Siyong Ma
Data AnalystSenior ML Research Scientist at Apple
Apple
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 ML Research Scientist at Apple
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Experienced machine learning engineer and data scientist interested in solving complex problems through iterations. Walk the talk, let the data speak and make things happen.
MITx Courses
MicroMasters, Statistics and Data Science
January 1, 2019 – January 1, 2020
Northeastern University
Master of Science (MS), Computer Science
January 1, 2013 – January 1, 2015
East China Normal University
Bachelor of Engineering (BEng), Software Engineering
January 1, 2009 – January 1, 2013
Apple
Senior ML Research Scientist
February 1, 2021 – Present
Chewy
Data Scientist
January 1, 2019 – February 1, 2021
Greater Boston Area
StubHub
Software Engineer 3, Machine Learning
March 1, 2017 – January 1, 2019
Greater Boston
StubHub
Software Engineer 2, Machine Learning
March 1, 2016 – March 1, 2017
Greater Boston
Northeastern University
Teaching Assistant
September 1, 2015 – December 1, 2015
Boston, MA
StubHub
Graduate Student Intern, Recommender System
July 1, 2015 – September 1, 2015
Boston, Massachusetts
EMC
Graduate Student Intern, Docker and Virtualization
January 1, 2015 – June 1, 2015
Hopkinton, MA
Northeastern University
Teaching Assistant
September 1, 2014 – December 1, 2014
Boston, MA
Dianping
Software Developer Intern
June 1, 2012 – November 1, 2012
DS.CFx: Capstone Exam for Statistics and Data Science
MITx Courses
June 24, 2026 – Present
MITx 18.6501x: Fundamentals of Statistics
MITx Courses
June 24, 2026 – Present
MITx 6.431x: Probability - The Science of Uncertainty and Data
MITx Courses
June 24, 2026 – Present
MITx 14.310x: Data Analysis in Social Science
MITx Courses
June 24, 2026 – Present
BerkeleyX CS190.1x Scalable Machine Learning
edX
June 24, 2026 – Present
MITx 6.041x: Introduction to Probability - The Science of Uncertainty
MITx Courses
June 24, 2026 – Present
CaltechX CS1156x: Learning From Data
CaltechX | California Institute of Technology
June 24, 2026 – Present
BerkeleyX CS188.1x: Artificial Intelligence
edX
June 24, 2026 – Present
MITx 6.86x: Machine Learning with Python-From Linear Models to Deep Learning
MITx Courses
June 24, 2026 – Present
BerkeleyX CS100.1x Introduction to Big Data with Apache Spark
edX
June 24, 2026 – Present
MITx 6.419x: Data Analysis: Statistical Modeling and Computation in Applications
MITx Courses
June 24, 2026 – Present
MicroMasters in Statistics and Data Science
MITx Courses
June 24, 2026 – Present
MITx 6.00.1x: Introduction to Computer Science and Programming Using Python
MITx Courses
June 24, 2026 – Present
MITx 6.00.2x: Introduction to Computational Thinking and Data Science
MITx Courses
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has worked in diverse environments, from large tech companies like Apple to e-commerce platforms like Chewy and StubHub. Their continuous pursuit of education (MicroMasters from MITx) and certifications demonstrates a strong drive for learning and self-improvement. The roles held, particularly in ML research and data science, align with a data-driven culture. However, the target role is 'Data Analyst' while the experience is heavily skewed towards 'ML Research Scientist' and 'Data Scientist', which might indicate a potential mismatch in day-to-day responsibilities and expectations, as a Data Analyst role typically focuses more on reporting, dashboards, and ad-hoc analysis rather than model development and deployment. This could be a strength if the role requires advanced analytical capabilities, but a potential misalignment if the role is more operational data analysis.
Soft Skills & Operational Fit
The candidate's experience as a Teaching Assistant and mentor suggests good communication and collaboration skills. Their work on full-stack data science projects indicates an ability to manage projects end-to-end and contribute across different operational stages. The focus on building automated evaluation frameworks points to a methodical and quality-oriented approach.