Qian Liu
Data AnalystSenior Applied Scientist @ Microsoft AI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Senior Applied Scientist @ Microsoft AI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Skilled applied scientist working for Microsoft. Extensive working experience in production machine learning systems, and software development. Expert programmer with data mining, software development, computational statistics, distributed systems, and parallel computing skillset. Strong analytical and probabilistic reasoning skills built on a foundation of computer science and statistical academic background.
Washington University in St. Louis
Master’s Degree, Engineering Data analysis and Statistics
January 1, 2015 – January 1, 2017
Washington University in St. Louis
Master's degree, Electrical Engineering
January 1, 2015 – January 1, 2017
Beijing University of Posts and Telecommunications
Bachelor of Engineering - BE, Telecommunications Engineering with Management
January 1, 2011 – January 1, 2015
Microsoft AI
Senior Applied Scientist
April 1, 2024 – Present
Suzhou, Jiangsu, China
Ant Group
Machine Learning Algorithm Expert
September 1, 2021 – April 1, 2024
Shanghai, China
DiDi
Senior Machine Learning Engineer
April 1, 2021 – September 1, 2021
Beijing, China
Allianz Partners
Senior Machine Learning Engineer, Allianz Partners USA
August 1, 2019 – April 1, 2021
Richmond, Virginia, United States
Allianz Partners
Machine Learning Engineer, Fusion Team
June 1, 2018 – August 1, 2019
Richmond, Virginia, United States
Kavout Corporation
Machine Learning Engineer
August 1, 2017 – June 1, 2018
Bellevue, Washington, United States
Washington University in St. Louis
Research Assistant in Systems Neuroscience Lab
June 1, 2016 – February 1, 2017
St Louis, Missouri, United States
Beijing University of Posts and Telecommunications
Research Assistant in Network Information Centre
July 1, 2014 – June 1, 2015
Beijing City, China
Udacity Self-driving Car Nanodegree Projects
March 1, 2017 – July 1, 2017
- Implemented an advanced lane-finding algorithm using computer vision techniques and OpenCV-python. - Built a convolutional neural network(CNN) model to classify German traffic signs using TensorFlow and achieved 98.8% accuracy. - Trained a CNN to drive a vehicle autonomously on multiple tracks in a simulation environment by learning to predict steering angles from human demonstration data. - Implemented a vehicle detection algorithm using histogram of oriented gradients(HOG) and SVM and achieved 99% accuracy.
Real-time Image Style Transform Web App
March 1, 2017 – Present
- Implement a CNN model using instance normalization and VGG19 as feature extractor to transform a high-resolution image into the artistic style in real time. Trained the model with 12GB images on AWS with p2.xlarge GPU. - Implemented a style transformation service on Google Compute Engine with pre-trained convolutional neural network model encased using Docker, Python, Nginx, Supervisor, Gunicorn. - Implemented web service on Google App Engine using Python (Flask), JSON, HTML, CSS, Javascript and Bootstrap
Image Recognition Web App
February 1, 2017 – July 1, 2017
- Implemented image recognition service on Google Compute Engine with Keras InceptionV3 image recognition model encased using Docker, Python, Nginx, Supervisor, Gunicorn. - Implemented database service on Google Firebase with public access to store image and JSON data. - Implemented web service on Google App Engine using Python (Flask), JSON, HTML, CSS, Javascript and Bootstrap
Netflix Movie Recommendation System Based on Hadoop
January 1, 2016 – May 1, 2016
- Extracted 30000 users' movie ratings from Netflix raw data with Pig. - Implemented a collaborative filtering algorithm using Hadoop on Amazon EMR to predict movie ratings. - Achieved a high accuracy and ranked 5th of 83 teams in final competition.
Algorithms and Data Structures
September 1, 2015 – December 1, 2015
- Implemented the divide-and-conquer algorithm to find the closest pair of points in the input. - Implemented hashing as part of a tool for comparing genomic DNA sequences. - Implemented skip list as a database indexing strategy to achieve quick queries. - Implemented Dijkstra’s algorithm to find the shortest paths to minimize total travel time.
Pacman artificial intelligence project
September 1, 2015 – December 1, 2015
- Dynamic programming. Application of Depth First Search, Breadth First Search, Uniform Cost Search, Greedy Search, A* Search. - Dynamic programming. Solving Constrain Satisfaction Problem. Using improved Backtracking Search(forward checking applied) and Minimax Search(Alpha-Beta Pruning applied). - Dynamic programming. Solving Markov Decision Processes with reinforcement learning. - Dynamic programming. Application of Hidden Markov Models and particle filter. - Finished the project without a teammate and outperformed 90% of other teams in the final competition.
Test Suite Development for netconf Protocol Conformance Testing
September 1, 2014 – June 1, 2015
- Tested the RPC layer of the protocol and provide a mechanism to implement the Conformance testing of the NETCONF. - Designed the test suites using TTCN3 (The Testing and Test Control Notation Version 3) language and build the test system using the TTworkbench and Java. - Designed the test flow in detail. By analyzing the test output, proves the whole test to be feasible and serviceable.
Introduction to Genomic Technologies
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong academic background and diverse project experience, including personal projects and roles in large tech companies (Microsoft, Ant Group, DiDi). The breadth of projects, from image recognition to financial recommendation systems, indicates a versatile and curious individual. The target role of 'Data Analyst' aligns well with the candidate's demonstrated skills in data processing, machine learning, and statistical analysis. The candidate's experience in risk management and recommendation engines suggests an ability to apply data analysis in business-critical contexts. However, the resume does not provide explicit details on collaboration styles or cultural preferences, making a deeper cultural fit assessment challenging.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and a results-oriented approach, often outperforming peers in competitions. Experience in diverse project types (web apps, self-driving cars, recommendation systems) suggests adaptability. However, without specific psychometric test results or interview data, a detailed assessment of soft skills like teamwork, leadership, or stress handling is not possible.