Jeremy Gray
About
Jeremy works in the following industries: "Internet", "Publishing", "Higher Education", "Research", "Maritime", "Information Technology & Services", "Non-profit Organization Management", "Retail", and "Media Production". Jeremy is currently Senior Data Scientist at Twitter. In Jeremy's previous role as a Chief Data Scientist, Sophi at Globe and Mail, Jeremy worked in until Oct 2021. Prior to joining Globe and Mail, Jeremy was a Manager, Data Science at Globe and Mail and held the position of Manager, Data Science at Toronto, Canada Area. Prior to that, Jeremy was a Senior Data Scientist at Globe and Mail, based in Toronto, Canada Area from Sep 2018 to May 2019. Jeremy started working as Lead Educator, Data Science at BrainStation in Toronto, Canada Area in Mar 2018. From May 2016 to Mar 2018, Jeremy was Senior Data Scientist, R&D at Precima, based in Toronto, Canada Area. Prior to that, Jeremy was a Contract Python Instructor to Data Science Team at Precima, based in Toronto, Canada Area from Feb 2016 to May 2016. Jeremy started working as Data Science Consultant at Corporate Knights Inc. in Toronto in Sep 2015.
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Jeremy Gray's current jobs
Jeremy Gray's past jobs
Developed a model engine to carry out a wide range of tasks related to retail optimization, pricing, assortment and marketing. Moved existing solutions into Amazon Web Services, and developed new ones. Bayesian statistics, machine learning, linear and logisitic regression and optimization. Current tech stack: Python, AWS, EC2, S3, Redshift, R, Flask, Redis, SQL, Celery.
Developing, designing and presenting a 10 week course in Python programming for data science to the 50 people in the Precima R&D and Applied Statistics teams.
Refactoring, bug fixing and completing an R Shiny Web App to compare investment portfolios with and without polluting investments. Available online at decarbonizer.co.
Lectured 0.5 of a course, Biostatistics, for second year EEB majors. Course content involved t-tests, ANOVA, linear and multiple linear regression, logistic regression, ANCOVA and experimental design. Developed new R labs and statistical lectures. A total of 12 hours lecturing, and 30 hours leading computational labs.
66% Computational - Developing two major R packages for high throughput statistical analysis of experimental data, and agent based modelling of evolution. Routinely carrying out statistical tasks (ANOVA, linear and non-linear regression, survival analysis, image analysis). Working with gigabyte scale bioinformatic data - creating, automating and maintaining pipelines, carrying out evolutionary analysis of genomic data. 33% Wet Lab - Carrying out a wide range of contemporary molecular biological techniques.
Responsible for cleaning and caring for oiled wildlife
“Horizontal Gene Transfer in the New Zealand Environment” Environmental microbiology, soil science, molecular biology, statistics and data management.