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Senior Staff AI/ML Engineer at Flex
Hello! I'm Fengtao, a seasoned professional with a multifaceted background in data science, machine learning, software development, and finance. With over 8 years of expertise in data-driven technologies, I've successfully navigated the intersections of these diverse fields. Currently serving as the Applied AI ML Lead at JPMorgan Chase, I lead initiatives that harness the power of artificial intelligence to enhance the business processes. My experience spans over 4 years in finance, supplemented by the completion of CFA Level II in 2018, providing a solid foundation for tackling financial complexities. Known for being a quick learner and effective communicator, I excel in business writing and project management. I'm passionate about translating ideas into actionable strategies that deliver tangible results. Let's connect and explore opportunities to collaborate or share insights within the dynamic fields of data science, finance, and beyond!
University of Pittsburgh School of Computing and Information
Master’s Degree, Big Data Analytics specialization in Information Science
January 1, 2015 – January 1, 2017
The Hong Kong University of Science and Technology
Master of Science (MSc), Financial Mathematics and Statistics
January 1, 2011 – January 1, 2012
Fudan University
Bachelor of Science (BSc), Information and Computing science
January 1, 2007 – January 1, 2011
Flex
Senior Staff AI/Machine Learning Engineer
December 1, 2025 – Present
New York, New York, United States · Remote
JPMorgan Chase & Co.
Applied AI ML Lead, Vice President
January 1, 2024 – November 1, 2025
Jersey City, New Jersey, United States · Hybrid
Amazon
Research Scientist
April 1, 2021 – February 1, 2024
Amazon
Data Scientist II
April 1, 2020 – April 1, 2021
Amazon
Data Scientist
April 1, 2019 – April 1, 2020
FactSet
Machine Learning Engineer
September 1, 2017 – November 1, 2018
New York, New York, United States
University of Pittsburgh
Teaching Assistant
January 1, 2017 – April 1, 2017
Pittsburgh, Pennsylvania, United States
Bohai Capital
Finance Executive
August 1, 2012 – December 1, 2014
Beijing, China
All You Need to Know about Healthy Ride in Steel City
December 1, 2016 – Present
1. The goal is to visualize the global and local data according to the data aggregation parameters set by viewers 2. Enabled viewers to control the parameters including time range, time granularity and geographical coordinates 3. Empowered viewers to identify the features and dynamics embedded in the system on their own interests 4. The project was awarded as the most comprehensive and best interaction design project in the showcase gallery Note: You can find the demo at http://picso.org:8889/~classinfovis2016fall/projects/group-11/ and the source codes at https://github.com/woodswift/visualizationFinalProject
Enough Bikes Already for Sharing?
December 1, 2016 – Present
1. The goal is to predict the hourly utilization demand of the system in order to solve the rebalancing problem 2. Incorporated clustering and classification techniques to tackle the imbalanced distribution problem 3. The prediction from the autoregressive model captured the variation of demand in the course of day well 4. The project was awarded as the most technical sound project in the showcase gallery Note: The project is implemented in R, and the source codes are published in GitHub https://github.com/woodswift/Data-Mining-Project-for-Pittsburgh-Bike-Sharing. There are a report paper and presentation slides in the Result folder.
A Study: Deep Learning Algorithms Performance in Geo-Distributed Environment
April 1, 2016 – Present
1. Selected the MXNet library and the MNIST data to evaluate the accuracy and time performance of the algorithm 2. Explored how the bandwidth, latency, and loss rate influenced the performance 3. Investigated how the neural network architecture influenced the performance 4. Studied how the hardware including CPU, memory and hard drive influenced the performance
Online Bookstore Database System
April 1, 2016 – Present
The goal of the project is to implement a database system for an online bookstore. The database system includes all the functions in the online business and daily operations, and there are generally three types of users: customers, clerks, managers and a super administrator. The system offers the following functions to the customers: 1) Register and Login 2) View/update his/her personal information 3) Browse book information 4) Purchase books 5) Check out his/her purchase history The system offers the following functions to the clerks: 1) View/update clerk's information 2) View/update store information 3) Create book record 4) Create inventory record of the store 5) Update inventory record of the store 6) View order list of the store 7) View operation report of the store: including different queries and data aggregations on inventory and sales 8) Query on store order given a specific book 9) Query on store order given a specific customer The system offers the following functions to the managers: 1) View/update manager's information 2) View/update store information 3) View operation report of the region: including different queries and data aggregations on inventory and sales
Titanic: Machine Learning from Disaster
December 1, 2015 – Present
The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. In this challenge, we completed the analysis of what sorts of people were likely to survive, and applied the tools of machine learning to predict which passengers survived the tragedy. We achieved 0.82297 accuracy and ranked in 99th in the leaderboard globally on December 4, 2015.
AWS Certified Cloud Practitioner
Amazon Web Services (AWS)
June 24, 2026 – Present
JPMC – AWS Foundations
QA Ltd
June 24, 2026 – Present
JPMC - Terraform Foundations
QA North America
June 24, 2026 – Present
DeepLearning.AI TensorFlow Developer
Coursera
June 24, 2026 – Present
Sequences, Time Series and Prediction
Coursera
June 24, 2026 – Present
Natural Language Processing in TensorFlow
Coursera
June 24, 2026 – Present
Convolutional Neural Networks in TensorFlow
Coursera
June 24, 2026 – Present
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Coursera
June 24, 2026 – Present
CFA Level II Badge
CFA Institute
June 24, 2026 – Present
Sequence Models
Coursera
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from machine learning competitions to data visualization and database systems, indicates a broad interest and adaptability. Their career progression from Data Scientist to Research Scientist and then to Applied AI ML Lead and Senior Staff AI/ML Engineer shows ambition and a continuous learning mindset. While the target role is 'Data Analyst', the candidate's recent experience is heavily skewed towards advanced AI/ML engineering and research. This suggests a potential overqualification or a mismatch in the desired scope of work for a typical Data Analyst role, which might focus more on reporting, dashboards, and business intelligence rather than model development and deployment. The transition from finance to data science/ML also shows a willingness to pivot and learn new domains.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving skills through their project work and professional experience in identifying and fixing defects. Their experience in hosting research meetings and mentoring new teammates suggests good communication and collaboration skills. The project awards indicate a drive for quality and innovation. However, without psychometric test results, a full assessment of work attitude, stress handling, and team collaboration is not possible.