Lance Mack Email & Phone Number

New York, Ny, Us

Cupertino, California, Us

New York, Ny, Us

* Product Manager leading renewal revenue monetization & membership efforts.* Lead Data Scientist for business model innovation, long term experience, goal expansion, and winbacks.

San Francisco, California, Us

* Algorithmic Pricing: Co-developed a structural algorithm in a team of 2 to algorithmically set prices across the US to improve trips by 2%. Pioneered a temporal-spatial clustering algorithm to determine efficient pricing zones within cities. Designed and launched prices for new low-cost rideshare products to 100 LatAm cities. Recommended for promotion to Senior Applied Scientist prior to departure.* Lead data scientist for Marketplace Incentives. Built zero-to-one causal models, designed synthetic control experiments, conducted robustness analyses, and coordinated production-ready efforts for the rider marketplace incentive allocation algorithm, which manages Uber's weekly incentive allocation for riders and drivers. Improved gross bookings by 2%+ in the US & LatAm. Coordinated with Operations teams across the world to integrate algorithmic decision-making into weekly incentive planning process. Led the onboarding of 5 engineers to deploy our model to production and build the next iteration of ML-based causal models. Improved temporal-spatial rider demand prediction by 20%. * Lead data scientist for Winbacks within Driver Growth org. Developed a causal machine learning model to optimize the targeting of our winback phone outreach program (Increased ROI by 100%). Led experimentation design & analysis, foundational winbacks research, KPI setting, and weekly business updates.

New York, Ny, Us

•Engineered budget pacing algorithm to spend a campaign's budget to optimize cost action and rate optimization goals •Designed a reinforcement learning gym to simulate a real time bidding environment in order to compare, test, and iterate budget pacing algorithms•Developed a Bayesian budget allocation algorithm to optimize paid-social campaigns•Designed validation pipeline and experiments to test campaign-level budget allocation•Engineered impression-level feature set for predicting user-conversions

•Analyzed intracranial electroencephalographic (iEEG) data and applied Bayesian statistics to model memory fatigue•Used supervised classification algorithms to identify (non)recalled items in memory tests based on iEEG data

•Aggregated customer data tables using SQL to yield a more complete one-to-one view of each user•Solved ad hoc data problems for several public companies•Created databases and manipulated pre-existing datasets for our corporate partners

Atlanta, Georgia, Us

•Engineered the #1 most predictive meta-feature for the core underwriting model•Increased out-of-sample performance on the core underwriting model by 11% •Reduced overfitting on the core underwriting model by 35%•Designed and developed native data pipeline to support multi-classification machine learning algorithms•Applied natural language processing (NLP) and multi-classification algorithms to impute missing data

New York, Ny, Us

•Built and managed the company’s Google Analytics account •Answered ad hoc data questions and designed company-wide analytics reports for executives at all levels•Researched top SEO practices and executed a three-stage SEO strategy for the website•Assisted in modeling a lead scoring system in Eloqua to automate the transfer of qualified leads to sales•Performed analytics for our Salesforce lead nurture system

Dallas, Texas, Us

•Constructed lists of influencers in lead generation and digital marketing to identify prospective customers•Researched 500 top US tech conferences to compile data about influential speakers