Data Scientist

ATS Automation•Cambridge, ON

3d•CA$88,000 - CA$121,000

About The Position

Join our Innovation Center at ATS Corporation - a place to create differentiators with the future in mind. Our Innovation Center is focused on R&D; advancing existing technologies, filling gaps in existing automation products, technologies and processes to give ATS a competitive advantage.

Requirements

A post-secondary engineering degree, diploma or equivalent in a quantitative field (Computer Science, Information system, Mathematic, Statistics, Machine Learning, Artificial intelligence, Engineering)
Strong experience with the deployment, configuration, and operationalization of Databricks environments, including workspace architecture, cluster management, CI/CD integration, security, governance, and enterprise-scale administration.
Experienced in building and managing modern data pipelines and lakehouse architectures using Delta Lake, Delta Live Tables, Structured Streaming, Workflows, medallion architectures (Bronze/Silver/Gold), and real-time/batch ingestion frameworks.
Deep understanding of Databricks ecosystem components including Unity Catalog, data lineage, RBAC, monitoring/observability, cost optimization, ML/AI enablement, model serving, and secure enterprise data collaboration through Clean Rooms.
Proven experience integrating Databricks with enterprise cloud and industrial data ecosystems, including Kafka, SQL databases, APIs, IoT/OT platforms, and cloud environments such as Azure, AWS, and GCP.
Strong understanding of scalable data engineering, governance, multi-tenant architectures, and enterprise data platform strategies supporting analytics, AI, and operational intelligence initiatives.
Proficiency in programming languages like Python, R, or Java
Experience with data manipulation and analysis libraries (e.g., Pandas, NumPy)
Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
Experience with databases (SQL, Influx)
Knowledge of data warehousing and ETL processes
Familiarity with tools like Hadoop, Spark, or Kafka
Experience with cloud services such as AWS, Google Cloud, or Azure
Understanding of software engineering principles and best practices
Experience with version control systems (e.g., Git)
Ability to design and implement efficient algorithms and solutions
Demonstrated experience in deploying machine learning models to production
Experience with data visualization tools and techniques
Strong analytical and communication skills
Ability to work collaboratively in a team environment
Ability to communicate effectively, both orally and in writing
A self-starter with the ability to work as part of a team in a fast paced environment with minimal supervision

Nice To Haves

A Master’s degree is considered beneficial.
Experience with Agile development practices
Understanding of automation mechanical, electrical and control systems
Understanding of machine operation, maintenance, service and troubleshooting
Understanding of Machine Vision systems and solutions
Understanding of PLCs and PLC communication
Exposure and understanding of business intelligence

Responsibilities

Deployment, configuration, and operationalization of Databricks environments, including workspace architecture, cluster management, CI/CD integration, security, governance, and enterprise-scale administration.
Building and managing modern data pipelines and lakehouse architectures using Delta Lake, Delta Live Tables, Structured Streaming, Workflows, medallion architectures (Bronze/Silver/Gold), and real-time/batch ingestion frameworks.
Deep understanding of Databricks ecosystem components including Unity Catalog, data lineage, RBAC, monitoring/observability, cost optimization, ML/AI enablement, model serving, and secure enterprise data collaboration through Clean Rooms.
Integrating Databricks with enterprise cloud and industrial data ecosystems, including Kafka, SQL databases, APIs, IoT/OT platforms, and cloud environments such as Azure, AWS, and GCP.
Understanding of scalable data engineering, governance, multi-tenant architectures, and enterprise data platform strategies supporting analytics, AI, and operational intelligence initiatives.
Programming in languages like Python, R, or Java.
Data manipulation and analysis using libraries (e.g., Pandas, NumPy).
Utilizing machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
Working with databases (SQL, Influx).
Applying knowledge of data warehousing and ETL processes.
Familiarity with tools like Hadoop, Spark, or Kafka.
Experience with cloud services such as AWS, Google Cloud, or Azure.
Applying software engineering principles and best practices.
Using version control systems (e.g., Git).
Designing and implementing efficient algorithms and solutions.
Deploying machine learning models to production.
Utilizing data visualization tools and techniques.
Working collaboratively in a team environment.
Communicating effectively, both orally and in writing.
Working as a self-starter in a fast-paced environment with minimal supervision.
Working in a safe manner and reporting any health, safety or environmental concern to their manager or supervisor in a timely manner.
Working in compliance with divisional health, safety and environmental procedures.
Refraining from removing or altering safety devices or guarding unless hazardous energies are controlled through lockout-tagout methods.
Reporting any unsafe conditions or unsafe acts.
Reporting defects in any equipment or protective device.
Ensuring that the required protective equipment is used for the assigned tasks.
Attending all required health, safety and environmental training.
Reporting any accidents/incidents to supervisor.
Assisting in investigating accidents/incidents.
Refraining from engaging in any prank, contest, feat of strength, unnecessary running or rough and boisterous conduct.