Dec 2021 – Sep 2022 · 9 mos · Greater New York City Area

• I ran a growth-y data science team @ Citizen. We supported the following functions: product, marketing, expansion, policy, and operations. In addition to supporting other functions, my team directly ran 5+ weekly growth and revenue-increasing experiments (pricing tests, onboarding experiments, feature launches, etc.)
• I 2xed the team size to 6 directs. My team helped set many OKRs for the entire company but directly owned several critical top-line growth and revenue OKRs.
• Few wins from my team
• 3xed CTR, improved user retention by 5% abs
• Ran 20+ onboarding experiments that yielded $2M+ in incremental ARR
• Saved 350k / year in infra costs
• Kick-started ML personalization efforts to improve user retention (est. impact: 5-10% increase in retention)

May 2019 – Dec 2021 · 2 yrs 7 mos · Greater New York City Area

• I joined Citizen as its 2nd DS. I helped 4X MAU, drove strategic discussions with board members around pricing + fundraising, and sharpened the team's product/growth thinking through a metrics-first lens.
• Highlights
• Made capital allocation decisions to fund or de-fund entire teams by comparing growth vs. revenue trade-offs
• Helped set OKRs for the entire company for multiple quarters
• Created a metrics architecture that allowed each person in the company to connect their work to concrete business impact (this drove employee engagement and ownership)
• Pushed for COVID coverage in March of 2020 within the product; increased DAU by 20% and lead to millions of users being more safe and informed
• Supported fundraising efforts by crafting our growth story from metrics pov (acquisition, activation, retention, virality)

Dec 2018 – Apr 2019 · 4 mos

• Built machine learning-based systems that drove millions in revenue, increased gross margins, and optimized efficiency by 10x across the org. Worked with the C-Suite to lead high-performance teams and execute critical, high-profile business initiatives.
• Notable wins:
• 1) Saved $2M / year and drove⭡$XXXM / year in additional revenue (without an increase in sales spend)
• 2) Simplified supply chain so the factory can build +10% more cars + build the right cars that people want by forecasting demand (⭡Working capital + Gross margins)
• 3) Improved delivery experience by giving owners a better ETA of when their cars will be delivered (Better delivery experience ⭢ More referrals ⭢ More revenue with lower CAC)

Oct 2016 – Dec 2018 · 2 yrs 2 mos

• Machine learning @ Growth; see⭡for the wins

Sep 2015 – Oct 2016 · 1 yr 1 mo · Waterloo, Ontario

• Content based techniques for medical image retrieval. Techniques used: stacked autoencoders (deep learning), LSH / k-NN, various classifiers (SVM, random forests, etc.) and domain heuristics.
• Lead author in published paper: https://arxiv.org/pdf/1610.00320.pdf

Sep 2014 – Sep 2016 · 2 yrs · Santa Clara

• Domain modeling, API design, reactive backend services (user management & auth, time series, biz logic), devOps (scala, docker, kafka, akka, elasticsearch, mongo)
• Co-­developed datacenter simulator (akka). Benefits: more robust, exhaustive & cheaper testing; also what­if scenarios
• Co-­developed power optimization algorithms (core IP) which can reduce the total cost of ownership of datacenters by up to 50%; developed L1 regression models to calibrate current/voltage sensors

Dec 2013 – Sep 2014 · 9 mos · San Mateo, California

• Lead Reflektion’s search and relevance team of four engineers (reported directly to Chief Data Scientist); deployed new products to customers such as Converse.com
• Designed and implemented Reflektion’s search product (API, backend services); increased customer engagement by 70% and increased conversion for our customers by 26% (python, pyramid, elasticsearch, pig)
• Developed new ranking techniques combining pCTR (logistic regression)
• and domain-specific heuristics
• Designed a multivariate testing framework using a multi-armed bandit approach to optimize the CTR and overall engagement across an arbitrary number of parameters

May 2013 – Sep 2013 · 4 mos · San Francisco Bay Area

• Shipped core application features using Rails; A/B testing; featured in CNN & MSN Money.
• Marketing strategy and customer development with co-founders (Bo & Jon)