Charger Logistics Logo

Charger Logistics

Sr. Data Scientist

Posted Yesterday
Be an Early Applicant
In-Office
Brampton, ON, CAN
Senior level
In-Office
Brampton, ON, CAN
Senior level
Develop and deploy production-grade machine learning and AI solutions for fleet analytics, logistics optimization, forecasting, anomaly detection, predictive maintenance, and operational decision-making. Build batch and real-time pipelines, MLOps workflows, analytical data models, dashboards, and LLM-powered systems using Google Cloud, Kafka, RisingWave, BigQuery, and related technologies. Collaborate with stakeholders to translate business problems into scalable data science solutions.
The summary above was generated by AI

Charger Logistics Inc. is a leading asset-based transportation company with over 20 years of experience delivering innovative logistics solutions. We have evolved into a world-class transport provider and continue to expand across North America.

We invest in our people, fostering an environment where learning, growth, and career advancement are encouraged. As an entrepreneurial organization, we value initiative, creativity, and forward-thinking strategies.
We are looking for a Sr. Data Scientist to develop, deploy, and scale machine learning (ML) and AI solutions for fleet analytics, logistics optimization, and operational decision-making. This is a hands-on role focusing on production-grade ML, real-time and streaming analytics, and AI-driven decision systems built on cloud platforms, including Google Cloud, Kafka, and RisingWave.

Responsibilities:

  • Design, develop, and deploy production-grade ML models for fleet optimization, including route optimization, ETA prediction, fuel efficiency, capacity planning, predictive maintenance, and driver behavior analysis.
  • Build anomaly detection, forecasting, and time-series models to monitor vehicle health, trip deviations, fuel theft, and demand fluctuations.
  • Develop batch and real-time ML pipelines with low-latency inference using Kafka, RisingWave, and cloud services.
  • Integrate large language models (OpenAI, Google MCP, Ollama, Hugging Face) for conversational analytics, automated insights, and retrieval-augmented generation (RAG) systems.
  • Operate MLOps workflows on Google Cloud using Vertex AI Pipelines, Feature Store, and Model Registry, supporting model training, deployment, monitoring, and drift detection.
  • Build and optimize end-to-end data pipelines for analytics and ML using BigQuery, Dataflow, Dataproc, Vertex AI, Cloud Functions, Pub/Sub, and Cloud Composer (Airflow).
  • Design scalable analytical data models in BigQuery, AlloyDB PostgreSQL, and Snowflake; optimize SQL-based feature engineering, data partitioning, and clustering.
  • Perform exploratory data analysis (EDA) to uncover trends, anomalies, and business insights.
  • Build dashboards and visualizations for stakeholders.
  • Collaborate with cross-functional teams to translate business problems into robust data science solutions.
  • Support best practices in model development, experimentation, documentation, and data governance.

Requirements
  • Bachelor’s degree or equivalent in Data Analytics, Statistics, Mathematics, or Computer Science.
  • 6+ years of hands-on experience in data science and machine learning/AI, delivering production-grade ML solutions.
  • Strong experience in Python, including libraries such as Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, XGBoost, and LightGBM.
  • Advanced SQL skills, including CTEs, window functions, and query optimization.
  • Hands-on experience with Google Cloud, including Vertex AI (training, pipelines, deployment, feature store) and BigQuery (data modeling, performance tuning).
  • Experience with streaming platforms (Kafka, RisingWave) and Snowflake.
  • Knowledge of anomaly detection, time-series forecasting, optimization, and applied statistical modeling.
  • Experience deploying and monitoring ML models in production, including testing, and working with ETL/orchestration tools like Matillion, Airflow, and Cloud Composer.
  • Familiarity with advanced ML and AI techniques, including LLMs, geospatial or graph ML, computer vision, and GPS data analysis.
  • Solid understanding of knowledge retrieval patterns including RAG (Retrieval-Augmented Generation), with familiarity of emerging approaches like KAG (Knowledge-Augmented Generation) and CAG (Cache-Augmented Generation).
  • Experience with Azure, AWS, GCP, Databricks, or multi-cloud deployments is a plus.
  • Excellent communication and problem-solving skills, with the ability to thrive in fast-paced environments.
  • Certifications: Google Cloud Professional Data Engineer or Machine Learning Engineer is an asset; SnowPro® Advanced: Data Scientist certification preferred.

Benefits
  • Competitive Salary
  • Healthcare Benefit Package
  • Career Growth
HQ

Charger Logistics Brampton, Ontario, CAN Office

25 Production Road, Brampton, Ontario, Canada, L6T4N8

Charger Logistics Cambridge, Ontario, CAN Office

Cambridge, Canada

Similar Jobs

5 Days Ago
In-Office or Remote
2 Locations
Senior level
Senior level
Software • Cybersecurity
Provides technical leadership for applied AI and data science initiatives from experimentation through production. Builds machine learning and generative AI solutions for anomaly detection, malicious behavior detection, fraud analysis, and security use cases. Designs LLM, retrieval, embedding, and agentic workflows; establishes evaluation and monitoring practices; partners with engineering and MLOps on reliable deployment; advises stakeholders; mentors technical contributors; and promotes secure, scalable, and responsible AI practices.
Top Skills: AnthropicAws SagemakerAzure MlCi/CdClaudeClaude CodeCodexDatabricksGitGithub CopilotHugging FaceLangchainLanggraphLlm ApisMcpMlflowOpenaiPysparkPythonScikit-LearnSemantic Kernel
8 Days Ago
In-Office or Remote
3 Locations
Senior level
Senior level
Fintech • Software • Financial Services
Develop, validate, implement, maintain, and monitor predictive models supporting property and casualty claims reserving. Translate actuarial objectives into statistical and machine-learning solutions, deliver production-ready analytics, evaluate model performance, and strengthen governance, documentation, testing, controls, and monitoring. Collaborate with actuaries and business intelligence partners, integrate analytics into recurring processes, provide evidence-based recommendations, and coach peers on analytical practices.
Top Skills: Cloud-Based Analytical EnvironmentsDatabricksPythonRSQL
11 Days Ago
In-Office
Senior level
Senior level
Information Technology • Consulting
Develop and improve large-scale optimization and machine learning models for supply chain decisions, including routing, scheduling, and resource allocation. Build Python and SQL data workflows using Azure and Databricks, diagnose model and data issues, validate reliability, and translate business requirements into scalable analytical solutions. Collaborate with technical and business stakeholders, communicate trade-offs, contribute to technical design, and guide junior team members.
Top Skills: AzureDatabricksGurobiMachine LearningMilpPythonSQL

What you need to know about the Toronto Tech Scene

Although home to some of the biggest names in tech, including Google, Microsoft and Amazon, Toronto has established itself as one of the largest startup ecosystems in the world. And with over 2,000 startups — more than 30 percent of the country's total startups — Toronto continues to attract new businesses. Be it helping entrepreneurs manage their finances, simplifying business operations by automating payroll or assisting pharmaceutical companies in launching new drugs, the city's tech scene is just getting started.

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account