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Principal Data Scientist at Microsoft Gray Systems Lab
I am a passionate research scientist wearing many hats. Typically, my role is some combination of machine learning researcher, polyglot developer, data scientist, and mentor. I have a long track record of building high-performance scalable systems for big data and implementing distributed machine learning algorithms. I am a MOOC addict and a fan of hack days and hackathons. I am an active open-source contributor and Apache REEF PMC chair. In my spare time, I enjoy solving ProjectEuler problems in Haskell or training my models on Kaggle datasets. Obligatory alphabet soup: Machine Learning, Deep Learning, Data Science, Natural Language Processing, High Scalability, Distributed Systems, Functional Programming; C++/STL, Java, Python, R, C#, SQL, Pig, Matlab, Unix shell, Erlang, Haskell, Clojure, Scala, Prolog; Hadoop, Spark, PyTorch, TensorFlow, ML.NET, ONNX, NLTK, DRY, RTFM.
Stanford University
Statistics
January 1, 2007 – January 1, 2007
Rutgers University
Mathematics
January 1, 2005 – January 1, 2006
National Technical University of Ukraine 'Kyiv Polytechnic Institute'
MS, Mathematics and Computer Science
January 1, 1989 – January 1, 1995
Microsoft
Principal Data Scientist
April 1, 2022 – Present
Microsoft
Principal Data and Applied Scientist
April 1, 2019 – March 1, 2024
Microsoft
Principal Machine Learning Research Engineer
February 1, 2018 – April 1, 2019
Microsoft
Principal Research Engineer
May 1, 2013 – February 1, 2018
Knobout Inc.
Machine Learning Engineer #4
September 1, 2012 – March 1, 2013
Palo Alto, CA
Spreezio
Software Engineer #3, CTO
March 1, 2010 – April 1, 2011
San Francisco Bay Area
Yahoo! Research
Senior Data Research Engineer
September 1, 2006 – September 1, 2012
Santa Clara, CA
Telcordia Technologies
Senior Software Developer
October 1, 2000 – September 1, 2006
Piscataway, NJ
UNITY-BARS
Senior Software Developer
September 1, 1994 – October 1, 2000
Kiev, Ukraine
National Technical University of Ukraine 'Kyiv Polytechnic Institute'
Teaching Assitant
September 1, 1994 – January 1, 1996
Kiev, Ukraine
MLOS: VM Auto-Tuning and Experimentation Framework
June 1, 2022 – Present
MLOS is an open-source framework for optimization, benchmarking, and experimentation in the cloud.
Functional Programming Principles in Scala
Coursera
June 24, 2026 – Present
Probabilistic Graphical Models
Coursera
June 24, 2026 – Present
Natural Language Processing
Coursera
June 24, 2026 – Present
Algorithms: Design and Analysis, Part 1
Coursera
June 24, 2026 – Present
Discrete Optimization
Coursera
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Introduction to Artificial Intelligence
Udacity
June 24, 2026 – Present
Artificial Intelligence for Robotics
Udacity
June 24, 2026 – Present
Digital Signal Processing
Coursera
June 24, 2026 – Present
Discrete Inference and Learning in Artificial Vision
Coursera
June 24, 2026 – Present
Statistical Learning
Stanford University
June 24, 2026 – Present
Geometry and Group Theory
Coursera
June 24, 2026 – Present
Convex Optimization
Stanford University
June 24, 2026 – Present
CaltechX CS1156x: Learning From Data
edX
June 24, 2026 – Present
Mining Massive Datasets
Coursera
June 24, 2026 – Present
Robotic Vision
QUT (Queensland University of Technology)
June 24, 2026 – Present
BerkeleyX CS100.1x: Big Data Analysis with Apache Spark
edX
June 24, 2026 – Present
BerkeleyX CS190.1x: Scalable Machine Learning
edX
June 24, 2026 – Present
Game Theory
Coursera
June 24, 2026 – Present
BerkeleyX CS105x: Introduction to Apache Spark 1.6
edX
June 24, 2026 – Present
Neural Networks for Machine Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background shows a strong inclination towards research, innovation, and open-source contributions, which aligns well with a culture that values continuous learning and knowledge sharing. Their diverse project experience, from low-level systems to high-level ML applications, indicates adaptability. However, the target role is 'Data Analyst', while the candidate's experience is heavily skewed towards 'Data Scientist' and 'Machine Learning Engineer' at a Principal level. This suggests a potential mismatch in the scope and depth of responsibilities typically associated with a Data Analyst role, which might be more focused on reporting, dashboards, and business insights rather than model building and distributed systems. While the candidate possesses the underlying skills, the direct alignment with a pure 'Data Analyst' role's day-to-day tasks might require clarification.
Soft Skills & Operational Fit
The candidate's extensive experience in research and development roles, coupled with contributions to open-source projects, suggests strong problem-solving abilities, initiative, and a collaborative mindset. Their long tenure at Microsoft in various Principal roles indicates leadership potential and the ability to drive significant technical projects. The descriptions imply a strong work ethic and adaptability to evolving technical landscapes.