Trying to make the internet a better place, one data-driven feature at a time.- Established a globally distributed data & insights team to support the YouTube Monetization Policy, Engineering, and Product teams- Developed a personal portfolio focused on leveraging machine learning techniques to generate deep insights and launch data products to improve the quality and efficiency of human reviews of YouTube content- Led analytics and data science initiatives to create an advertiser-facing ‘industry monetization badness rate’ KPI for accreditation by the Media Rating Council (MRC), helping YouTube become the first content platform to prove it has over 99% brand safe ad impressions

- Built and led the product data science group (data scientists + data engineers + data analysts) to support development of new creator, commerce, social, and moderation features with both data products and in-depth behavioral research- Modeled the Twitch ecosystem to understand user acquisition, creator / viewer lifecycles, user retention, watch time growth, content mechanics, and customer segmentation- Proposed, resourced, and led technical initiatives to develop AutoMod, an industry-leading adaptive text moderation system for Twitch chat- Provided direct decision support to Twitch and Amazon executive leadership teams and led execution of special projects related to growth strategy, metrics, and acquisitions

- Led development of data products and experiments for community and social features across Twitch.- Built a team of data scientists and analysts to support product development and growth.

See above - Twitch was acquired by Amazon just before I joined, so I was technically an Amazon employee.

* Humin acquired by Tinder in 2016 *- Joined as the 7th member of technical staff and built out a team of data scientists and data engineers to address everything from tracking and infrastructure to data products and unsupervised learning models.- Developed A/B testing protocols and infrastructure to test behavioral response to new features in a network setting (graph cluster randomization, etc.).- Built general analytics, monitoring, and self-serve reporting platforms to inform business development and product decisions (engagement, retention, virality, etc.).- Utilized social network analysis techniques to model personal and professional relationship networks to inform intuitive communication interfaces.Data science leader with deep experience designing and executing quantitative and qualitative analysis of user behavior: - statistical modeling and factor analysis- A/B testing protocols and data interpretation- data visualization / communication of results in presentations, white papers, etc.- machine learning techniques (GLMs, neural networks, boosted trees, GANs, etc.)- survey design, user testing, and targeted / expert focus groups- mobile and web development / scripting for collection and analysis of user interaction data- intellectual property strategy and patent draftingSpecific Tools & Techniques:Utilization of novel data processing tools and languages for large scale data analytics (Python, R, SQL, Spark / PySpark, TensorFlow, PyTorch, Postgres, Airflow, Storm-Kafka, Hive, AWS EC2 / EMR / Lambda / Kinesis / Redshift / DynamoDB, Tableau, Gephi, etc.).Skilled at implementation of accurate statistical models / machine learning methodologies (logistic regression, factor analysis, decision trees [gradient boosted, RF ensemble], clustering [k-means], Bayesian [logistic and naive], Hidden Markov, Linear Discriminant Analysis, Natural Language Processing, Support Vector Machines, Conjoint Analysis, Significance Testing, etc.)

Consultant to high-tech hardware and software firms. Leveraging data mining and machine learning techniques to gather insights from internal and external unstructured data. Example engagements:- Pricing for Cloud Infrasctructure Products - Worked to reverse margin erosion by operationalizing real-time analytics to achieve optimal pricing specific to product categories and individual customer profiles- Resource Allocation for Networking Hardware Sales - Empowered the sales management organization to make data-driven resource allocation decisions by linking disparate sources of unstructured information to generate novel insights into the sales process

Consultant for mid-size to large high-tech, biotech, and biomedical companies. Managing independent workstreams, presenting to clients, and directing ex-US research teams. Example engagements:- Partner Program and Sales Data Analytics – regression-based ROI and customer targeting- Marketing and Pricing Strategy – price sensitivity, quant market research, and financial forecasting- NPS Factor Analysis - NLP to identify key drivers of consumer satisfaction for a large online retailer- Acquisitions Diligence and Valuation – Market research, price sensitivity, and insurance analysis to project revenue potential of emerging therapies

Supported SPC leadership via development of flexible and accurate forecasting models as well as analyzing physician level data to identify potential contracting opportunities.

Conducted research portfolio planning and independent work on novel materials for organic and inorganic photovoltaics. Advisors: Prof. Mark Hersam and Prof. Tobin MarksNational Science Foundation Graduate Research Fellow

Led new research initiatives into bio-inspired self-assembly of organic chromophores for energy conversion. Advisor: Prof. Samuel I. StuppNational Defense Science and Engineering Graduate Fellow

Brokered the acquisition of the yearbook from Cornell University and increased annual profitability by $160k

P&L responsibility for a shipping and storage company with 80+ employees, $400k+ in revenues, and >$1.5M in stored assets and inventory

Developed novel fabrication methods for organic light emitting diodes (OLEDs) in the Malliaras research group.