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Sr. Software Engineer/Finance Engineer at Fannie Mae
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Assessing your cultural and operational fit
• Skilled in Amazon Cloud Services, Kubernetes, Jenkins, databricks • Build Machine Learning Quantitative Trading Decision Platform • 3 years Reinforcement Learning/Machine Learning Experiences • Artificial Intelligence for Robotics(AutoCar), Computer Vision(OpenCV) • C/C++ (8+ years), Linux/UNIX (8+ years), Python (5+ years), Scala(2+ years), CUDA (3+ years) • Platforms and Tools: AWS, Scala/Spark • Data Science Technology: Machine Learning, Data Mining, Big Data Processing, Data Visualization Analytics
Georgia Institute of Technology
Master of Science - MS, Computer Science
January 1, 2017 – January 1, 2018
Brown University
Doctor of Philosophy (Ph.D.), computational physical chemistry
January 1, 2005 – January 1, 2012
Tsinghua University
Master's degree, Inorganic Chemistry
January 1, 2001 – January 1, 2004
Central South University
Bachelor's degree, applied chemistry
January 1, 1994 – January 1, 1998
Fannie Mae
Sr. Software Engineer/Finance Engineer
September 1, 2019 – Present
Herndon, Virginia, United States
Capital One
Sr. Software Engineer/Data Architect III
October 1, 2018 – September 1, 2019
Mclean, Virginia
Fractal Industries, Inc.
Data Analytics Engineer/Machine Learning Quant Developer
June 1, 2017 – October 1, 2018
Reston, VA 20190
Brookly College
Postdoctoral Research Associate
August 1, 2014 – September 1, 2016
Brooklyn
Brown University
Research Assistant
August 1, 2005 – May 1, 2012
Providence, RI
Data and Visual Analytics (R Programming)
January 1, 2017 – April 1, 2017
Data and Visual Analytics (R Programming) Jan. 2017 – May 2017 • Data Visualization and Processing for relationship between the movie descriptors and the box office success of movies for the dataset, movies_merged • Implement logistic regression for classification of digits on an image dataset, MNIST, Changing parameters and training set sizes, and evaluate how the behaviour of the model is affected (Machine Learning)
Machine Learning Trading System (Python Programming)
January 1, 2017 – April 1, 2017
Machine Learning Trading System (Python Programming) Jan. 2017 – May 2017 • Create a market simulator that accepts trading orders and keeps track of a portfolio's value over time and then assesses the performance of that portfolio. • Implement and assess a regression learner using decision trees and random forests. Implement Bootstrap Aggregating as a Python class named BagLearner. • Implement the Q-Learning and Dyna-Q solutions to the reinforcement learning • Develop trading strategies using Technical Analysis, and test them using market simulator. Utilize Random Tree learner to train and test the learning trading algorithm. • Build the strategy learner based on Q-Learner and indicators. Test/debug the strategy learner on specific symbol/time period problems
Data Engineering on AWS: Data Cataloging, Processing, Analytics, and Visualization
June 24, 2026 – Present
Delivering Data-Driven Decisions with AWS: Applying Machine Learning, Data Engineering, and Generative AI
June 24, 2026 – Present
Chartered Financial Analyst (CFA)
CFA Institute
June 24, 2026 – Present
CFA(Chartered Financial Analyst) passed CFA level III
CFA Institute
June 24, 2026 – Present
Artificial Intelligence for Trading Nanodegree
Udacity
June 24, 2026 – Present
Apache Spark Essential Training
Lynda.com
June 24, 2026 – Present
Amazon Web Services Essential Training
June 24, 2026 – Present
NLP with Python for Machine Learning Essential Training
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse academic background (chemistry, computer science) and experience across research and industry (finance, data analytics) suggest adaptability and a broad perspective. The focus on Machine Learning and quantitative finance aligns well with roles requiring innovation and data-driven decision-making. The pursuit of multiple advanced degrees and certifications indicates a strong drive for learning and professional development, which is a positive cultural fit for growth-oriented organizations. The blend of scientific research and practical engineering roles demonstrates a capacity to bridge theoretical knowledge with real-world application.
Soft Skills & Operational Fit
The candidate's project descriptions and work history suggest a strong analytical and problem-solving aptitude. The academic background and research roles imply a capacity for independent work and complex problem-solving. However, without specific behavioral assessment data, it is difficult to fully evaluate soft skills like teamwork, communication style, or stress handling.