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🔜 GDC | CEO/Founder at Sapient AI
Technology leader experienced with delivering high-impact data and ML products and solutions across multiple industries: from cloud technology (e.g., AWS), quantitative finance & asset management (e.g., Goldman Sachs), healthcare and life sciences (e.g. Janssen), sports (e.g., NFL, Formula 1, NHL, Bundesliga) to media & entertainment (e.g. Netflix). Technology products and solutions featured in popular media outlets including VentureBeat and ESPN; and leading scientific journals including Nature Scientific Reports. Passionate about helping people unlock the power of data, making ML accessible to all, and making the next generation of technology leaders more diverse.
Columbia Engineering
Master of Science (MS), Computer Science
January 1, 2017 – January 1, 2018
W. P. Carey School of Business – Arizona State University
Bachelor of Science: Finance | Statistics, magna cum laude
January 1, 2006 – January 1, 2010
NYU Tandon School of Engineering
Sapient - 2025 Cohort – NYU Game Design Future Lab Accelerator
May 1, 2025 – Present
New York, New York, United States · Hybrid
Sapient AI
Chief Executive Officer
June 1, 2024 – Present
Santa Monica, California, United States · On-site
Netflix
Senior Engineering Manager
May 1, 2023 – April 1, 2024
Netflix
Engineering Manager, Content Knowledge Graph
July 1, 2021 – May 1, 2023
Amazon Web Services (AWS)
Manager, Applied Science - Machine Learning Solutions Lab
June 1, 2020 – June 1, 2021
New York, New York, United States
Amazon Web Services (AWS)
Applied Scientist, Machine Learning Solutions Lab
October 1, 2019 – January 1, 2020
New York City Metropolitan Area
Yewno, Inc.
VP, Head of Product Development & Applied Research
June 1, 2019 – October 1, 2019
Yewno, Inc.
Data Science Manager
March 1, 2019 – June 1, 2019
Yewno, Inc.
Data Scientist
June 1, 2018 – March 1, 2019
Columbia University Medical Center
Research Assistant
May 1, 2017 – September 1, 2017
Department of Neuroscience - Losonczy Lab
Columbia Engineering
M.S. Candidate Computer Science (Machine Learning)
January 1, 2017 – June 1, 2018
New York
Traverse Technologies
CEO + Co-Founder
March 1, 2015 – February 1, 2017
New York City Metropolitan Area · On-site
Goldman Sachs
Trader, Agency Mortgage Backed Securities
June 1, 2010 – February 1, 2016
New York City Metropolitan Area
Goldman Sachs
Sales & Trading Summer Analyst
June 1, 2009 – August 1, 2009
New York City Metropolitan Area
J.P. Morgan
Sales & Trading Summer Analyst
June 1, 2008 – August 1, 2008
New York City Metropolitan Area
Cpptext: Multithreaded Text Preprocessing Library
May 1, 2018 – Present
Cpptext incorporates OpenMP compiler-level loop parallelization with Boost Filesystem/Program Options libraries to create a fast, multithreaded C++ text processing library that’s up to 5 times faster than traditional preprocessing methods
Particle Thompson Sampling for Online Recommendation
May 1, 2018 – Present
Combined several statistical techniques (collaborative filtering, Thompson sampling/reinforcement learning, particle filtering) to build Bayesian online recommendation model following Efficient Thompson Sampling for Online Matrix-Factorization Recommendation (Kawale, etl al. 2015).Explored the cold-start problem and showed quantitatively and qualitatively that algorithm is similar or better at learning user preferences given limited consumption history
Deep Learning based Movie Recommender System
January 1, 2017 – Present
Worked with team to implemented recently published and acclaimed Deep Learning paper Joint Deep Modeling of Users and Items Using Reviews for Recommendation using the open-sourced Amazon Instant Video dataset in TensorFlow/Keras with GloVe embedding. In addition to original CNN architecture, implemented Long Short Term Memory (LSTM) and Gate Recurrent Unit (GRU) parallel architectures to jointly learn user and movie feature mappings; trained using GPU on Google Cloud
NLP - Abstractive Summarization with Encoder-Decoder Attention Network
January 1, 2017 – Present
Implemented an bi-directional LSTM encoder with Attention decoder neural network in Keras with Python for abstractive summary generation from news article input data; ROUGE-1 scores of 21% and ROUGE-2 scores of 5%
Predicting NYC Taxi Trip Duration with Gaussian Processes in Edward
January 1, 2017 – Present
Worked with team to build a Gaussian Process generative model with the Edward probabilistic programming framework and TensorFlow in Python to predict NYC taxi trip duration. Used ~1.8 million trips from the TLC’s open-sourced 2016 NYC Yellow Cab data set
Algorithms (Part 1)
Coursera
June 24, 2026 – Present
Database Foundations
Treehouse
June 24, 2026 – Present
Deep Learning in Python
DataCamp
June 24, 2026 – Present
Python Data Structures
Coursera
June 24, 2026 – Present
R Programming
Coursera
June 24, 2026 – Present
Statistical Inference
Coursera
June 24, 2026 – Present
An Introduction to Interactive Programming in Python (Part 1)
Coursera
June 24, 2026 – Present
Java Data Structures
Treehouse
June 24, 2026 – Present
Supervised Learning with scikit-learn
DataCamp
June 24, 2026 – Present
Object Orient Programming in Java
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory shows a strong entrepreneurial spirit (CEO of Sapient AI, Traverse Technologies) and a history of working in innovative, fast-paced environments (Netflix, AWS, Yewno). Their project diversity, ranging from multithreaded C++ libraries to deep learning recommenders and NLP, demonstrates a broad technical curiosity and adaptability. The target role is 'Data Analyst', which seems to be a significant step down from their recent senior leadership and applied science roles. While they possess strong analytical skills, their extensive experience in leading ML/AI initiatives and managing teams might indicate an overqualification or a mismatch in career aspirations for a pure Data Analyst role. Their background in finance and statistics, combined with their later pivot to ML/AI, shows a blend of quantitative and technical skills, which could be beneficial in a data-centric role, but the 'analyst' designation might not fully leverage their leadership and advanced ML expertise.
Soft Skills & Operational Fit
The candidate's experience as a CEO and various leadership roles (Senior Engineering Manager, Manager, Applied Science, VP, Head of Product Development) suggests strong leadership, strategic thinking, and project management skills. Their work at Netflix and AWS indicates an ability to operate in high-performance, data-driven environments. The description of building 'dream teams' and 'leading incredible orgs' points to strong team collaboration and mentorship capabilities. However, without psychometric test results, a definitive assessment of stress handling or specific work attitude is not possible.