Data Scientist at Charger Logistics Inc

Brampton, Ontario, Canada -

Full Time

Start Date

Immediate

Expiry Date

02 Jan, 26

Salary

0.0

Posted On

04 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Cloud Technologies, Python, SQL, Data Pipelines, Exploratory Data Analysis, Data Governance, MLOps, Anomaly Detection, ETL Tools, Visualization, Statistical Analysis, Data Testing, Problem Solving, Communication

Industry

Truck Transportation

Description

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 continue to grow. Charger logistics invests time and support into its employees to provide them with the room to learn and grow their expertise and work their way up. We are entrepreneurial-minded organization that welcomes and support individual idea and strategies. Responsibilities: Understand business processes and translate analytical needs into scalable data science and ML solutions using cloud technologies. Collect and process structured and unstructured data from multiple internal and external sources to support model development and analytics initiatives. Perform exploratory data analysis (EDA) to uncover trends, anomalies, and root causes behind business outcomes. Develop and deploy machine learning models using modern cloud services (e.g., Vertex AI, BigQuery ML, Dataflow) and open-source frameworks. Build and automate end-to-end data pipelines for analytics and machine learning using tools such as: BigQuery, Dataflow, Dataproc, RisingWave, Vertex AI, Cloud Functions, Pub/Sub, and Cloud Composer (Airflow). Collaborate with engineering teams to implement real-time data pipelines and APIs for integrating ML models into production systems. Drive scalable, reproducible experimentation, model validation, and deployment of ML solutions. Leverage strong Python and SQL skills, including advanced techniques such as CTEs and window functions, for data wrangling, modeling, and analysis. Build dashboards and visualizations to present actionable insights to business stakeholders. Contribute to the development and implementation of data governance, MLOps, and model monitoring best practices. University degree or equivalent in data analytics, statistics, math, or computer science, with a minimum of 5 years relevant experience. Strong expertise in SQL (including CTEs, window functions, query optimization) and Python for data science and analytics workflows. Experience working with RisingWave or similar streaming SQL/data platforms is preferred. Google Cloud Professional Data Engineer or Machine Learning Engineer certification is a strong asset. Minimum of 3 years of hands-on Snowflake experience; SnowPro® Advanced: Data Scientist Certification is preferred. Experience with Azure, AWS, and Databricks platforms. Experience with ETL tools like Matillion and orchestration tools such as Airflow or Cloud Composer is an asset. Strong knowledge of Python libraries such as Pandas, NumPy, Scikit-learn, and PyTorch for machine learning and statistical analysis. Experience with Azure ML Studio is considered an asset. Practical experience with anomaly detection algorithms and unstructured data is a plus. Solid understanding of data testing practices, including unit and integration testing. Excellent communication skills to translate analytical findings into actionable business insights for both technical and non-technical audiences. Ability to work in ambiguous environments, apply creative problem-solving, and deliver data solutions aligned with business objectives. Health Benefits Career Growth High Competitive Salary

Responsibilities

The Data Scientist will understand business processes and translate analytical needs into scalable data science and machine learning solutions. They will collect and process data, perform exploratory analysis, and develop machine learning models using cloud technologies.