Job Posting: Blue Jays Baseball Research Analyst

Position: Analyst, Baseball Research

The Toronto Blue Jays are seeking a highly motivated and creative Baseball Research Analyst to conduct baseball research and contribute to ongoing departmental research. Additionally, the analyst will create tools that incorporate data into the decision making process, as well as learn how decisions are made in all areas of Baseball Operations and work to improve those processes.

Please note that this is a full-time position.

Responsibilities and Duties:

  • Design, test, implement and maintain advanced baseball metrics and predictive models using statistical techniques in order to contribute to strategy and decisions across all departments within Baseball Operations.
  • Conduct empirical research related to baseball strategy and player evaluation, with an understanding of how findings would apply to better decision making and increased operational effectiveness.
  • Collaborate with the front office, coaches and scouts to develop best practices for analyzing and displaying baseball data, including the creation of reports, charts, graphics, and other tools to deliver information to end users. Results of this work should help those within the organization better understand, consider and apply the use of information and data to their decisions and operation on a daily basis.
  • Collaborate with members of the Research and Development Department to provide constructive feedback on their projects.
  • Complete ad-hoc database queries and analysis as dictated by circumstances.
  • Recommend new data sources for purchase and/or new techniques to gather proprietary data.
  • Work to integrate new information into existing Baseball Operations processes and infrastructure.

Experiences and Job Requirements:

  • Passion for baseball and excellent reasoning, problem-solving, creative thinking, and communication skills.
  • Demonstrated ability to successfully design and execute rigorous quantitative research projects.
  • Published quantitative research about baseball (either online or print), related experience with sports teams or facilities, and/or open source code to review is a plus.
  • Strong understanding of current baseball research.
  • Proficiency with R, Python or other similar mathematical language is required.
  • Proficiency with SQL and relational databases is required.
  • Understanding of Python, Perl, Ruby or other similar scripting language is a plus but not required.
  • Demonstrated experience with machine learning methods, including clustering, random forests, boosting, and neural networks is a plus but not required.
  • Experience with web design is a plus but not required.
  • Strong interpersonal skills to communicate effectively with a wide range of individuals including members of the front office, scouts, and field staff.
  • Ability to read, speak and comprehend English effectively.
  • Ability to work evening, weekend and holiday hours.

To Apply:
Please email a copy of your resume to and answer these 3 prompts in the body of the email. Please limit your answers to no more than one paragraph per question.

  • Describe in detail a time when you used your analytical and research skills to answer a research question, ideally about baseball.
  • What experience of yours do you feel has best prepared you for this opportunity?
  • In addition to FanGraphs, what baseball websites do you read and why?

Meg is the managing editor of FanGraphs and the co-host of Effectively Wild. Prior to joining FanGraphs, her work appeared at Baseball Prospectus, Lookout Landing, and Just A Bit Outside. You can follow her on twitter @megrowler.

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4 years ago

Let’s play a game. How much would this position pay in the non-baseball private sector?

First off, this isn’t an analyst role. With R/Python, SQL, and Ruby, this is data scientist position. Actually having web design experience would make it beyond your standard data scientist.

So we’re talking CA$90,000.

Hopefully this $1.3 billion company can afford to pay that.

(Salary source: Glassdoor “Toronto Data Scientist”)

4 years ago
Reply to  Trev

“Demonstrated experience with machine learning methods, including clustering, random forests, boosting, and neural networks is a plus but not required.”

This makes it data scientist. Knowing those languages doesn’t – many, many analysts in Silicon Valley know R, Python, SQL and more yet don’t do data science. The science part is the modeling.

4 years ago
Reply to  tb.25

Someone who builds models using Pyhton and SQL here, I do not consider myself a data scientist

4 years ago
Reply to  Trev

I also hope the Jays are paying a competitive wage! I wonder if being part of large-company Rogers would limit how low they could go for desperate baseball nerds. They Jays posted something almost identical about 3 years ago. I applied then and made it through a couple of assignments before I was told thanks but no thanks. It was a fun and interesting experience, if nothing else.

Which is to say that I had/have the technical skills to get past screening, but didn’t wow them enough to be hired, so I’m probably a reasonable comparison. I was making about $75k at the time, and make about twice that now.

For what it’s worth, I got the impression that my (total) lack of experience in organized baseball was too much for me to overcome. I’m good with the stats and data but not overwhelmingly so.

Happy applying!