Data Scientist – Montreal, QC (Hybrid) | Visa + Salary Insights

📍 Location: ca

🏷 Type: Not specified

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Job Overview

This role is for a Senior-Level Data Scientist (5+ years) at a global digital transformation consultancy focused on delivering advanced AI, Machine Learning, and Generative AI solutions. You will join a specialized machine learning squad working with enterprise clients to design, build, and deploy predictive models and LLM-powered systems. The ideal candidate has strong expertise in Python, statistical modeling, and modern AI workflows including prompt engineering and model evaluation. This position is suited for professionals who can bridge business problems with scalable AI systems in production environments while collaborating across global engineering and analytics teams.

📅 Job Timeline & Status

This role is in a mid-to-late active hiring cycle, meaning the company is likely already reviewing applications while continuing to accept new candidates. Given the demand for AI and ML talent, competition is expected to be high. Candidates with strong production-grade ML experience should apply immediately to maximize visibility before shortlist saturation occurs.

🌍 Work Eligibility & Location

This is a Hybrid role anchored in Montreal, making it highly accessible for both local and relocating professionals. The employer is open to global talent and provides visa sponsorship pathways for qualified candidates, especially those with strong AI/ML engineering backgrounds and production deployment experience.

💰 Salary Intelligence

This compensation range is competitive for the Canadian AI market, particularly for professionals skilled in Generative AI, LLM integration, and predictive analytics. Candidates with strong production deployment experience and client-facing consulting exposure may command offers at the upper end of the range.

📊 Role Breakdown

This position centers on building and maintaining advanced Machine Learning and Generative AI systems for enterprise-scale applications. You will be responsible for translating business requirements into deployable AI pipelines using Python, statistical modeling, and modern LLM frameworks. A key focus is on building predictive analytics systems, conducting experimentation, and continuously improving model performance through structured evaluation methods.

A significant portion of the role involves working with prompt engineering techniques for LLM-based solutions, enabling better data analysis and insight generation. You will also participate in model benchmarking, feature selection, and performance tuning using real-world datasets. Approximately 30–40% of your time will be spent on model optimization and experimentation, while another 40–50% will focus on development, deployment, and client collaboration. The remaining portion involves monitoring, documentation, and iterative improvement of deployed ML systems. Strong emphasis is placed on production readiness and lifecycle management of AI systems.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a high-difficulty role due to its blend of enterprise ML engineering, GenAI integration, and consulting expectations. Candidates are expected to demonstrate not only theoretical knowledge but also real-world deployment experience with scalable AI systems.

🚀 Career Impact

This role offers strong long-term value in AI engineering leadership pathways, especially for professionals aiming to transition into AI Architect or Machine Learning Lead roles. Exposure to enterprise clients and GenAI systems significantly enhances future employability in high-growth AI sectors.

📋 Key Responsibilities

You will be responsible for designing, building, and maintaining Machine Learning models that solve real business problems across multiple industries. Core responsibilities include developing predictive analytics systems using Python, conducting feature engineering, and optimizing model performance through iterative testing. You will also contribute to prompt engineering workflows to improve LLM-based data insights and automation capabilities.

A key part of the role involves performing benchmarking experiments, validating model accuracy, and ensuring robust performance in production environments. You will collaborate with cross-functional teams to translate business requirements into scalable AI solutions. Additionally, you will monitor deployed models, ensure compliance with data privacy standards, and continuously improve system performance through structured evaluation and retraining cycles.

🎯 Application Strategy

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🧠 Fit & Positioning Analysis

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📅 Application Signals

This role shows strong application urgency due to active hiring in a high-demand AI domain. While no fixed deadline is published, roles like this often close quickly once sufficient shortlists are formed. Expect high competition from global ML engineers and AI specialists, especially those with GenAI experience. Interview cycles are likely to move within 1–3 weeks for shortlisted candidates, making early application critical. Applications may close without notice.

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✅ Job Source & Verification

This listing is sourced from a verified corporate career posting via a recognized global digital consultancy. Source credibility is high due to direct employer publication. Last updated: 2026-05-13, reflecting the most recent validation of role availability and requirements.