Senior Data Scientist

Charger Logistics Inc. is a leading asset-based transportation company with operations across North America. With more than 20 years of experience delivering innovative logistics solutions, Charger Logistics has evolved into a world-class transport provider and continues to expand its footprint.

We are deeply committed to our people, investing in their development by fostering an environment where learning, growth, and career advancement are encouraged. As an entrepreneurial organization, we value initiative and actively support new ideas and forward-thinking strategies.

We are currently looking for a Senior Data Scientist to join our team in Brampton, Ontario. This role is a hands-on, highly technical position focused on developing, deploying, and scaling machine learning and AI solutions for fleet analytics and logistics optimization. The position emphasizes production-grade ML, real-time and streaming analytics, and AI-driven decision systems built on Google Cloud (Vertex AI, BigQuery), Kafka, and RisingWave.

In this role, you will work closely with data engineering, product, and platform teams to build scalable ML systems that power real-time fleet optimization, predictive maintenance, anomaly detection, and intelligent operational decision-making.

Responsibilities:

- Design, develop, and deploy production-grade machine learning models for fleet optimization, including route optimization, ETA prediction, fuel efficiency, capacity planning, and predictive maintenance.

- Build and refine anomaly detection and forecasting models to identify trip deviations, fuel theft, vehicle health issues, driver behavior risks, and demand fluctuations using time-series and statistical techniques.
- Develop and support real-time and streaming ML pipelines with low-latency inference and live feature engineering using Kafka and RisingWave.
- Implement and maintain batch and near-real-time ML workflows for operational decision support.
- Contribute to online learning, adaptive models, and optimization approaches for dynamic routing and operational improvements.
- Integrate large language models (OpenAI, Google MCP, Ollama, Hugging Face) to enable conversational analytics, automated insights, and AI-powered operational tools.
- Build and maintain retrieval-augmented generation (RAG) systems for fleet intelligence, anomaly explanation, and knowledge discovery.
- Develop and operate MLOps workflows on Google Cloud using Vertex AI Pipelines, Feature Store, and Model Registry, supporting training, deployment, monitoring, experimentation, and drift detection.
- Ensure model reliability, explainability, governance, and cost-efficient serving in production.
- Design and optimize analytical data models in BigQuery and AlloyDB PostgreSQL, and contribute to scalable ETL/ELT pipelines for high-volume telematics and IoT data.
- Optimize SQL-based feature engineering, data partitioning, and clustering for performance at scale.
- Collaborate with cross-functional teams to translate business problems into robust data science solutions.
- Support best practices in model development, experimentation, and documentation, and provide guidance to junior team members when needed.

Requirements

- 5+ years of hands-on experience in data science and machine learning, delivering production-grade ML solutions.
- Expert-level proficiency in Python (3.9+), with strong software engineering, testing, and code quality practices.
- Deep hands-on experience with ML frameworks and libraries such as scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch.
- Strong expertise in anomaly detection, time-series forecasting, optimization, and applied statistical modeling.
- Proven experience deploying, monitoring, and experimenting with ML models in production, including A/B testing.
- 3+ years of hands-on experience with Google Cloud, including:

- Vertex AI (training, pipelines, deployment, feature stores)
- BigQuery (advanced SQL, performance tuning, optimization)
- Kafka and real-time streaming architectures

- Experience building and integrating LLM-based systems, including embeddings, vector search, and RAG.
- Domain experience in logistics, fleet management, telematics, IoT, or other large-scale operational data environments.
- Nice to Have:-

- Experience with reinforcement learning, bandits, or advanced optimization techniques.
- Computer vision experience for driver monitoring or dashcam analytics.
- Exposure to geospatial or graph-based ML, including routing and GPS trajectory analysis.
- Experience deploying ML solutions across multiple cloud platforms (AWS and/or Azure).
- Background in sustainability initiatives, EV fleet optimization, or regulatory compliance.

Benefits

- Competitive Salary
- Healthcare Benefit Package
- Career Growth