Sumel Kaur

AI Researcher

Ontario, Canada2 yrs 9 mos experience

Key Highlights

Expert in scalable machine learning frameworks.
Proven track record in user retention strategies.
Strong background in data science interview processes.

Stackforce AI infers this person is a Data Science expert in SaaS with a focus on machine learning and user analytics.

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Skills

Core Skills

Machine LearningAzure EcosystemUser Retention

Other Skills

Microsoft Azure Machine LearningDistributed SystemsModel DeploymentCausal InferenceDockerScalable MLMLOpsRecommender SystemsFeature EngineeringGrowth AnalyticsPredictive ModelingDeep Learning

About

I'm a Staff Data Scientist at Google. I've sat on both sides of the data science interview table. I've prepped for the loop. I've also rejected candidates who I'd have hired if they'd known what was actually being tested. I write about that gap. Check out my Guide to Crack FAANG DS Role here - https://topmate.io/sumel_kaur

Experience

2 yrs 9 mos

Total Experience

2 yrs 7 mos

Average Tenure

1 mo

Current Experience

Google

Staff Data Scientist

Apr 2026 – Present · 1 mo

Microsoft

Senior Data Scientist

Jul 2023 – Feb 2026 · 2 yrs 7 mos

Spearheading the development of scalable, high-dimensional machine learning frameworks within the Azure ecosystem. I architect end-to-end predictive pipelines that integrate causal inference with deep learning to optimize enterprise-grade resource allocation. My work bridges the gap between theoretical research and production engineering, deploying robust algorithms that process petabyte-scale telemetry to drive strategic product intelligence.

Microsoft Azure Machine LearningDistributed SystemsMachine LearningAzure Ecosystem

Dropbox

2 roles

Data Scientist

Jun 2020 – Jul 2023 · 3 yrs 1 mo · Toronto, Ontario, Canada

Engineered the core probabilistic models driving user retention and feature adoption for a global collaboration platform. I utilized advanced multivariate experimentation and graph theory to decode complex user interaction patterns, directly influencing the product roadmap. By translating raw behavioral signals into actionable growth levers, I built self-learning systems that optimized the user journey and maximized lifetime value (LTV) at scale.

Model DeploymentCausal InferenceMachine LearningUser Retention

Data Scientist - Intern

Jan 2020 – Apr 2020 · 3 mos · Toronto, Ontario, Canada

Designed and implemented a novel anomaly detection algorithm to identify friction points within the file synchronization engine. I leveraged statistical process control and time-series analysis to reduce false positives in error reporting. This high-impact initiative streamlined the debugging workflow for senior engineering teams and established a new baseline for automated system health monitoring.