Sr. Data Scientist

Posted 2 Hours Ago
Hiring Remotely in United States
Remote
Senior level
Transportation
The Role
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 data pipelines, MLOps workflows, streaming analytics, LLM and RAG systems, analytical data models, and stakeholder dashboards using cloud platforms. Collaborate cross-functionally to translate business challenges into scalable data science solutions while supporting model governance, monitoring, experimentation, and documentation.
Summary Generated by Built In

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

Skills Required

  • 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 or AI
  • Production-grade machine learning solution delivery experience
  • Strong Python experience with Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, XGBoost, and LightGBM
  • Advanced SQL skills, including CTEs, window functions, and query optimization
  • Hands-on Google Cloud experience with Vertex AI and BigQuery
  • Experience with Kafka, RisingWave, and Snowflake
  • Knowledge of anomaly detection, time-series forecasting, optimization, and applied statistical modeling
  • Experience deploying and monitoring machine learning models in production
  • Experience with ETL and orchestration tools such as Matillion, Airflow, and Cloud Composer
  • Familiarity with LLMs, geospatial or graph machine learning, computer vision, and GPS data analysis
  • Understanding of RAG, KAG, and CAG knowledge retrieval patterns
  • Excellent communication and problem-solving skills
  • Experience with Azure, AWS, GCP, Databricks, or multi-cloud deployments
  • Google Cloud Professional Data Engineer or Machine Learning Engineer certification
  • SnowPro Advanced Data Scientist certification
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The Company
HQ: Brampton, Ontario
615 Employees

What We Do

Charger Logistics'​ strives to offer the best client focused logistics solution. We start with flexibility. By offering various safe and efficient solutions for all product sizes, weights and sensitivities our limits are minimal. Additionally, our network, various locations throughout North America and fleet size allow us to offer our clients what they need every time. Charger Logistics was founded in the early 2000's and has grown by leaps and bounds since then. From owning a single truck to owning a fleet of over eight-hundred trucks, two-thousand trailers including reefers, dry vans, chassis, flat beds, step decks and more! A lot has changed however, our commitment to our clients will never change.

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