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Data Science with 5+ years in Machine Learning & Cloud
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data scientist / ML engineer with a PhD in Electrical Engineering and 5.1 years of combined full-time and internship experience in building and deploying machine learning systems. Expert in Natural Language Processing, anomaly detection, time-series forecasting, and LLM applications. Proficient in Python, SQL, TensorFlow, PyTorch, AWS, Azure, and Databricks, with a proven track record of delivering production-ready ML solutions and interactive dashboards.
University of Victoria
PhD · Electrical Engineering
May 1, 2018 – May 1, 2026
University of Victoria
Master of Engineering
September 1, 2015 – August 1, 2017
Tianjin Normal University
Bachelor of Management
September 1, 2011 – July 1, 2015
Sunshine Logistics Inc.
Data & Machine Learning (Import Coordinator)
January 1, 2024 – Present
India
Ballard Power
Data Scientist (Co-op)
January 1, 2023 – August 1, 2023
India
Optigo Networks
Data Scientist (Mitacs Research Intern)
July 1, 2021 – December 1, 2021
India
VINN Auto
Data Scientist (Mitacs Research Intern)
January 1, 2021 – June 1, 2021
India
Istuary Innovation Group
IoT Security Specialist (Co-op)
May 1, 2017 – August 1, 2017
India
Canadian Tire
DevOps Support Analyst (Co-op)
September 1, 2016 – April 1, 2017
India
Cultural Fit Analysis
The candidate's diverse project experience across logistics, automotive, and network security, combined with academic research, suggests a broad interest in applying data science to various challenges. Their experience in both research and industry roles indicates a balanced perspective. The current role involves direct collaboration with operations teams, which points to a collaborative mindset. The breadth of skills and tools used aligns well with a dynamic data science environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project descriptions, particularly in identifying business needs and translating them into technical solutions (e.g., BERT classifier for email routing, KPI dashboard). Their experience with human-in-the-loop systems suggests an understanding of operational feedback and continuous improvement. The diverse internship experiences indicate adaptability and a willingness to learn across different domains.