone optimization- marketing spend & growth optimization- restaurant and enterprise analytics- Ads ranking - customer support analytics- ...Cutting-edge techniques in machine learning, statistical modeling, convex & combinatorial optimization, hypothesis testing, causal inference, and behavioral economics, etc. were frequently utilized in our work.

November 2014 - January 2016: Senior Data Scientist As a data scientist at Uber I worked on:• Marketplace Optimization for ride sharing: demand prediction, advance supply positioning, etc.• Uber Everything (Uber Eats, Uber Rush): building intelligent decision systems and models responsible for demand prediction, menu optimization, ranking, batching & scheduling, etc.; mobile product analytics, growth hacking, and more.

July 2015 - present: Board Director July 2013 - June 2015: PresidentJuly 2012 - June 2013: EVP of Marketing & ProductJuly 2011 - June 2012: Director of Marketing• Cofounded CHAIN (http://www.innovationchain.org/) in 2011 with the goal of building a cross-border platform to promote entrepreneurship and innovation in both U.S. and China. As a non-profit organization, we are powered by a team of passionate volunteers, successful entrepreneurs, business professionals and engineering wizards. CHAIN network currently connects 9000+ members in both Silicon Valley and China, and includes more than 30 partner organizations (www.innovationchain.org)• Led a core team of 20+ talented volunteers to execute on event planning and delivery, branding and marketing, partnership and alliance, fundraising and operations, etc. • Together with the team delivered more than 40 entrepreneurship-oriented events (panel discussions, startup bootcamps, video pitches, etc.), including 4 large-scale annual conferences in China

September 2011 - March 2014: Quantitative Analyst• Developed data-driven policies, models, and decision-support tools to improve the efficiency and economy of Google’s global cloud infrastructure and platform, such as: conducting demand forecasting and capacity planning based on the company’s traffic and resource usage growth; measuring compute resource stranding via large-scale monte carlo simulation models; resource oversubscription through leveraging the usage multiplexing effect; improving the VM scheduling algorithm, instance type offering, and pricing strategy for Google Compute Engine, etc.• Communicated findings to senior executives and collaborated with engineering/product stakeholders to drive adoption of recommendations, policies, and toolsSide Projects:• Improved sales forecast for various hardware products (Nexus phones, tablets, Chromecast, etc.) through building predictive models that leverage search query and ads click signals

• Interviewed internal Google Cloud Platform (GCP) stakeholders (product managers, engineers, sales, finance, etc.) and external Cloud customers (EA, Egnyte, etc.) to collect and synthesize their requests for BI metrics• Developed PRD to define a unified BI tool based on RESTful API that would allow systematic access to revenue, billing, and usage metrics of GCP, including advanced analytical features such as anomaly detection and usage pattern clustering• Defined feature release cycles and pitched to both product and engineering teams to get buy-in

----------PhD research project----------• Developed a dual-mode equipment procurement strategy/heuristic with three stages: contract negotiation, capacity reservation, and order execution • Implemented the strategy for the 45nm lithography tool and decreased the equipment procurement lead time by up to two quarters • Presented the project twice to the VP of TMG at Intel; the estimated annual savings on capital procurement exceed tens of millions of dollars• Constructed a detailed business “scenario bank” through interviews and surveys at Intel, which includes 200+ root causes of risk scenarios and 10+ reactive and proactive strategies for each possible scenario• Developed a “reactive dashboard” and a “proactive dashboard” that can quantitatively evaluate the impact of scenarios and effect of actions based on underlying analytical models; this decision support tool is used at Intel to manage operational risks during product transitions

• Empirically identified the existence of customer loyalty using more than 1 million historical transaction records obtained from Nor1’s hotel-chain clients • Developed a new pricing and room allocation heuristic that incorporates guest loyalty status as an input; the heuristics estimated impact on clients’ long-term revenue is a 3-5% increase

• Designed a correlation engine to extract future demand information from online consumer ‘sentiments’ (ratings, comments); utilized this information to improve clients’ demand forecasts• Implemented the prototype model with an Intrigo client and achieved a 20% decrease in average forecasting error

• Organized and streamlined a demand-driven supply chain to meet capacity increase in L'Oreal; built a managerial system to monitor operations and performance of the chain• Presented the plan to an international jury at Paris, France; won second place in the international final of 2006 L'Oreal Ingenious Contest, competing against 47 teams from six countries.