Jillian Anderson - Big Data Analyst

Jillian Anderson

Big Data Analyst

Greater Vancouver Metropolitan Area Canada

Join Prog.AI to see contacts

Summary

👤

Senior

🎓

Top School

Jillian Anderson is a masters-prepared Big Data Analyst with a decade of experience turning complex datasets into actionable insights across academia, agriculture, and e-commerce. Based in Greater Vancouver, she has built and productionized data pipelines and analytics at Simon Fraser University while earlier delivering customer-focused ML solutions at BuildDirect. Jillian contributes to open-source tooling for data integration and record linkage—having implemented and hardened a Smith-Waterman string-comparison module—to improve duplicate detection in Python projects. Comfortable collaborating with non-technical stakeholders and leading cross-functional teams, she blends rigorous research experience from Waterloo with practical, impact-driven deployments. Her background in knowledge integration and hands-on field research gives her an uncommon ability to connect domain nuance with scalable data solutions.

10 years of coding experience

7 years of employment as a software developer

Bachelor of Knowledge Integration, Honours Knowledge Integration, minor in Computer Science, Bachelor of Knowledge Integration, Honours Knowledge Integration, minor in Computer Science at University of Waterloo

Master of Computing Science, Professional Masters in Big Data, Master of Computing Science, Professional Masters in Big Data at Simon Fraser University

Github Skills (8)

algorithm10

data-structures10

pandas10

algorithms10

python10

data-structure10

deduplication9

machine-learning8

Programming languages (2)

HTMLPython

Github contributions (5)

J535D165/recordlinkage

Jul 2017 - Aug 2017

A powerful and modular toolkit for record linkage and duplicate detection in Python

Role in this project:

Data Scientist

Contributions:16 commits, 3 PRs, 1 comment in 1 month

Contributions summary:Jillian primarily contributed to the implementation of the Smith-Waterman algorithm for string comparison within the record linkage toolkit. They developed and refined the algorithm, including adding error handling, assertions, and documentation. The user's work also involved integrating the Smith-Waterman algorithm into the existing comparison methods and correcting errors in the gap continuation scoring.

pythonstring-distancerecord-linkagededupemodular

jillianderson8/jillianderson8.github.io

Mar 2016 - Feb 2018

Contributions:106 pushes, 1 branch in 1 year 11 months

Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.

Request Free Trial