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Big Data • Food • Hardware • Machine Learning • Retail • Automation • Manufacturing
Plans and executes capital engineering projects for food manufacturing, including feasibility, budgeting, technical development, equipment strategy, stakeholder coordination, project execution, and compliance with quality, safety, environmental, and engineering standards. Supports cross-functional teams, vendor site visits, factory acceptance tests, installations, and evolving project requirements.
Big Data • Food • Hardware • Machine Learning • Retail • Automation • Manufacturing
Supports shopper marketing and scale activation programs across Canadian retail banners. Responsibilities include managing multi-million-dollar budgets, developing annual plans, coordinating customer-specific creative and promotional activations, briefing agencies and vendors, securing approvals, analyzing Nielsen data, and collaborating with sales, brand, finance, customers, and external partners to drive conversion and ROI.
Top Skills:
Nielsen
Big Data • Fintech • Mobile • Payments • Financial Services
Own end-to-end analytical problems across risk, fraud, growth, and lending products. Develop strategies, monitor portfolio and macroeconomic trends, build metrics and analytical frameworks with Engineering and Machine Learning teams, and influence product, regulatory, compliance, and launch decisions. Engage external partners, translate complex insights into action, create AI-assisted workflows, and mentor junior analysts.
Top Skills:
Ai-Assisted WorkflowsData VisualizationMachine LearningPythonSQLStatistical Modeling
Analyze Gecko engine telemetry across desktop and Android to identify data-quality issues, including bot contamination and hardware noise. Build automated dashboards, audit telemetry collection gaps, develop data-sanitization methods, and produce statistical analyses connecting technical performance metrics with user behavior and engagement. Translate complex engineering data into actionable product and budget recommendations for leadership while working independently across Performance, Data, and Product teams.
The Gecko platform is the core engine of our product experience. To maintain competitiveness and user satisfaction, we must move beyond surface-level metrics to understand the deep correlations between engine performance and real-world user behavior. We currently face challenges with data quality, such as bot contamination in nightly telemetry, which obscures our understanding of true performance trends. A dedicated analyst is required to bridge the gap between engineering telemetry and actionable product strategy, ensuring that leadership has high-fidelity data to prioritize performance guardrails and technical budgets.
Engineering Deliverables- Automated Dashboards: Create and maintain high-level overview dashboards that translate complex performance data into consumable formats for Product Managers and leadership.
- Telemetry Gap Analysis: Audit the Gecko codebase to identify missing collection points or "probes" necessary for a complete understanding of the product experience.
- Data Sanitization Frameworks: Develop methodologies and tools to identify and filter data inconsistencies, specifically focusing on isolating bot traffic and hardware-specific noise from telemetry streams.
- Behavioral Inference Reports: Deliver deep-dive analyses correlating technical performance shifts (e.g., LCP, eTLD+1) with user-facing pain points and engagement trends.
The analyst will focus exclusively on Gecko engine telemetry across multiple platforms (Desktop and Android), collaborating with the Performance Team, Data Team, and Product Leadership.
Required Skills- Technical: Advanced SQL (BigQuery/Redash), proficiency in data visualization using AI tooling, and experience with statistical analysis (e.g., Pearson’s correlation, multivariate analysis). Knowledge of Rust/C++ is a strong plus. Strong proficiency with AI tools like Claude Code, Codex, or similar is highly desirable.
- Domain Knowledge: Ability to understand browser component interactions and their impact on user experience.
- Soft Skills: Proven ability to work independently, translating technical requirements into business insights.
- Independent problem solving: Comfortable working in unfamiliar technical problem spaces and figuring out a path forward with minimal guidance, using documentation, experimentation, source code, data, and other available resources.
Hiring Ranges:
Canada Tier 1 Locations
$93,450—$124,600 CAD
Canada Tier 2 Locations
$84,600—$112,800 CAD
Hiring Ranges:
Remote Germany
€62.100—€82.800 EUR
Hiring Ranges:
Remote UK
£58,500—£78,000 GBP
What you need to know about the Montreal Tech Scene
With roots dating back to 1642, Montreal is often recognized for its French-inspired architecture and cobblestone streets lined with traditional shops and cafés. But what truly sets the city apart is how it blends its rich tradition with a modern edge, reflected in its evolving skyline and fast-growing tech industry. According to economic promotion agency Montréal International, the city ranks among the top in North America to invest in artificial intelligence, making it le spot idéal for job seekers who want the best of both worlds.
Key Facts About Montreal Tech
- Number of Tech Workers: 255,000+ (2024, Tourisme Montréal)
- Major Tech Employers: SAP, Google, Microsoft, Cisco
- Key Industries: Artificial intelligence, machine learning, cybersecurity, cloud computing, web development
- Funding Landscape: $1.47 billion in venture capital funding in 2024 (BetaKit)
- Notable Investors: CIBC Innovation Banking, BDC Capital, Investissement Québec, Fonds de solidarité FTQ
- Research Centers and Universities: McGill University, Université de Montréal, Concordia University, Mila Quebec, ÉTS Montréal
