As a manager of Managers and the the org leader for the ACE (After Conversion Experiences) org within Ads integrity I was responsible for leading a multi disciplinary org tasked with maximizing the growth of Meta’s Ads business across both Facebook and Instagram by tuning and optimizing the entire ecosystem for growth over both the long and short term. The team does this by collecting data about users/businesses and then providing a combination of positive incentives as well as negative consequences for businesses to manage both risk and reward for meta.Within my direct reporting span there were 4 separate engineering teams focused on: signal collection, ML, data platforms, enforcement, anti circumvention and professional shopping. Additionally, I indirectly support a larger multi disciplinary group of XFN including DS/DE/PMM/PM/Research. Total ACE org: 45 members.Major accomplishments:• $2B incremental impact on Meta’s revenue on an annualized basis• Responsible for protecting/growing roughly 35% of Meta’s $17B revenue• Grew the org from 7 -> 45 in 3 years• Mentored for many Sr IC’s and fellow managers. This includes individuals both inside my org and across the company.• Acting as a facilitator for Meta’s cross company leadership development: FLiP - Facilitating Leadership in Practice. Aimed at growing leaders into leaders of leaders.

As director of the personalization organization my organization was responsible for building the products, services and platforms that utilize data, algorithms and analytics to improve the user experience every single day. My teams worked on a wide variety of problems including: Content recommendations, data infrastructure, data science (algorithm development, sentiment analysis, topic classification) and new product development. All of our software was built on AWS.Selected Products:• When-To-Post: Part of Lithium’s Social Publishing suite. Determines the optimal time to post content to various social networks such as Twitter, Facebook, Instragam.• Social Impact Ranking: Allows brands to understand how they are competing with others on social channels. Includes: audience statistics, support statistics, sentiment analysis and topic classification.• Content Recommendations: A social recommendations tool that recommends engaging content for users of Lithium's social communities.• Social Explore: A new product that allows marketing and social support organizations to efficiently engage with and understand what their customers are saying on social channels.I was also the executive sponsor for Lithium's Women in Tech group (LiWIT). The aim of the group is to support growing more women engineering leaders inside the company as well as future engineering leaders who are currently in high school and considering attending university for Computer Science or Engineering.

Outcome: Successful exit. Acquired by Zeta Global. https://techcrunch.com/2017/07/18/zeta-global-acquires-boomtrain-to-bring-more-machine-learning-to-its-marketing-automation-platform/Boomtrain is centered around a personalized notification platform that leverages machine learning and predictive algorithms to drive increased clicks, engagement and revenue through customer communications. Our technology surfaces the content that’s most likely to engage any individual and delivers it via email, push notifications, or the web. All automated, all in real-time.As the head of Engineering I was responsible for running both the engineering and product organizations. We were responsible for working closely with our customers, sales, marketing, services to determine what our customers needed to be successful, delivering the solutions and measuring the results.On a day-to-day basis I was involved in a wide range of activities including: building and architecting realtime recommendation systems, data science, recruiting, fund raising and engaging directly with customers.

Over 300 million members use LinkedIn to be more productive and successful. My team consisted of a mix of engineers with backgrounds in machine learning, personalization, application development and infrastructure engineering delivers this mission through news feeds, social conversations and personalization. My team was responsible for driving user engagement and revenue growth through highly scalable infrastructure and personalization technology. Using this technology, we were able to power the optimization of feeds across more than 20 of LinkedIn's most important products including: LinkedIn's Homepage, Mobile Application, Recruiter, Sales Solution and LinkedIn Today.As a hands on engineering leader I was involved in all aspects of product development including: algorithm design, AB testing, highly scalable architecture and new product design.

Outcome: Successful exit. Acquired by Walmart. https://techcrunch.com/2017/06/16/walmart-to-buy-bonobos-for-310m-in-its-bigger-push-into-fashion-retail/Led the Personalization and Merchandising team that was responsible for building Bonobos' very first apparel recommendations algorithms and systems that are used to optimize the customer experience and increase revenue. Additionally, we own and operate an A/B testing platform that is used to measure our success.Major Accomplishments:• Designed a new real time product and category ranking algorithm that was used to optimize the order of products and categories that were presented to users in order to increase conversion rate and revenue.• Developed the companies first A/B testing platform that is used to measure the business effectiveness of software changes that are pushed to production.• Assisted other teams across the company (technology, marketing, business) to design and instrument valid and efficient A/B tests.• Co-designed many key architectural components including: user identity, logging and monitoring. • Mentored engineers from across the organization.• Communicated directly with VC's during fund raising.• Helped drive recruiting efforts across all the technology focused lanes.Key Technologies: Service Oriented Architecture (SOA), Java, SQL, MongoDB, AWS, EC2, R, Splunk.

Led the Applied Personalization Algorithms team. We are responsible for designing and delivering personalization algorithms, systems and movie recommendations to 24+ million Netflix customers. Our goal is simple: Find and recommend movies that users will both watch and love.Major projects:•A leader on Netflix's Cloud Architecture team which redesigned all of the major personalized merchandising systems to run as a large-scale distributed application running in Amazon's cloud (EC2/AWS).• Defined and evangelized Netflix’s current client/service interaction model allowing teams to develop and launch software independently without being tightly coupled. This allowed for faster software iteration without increasing risk.• Developed and launched Netflix’s first customer facing service in the cloud.Responsible for some of Netflix's most personalized and well known features including: "Top Picks", Personalized Recommendations, and "More Like This"• Designed and deployed a new pluggable offline recommender system that allows innovation engineers to quickly launch new recommenders into production to test new prototypes. After launching this system we were able to test ~50 such prototypes at scale without impacting other production systems.• Re-designed the recommendations engine to work in Amazon's AWS system and to scale horizontally.• Developed and refined many new algorithms including post play recommendations, behavioral similarities, movie/TV recommendations, email recommendations and user to user similarity.• Deployed and scaled our systems to work in international locales including: US, Canada and 43 countries in Latin America.• Successfully moved the team from black box QA testing to almost fully automated testing.• Designed a logging and feedback system that allows our algorithms to observe and react to changes in customer behavior.

Leadership Experience• Successfully led a team of 4 engineers to build Amazon’s entire ‘Amazon Personalization Networks’ (APN) system. This system was delivered on time, on budget and without cutting any features.• Successfully launched two additional releases of the APN product targeting new customer segments.• Responsible for writing all technical and functional specifications for APN.•Owned both technical and business sides of the APN product.•Worked with external teams, including overseas development centers, in order to launch key components critical to the success of the product.•Mentored junior engineers on the team and throughout the company.•Directly lead the recruiting efforts for the team and Amazon in general.Software Development Experience•Filed 8 patents relating to personalization and personalized recommendations. •Designed and developed product recommendation and personalization systems and algorithms.• Developed a Natural Language Processing based data mining system that enables Amazon to perform automatic item-to-item comparisons to provide highly relevant product recommendations to customers. • Designed the complete ‘Amazon Personalization Networks’ (APN) system. This system was used to build a network of Associates websites that contributed customer behavior data. This data is used to target Amazon products, 3rd party products and content to website visitors. This allows Amazon to place products on display Ads on over 500,000 3rd party websites and drove an order of magnitude increase in Ad performance.• Re-engineered the recommendations engine to reduce overall system latency by 40%.