Charger Logistics Inc. is a world-class asset-based carrier with locations across North America. With over 20 years of experience providing the best logistics solutions, Charger Logistics has transformed into a world-class transport provider and continues to grow.

Charger Logistics invests time and support into its employees, providing them with the room to learn, grow their expertise, and advance their careers. We are an entrepreneurial-minded organization that welcomes and supports individual ideas and strategies.

We are currently seeking a senior, hands-on Lead Data Science & AI Engineer to join our team at our Brampton, Ontario office. This role will architect and lead large-scale ML and AI systems for fleet analytics and logistics optimization, focusing on production-grade machine learning, real-time streaming analytics, and AI-powered decision systems using Google Cloud (Vertex AI, BigQuery), Kafka, and RisingWave.

As a Lead Data Science & AI Engineer, you will design, build, and scale advanced ML and AI solutions for real-time fleet optimization, predictive maintenance, anomaly detection, and intelligent decision-making. This is a pure data science and ML engineering role, focused on solving complex logistics and telematics problems at scale using cloud-native and streaming architectures.

You will serve as the technical leader for ML initiatives, owning system architecture, model strategy, and production deployment while collaborating closely with product, engineering, and DevOps teams.

Responsibilities:

Machine Learning & Predictive Modeling

• Design and deploy ML models for route optimization, ETA prediction, fuel efficiency, and fleet utilization.
• Build anomaly detection systems for trip deviations, fuel theft, vehicle health, and driver behavior.
• Develop time-series forecasting models for demand planning, maintenance, and capacity optimization.
• Implement reinforcement learning and optimization models for dynamic routing.

Real-Time & Streaming ML

• Architect low-latency ML inference pipelines using Kafka and RisingWave.
• Build real-time feature engineering, streaming aggregations, and live anomaly detection.
• Develop online learning and adaptive models for continuously evolving fleet data.

AI & LLM Solutions

• Integrate LLMs (OpenAI, Google MCP, Ollama, Hugging Face) for conversational analytics and automated insights.
• Design and implement RAG systems for fleet knowledge and anomaly explanation.
• Build AI-driven recommendations and operational decision-support agents.

ML Platform & MLOps

• Design scalable ML infrastructure on GCP using Vertex AI Pipelines, Feature Store, and Model Registry.
• Implement automated training, deployment, A/B testing, monitoring, and drift detection.
• Ensure model explainability, governance, and cost-efficient serving strategies.

Data Engineering for ML

• Design optimized data models in BigQuery and AlloyDB PostgreSQL.
• Build scalable ETL/ELT pipelines for large-scale telematics and IoT data.
• Optimize data partitioning, clustering, and SQL-based feature extraction.

Technical Leadership

• Lead ML projects from ideation to production.
• Define ML architecture, standards, and best practices.
• Mentor data scientists and ML engineers.
• Communicate complex ML concepts to technical and non-technical stakeholders.

• 7+ years of hands-on experience in data science and machine learning.
• 3+ years in a senior or lead ML role.
• Expert-level Python (3.9+) with strong software engineering and testing practices.
• Deep experience with scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch.
• Strong background in anomaly detection, time-series forecasting, and optimization.
• Production ML experience including deployment, monitoring, and A/B testing.
• 3+ years of hands-on Google Cloud experience, including:
• Vertex AI (training, pipelines, deployment, feature store).
• BigQuery (advanced SQL, optimization).
• Kafka and real-time streaming architectures.
• Experience with LLMs, embeddings, vector search, and RAG architectures.
• Domain experience in logistics, fleet management, telematics, or IoT.

Nice to Have:

• Reinforcement learning, bandits, or optimization algorithms.
• Computer vision for driver or dashcam analytics.
• Geospatial or graph ML (routing, GPS trajectory analysis).
• Multi-cloud ML experience (AWS / Azure).
• Sustainability, EV fleet optimization, or regulatory compliance experience.

Why This Role?

• Architect ML systems with real-world, large-scale logistics impact.
• Work with cutting-edge AI, streaming data, and cloud-native ML platforms.
• High ownership, strong technical leadership, and direct business impact.
• Opportunity to shape the future of intelligent fleet operations at Charger Logistics.

• Competitive Salary
• Healthcare Benefit Package
• Career Growth