Jacob Danovitch

Experience

Microsoft

Redmond, WA

Data & Applied Scientist II - Microsoft Search, Assistant and Intelligence
L62: 09/2025 - Present
L61: 03/2024 - 08/2025
  • Leading development of ranking models serving millions of M365 Copilot Chat & Search queries daily.
  • Shipped record improvements to Copilot grounding relevance quality by designing novel LLM-powered training strategy.
  • Architected recall evaluation tool now used org-wide as part of model shipping process.
  • Tech lead of research project fine-tuning SLMs for document ranking; mentored & led 5 early-career ICs, published results in Microsoft Journal of Applied Research.

Data & Applied Scientist - Microsoft Search, Assistant and Intelligence
L60: 07/2023 - 02/2024
  • Led experimentation for search personalization using graph embeddings.
  • Reduced training & preprocessing pipeline runtime from 5 days to 12 hours.
  • Conducted internal study measuring reliability of offline metrics, resulting in revamp of evaluation framework.

Mila - Quebec Artificial Intelligence Institute

Montreal, Canada

Graduate Research Assistant
04/2020 - 06/2023
  • Conducted research on graph representation learning in the Complex Data Lab.
  • Released popular benchmark for temporal graph learning in collaboration with SNAP and PyTorch-Geometric maintainers.
  • Published at top ML conferences including NeurIPS 2023/2024, PAKDD 2023, WWW 2021.

York University - Osgoode Hall Law School

Toronto, Canada (Remote)

Data Science Tech Lead - Refugee Law Lab
02/2021 - 06/2023
  • Built data platform for legal analytics project from the ground up using open-source software.
  • Implemented pipelines for scraping, parsing, & analyzing Canadian federal immigration court cases using Prefect & Ray.
  • Delivered version-controlled datasets to research team using Delta Lake, LakeFS, Kubernetes, and GitOps (FluxCD / Terraform).

Microsoft

Redmond, WA / Canada (Remote)

Data & Applied Science Intern - Microsoft Search, Assistant and Intelligence
05/2020 - 08/2022
  • Developed task-agnostic quality metrics to monitor node embedding retraining for Graph Intelligence Sciences team.
  • Designed novel inductive Graph Neural Network architectures to personalize email search for Search Relevance team.

Software Engineering Intern - Cortana
05/2018 - 08/2019
  • Named author on Contextual Suggestions project (patent #US-20210073293-A1).
  • Trained & deployed character-level RNN for intent classification and NER to extract meeting details from Outlook emails.
  • Implemented full-stack user feedback dashboard with natural language Q&A using C#, .NET, and Azure SQL.

Canadian Broadcasting Corporation

Ottawa, Canada

Data Science Consultant
09/2019 - 01/2020
  • Implemented high-performance pipeline to aggregate data on health violations in long-term care homes.
  • Scraped, cleaned, and parsed semi-structured PDFs from government reports.

Carleton University

Ottawa, Canada

Teaching Assistant
09/2018 - 01/2020
  • Completed teaching assistantships for undergraduate computer science (Web Development, Discrete Math I, Intro to CS I) and graduate-level journalism (Storytelling with Data) courses.
  • Gave lectures in professors' absence, assisted students in-class, graded evaluations, and held office hours.

Education

McGill University

Montreal, Canada

MSc Machine Learning | CGPA: 4
09/2020 - 04/2023
  • Completed MSc thesis on inductive link prediction with GNNs under supervision of Prof. Reihaneh Rabbany.

Carleton University

Ottawa, Canada

BSc Computer Science, Honors | Minor in Psychology | CGPA: 4
09/2017 - 08/2020
  • Graduated August 2020 with high distinction (Dean's Honour List from 2018-2020).
  • Completed BSc thesis on explainable hate speech detection using language models under supervision of Prof. Majid Komeili.
August 23, 2025 Jacob Danovitch · Résumé 1

Selected Publications

Gastinger, J et al (2024). Tgb 2.0: A benchmark for learning on temporal knowledge graphs and heterogeneous graphs. Advances in Neural Information Processing Systems 37.

Huang, S et al (2023). Temporal graph benchmark for machine learning on temporal graphs. Advances in Neural Information Processing Systems 36.

Huang, S and Danovitch, J and Rabusseau, G and Rabbany, R. (2023). Fast and attributed change detection on dynamic graphs with density of states. Pacific-Asia Conference on Knowledge Discovery and Data Mining.

Pelrine, K* and Danovitch, J* and Rabbany, R. (2021). The Surprising Performance of Simple Baselines for Misinformation Detection. Proceedings of The Web Conference 2021.

Fenton, RR et al. (2021). Composing rich content messages.. U.S. patent granted: #US-20210073293-A1.

Pelrine, K* and Danovitch, J* and Orozco Camacho, A and Rabbany, R. (2020). Detecting Informative COVID-19 Tweets by Attending over Linked Documents. Proceedings of the 6th Workshop on Noisy User-generated Text.

Danovitch, J. (2020). Linking social media posts to news with siamese transformers. 9th International Conference on Information Technology Convergence and Services (ITCSE 2020), Vancouver, CA.

Danovitch, J. (2019). Trouble with the Curve: Predicting Future MLB Players Using Scouting Reports. 2019 Carnegie Mellon Sports Analytics Conference, Pittsburgh, USA.

Grants & Awards

NSERC CGS-M Research Award

$17500 (declined)

Carleton University
2020

Clarence C. Gibson Scholarship

$3000

Carleton University
2020

Reproducible Research Competition Finalist

$1000

Carnegie Mellon Sports Analytics Conference
2019

I-CUREUS Grant

$2250

Carleton University
2019

E.W.R. Steacie Scholarship

$3000

Carleton University
2019

Lester B. Pearson Scholarship

$3000

Carleton University
2018

President's Scholarship

$3000

Carleton University
2017

Leadership Roles

Carleton University

Ottawa

Recognized Study Group Leader
09/2017 - 04/2018
  • Co-led study group for introductory CS courses, leading sessions for as many as 50 students at once.
  • Prepared and presented Jupyter Notebooks to interactive demonstrate course material.
  • Implemented website for group using Node.js and React, using Firebase for authentication and content delivery.

STEM Fellowship

Remote

Data Science Team Volunteer
06/2017 - 01/2018
  • Assisted in development & organization of the Big Data Challenge, a competition promoting data science education.
  • Created workshops for machine learning such as k-means clustering, OLS regression, and stochastic gradient descent.

Carleton Computer Science Society

Ottawa

First Year Representative
09/2017 - 04/2018
  • Elected to position co-representing 1st year students in the Computer Science program.
  • Helped design first line of CCSS merchandise and organized sales.
August 23, 2025 Jacob Danovitch · Résumé 2