R & D Engineer - Data Science

Agilus Work Solutions; Mississauga; On-site; CAD 100,000 - 110,000; Full time

Benefits offered by this job

Health & dental benefits

Vacation allotment

Job summary

Agilus Work Solutions is recruiting for a R&D Engineer – Data Science in Mississauga. The role involves analyzing operational data, developing algorithms, and deploying predictive models to optimize manufacturing processes.

The ideal candidate holds a degree in a related field and has at least 3 years of experience along with proficiency in Python and machine learning techniques. Competitive salary along with health and dental benefits.

Qualifications

• 3+ years of relevant experience in data science and machine learning.
• Hands-on experience in designing end-to-end machine learning solutions.

Responsibilities

• Analyze operational and test data using statistical analysis.
• Develop algorithms and deploy predictive models for optimization.
• Participate in onsite engineering visits for project implementations.

Skills

Python for data analysis

Programming skills in Python

Supervised machine learning

Data science and machine learning libraries

Education

Degree or Diploma in Electrical, Computer Engineering or related field

Tools

pandas

NumPy

scikit-learn

PyTorch

Matplotlib

Seaborn

Job description

Agilus by Synergie is recruiting for a R&D Engineer – Data Science in the metals industry in Mississauga, Ontario. The R&D Engineer – Data Science is responsible for analyzing operational and test data, developing algorithms, and researching, building, evaluating, tuning, and deploying predictive and machine learning models for manufacturing process control, optimization, and simulation.

This role applies statistical analysis, data visualization, machine learning, and data processing techniques to identify patterns, interpret results, improve data quality, and develop data-driven solutions for development and production applications.

Total rewards: $100,000.00 – $110,000.00 annually. Health & dental benefits. Vacation allotment.

Responsibilities

• Develop and maintain data processing, cleaning, and validation routines to ensure data quality.
• Troubleshoot data collection and data quality issues across development and production environments.
• Research, develop, evaluate, tune, and deploy predictive and machine learning models for manufacturing process control, optimization, and simulation.
• Build and maintain end-to-end machine learning pipelines, from data ingestion and validation through deployment and inference.
• Monitor and analyze model and system performance, identify sources of prediction error, and tune models and integrated systems to improve accuracy and reliability in development and production applications.
• Investigate, recommend and integrate appropriate machine learning technologies into the company’s production platforms.
• Perform statistical analysis, data visualization, and interpretation of results to identify trends, patterns, and improvement opportunities.
• Collect, review, clean, and validate operational and test data for use in modeling, analytics and reporting.
• Participate in onsite engineering visits to customer locations, including international travel, to support project development and implementation.
• Develop reusable tools, procedures, and workflows to support deployment, monitoring, operations, and process automation.
• Participate in the development and maintenance of dashboards that summarize key performance indicators (KPIs) for data collection equipment and model health.
• Prepare technical documentation, progress reports, analysis summaries, and internal or external project updates.

Qualifications

• Degree or Diploma in Electrical, Computer Engineering or a related field.
• 3+ years related experience.
• Experience using Python for data analysis, algorithm development, and machine learning model development.
• Strong programming skills in Python, with solid understanding of data structures, algorithms, and software development principles.
• Strong understanding of supervised machine learning methods for regression and classification, including practical advantages, limitations, and appropriate use cases.
• Hands‑on experience with common data science, machine learning, and data visualization libraries such as pandas, NumPy, scikit‑learn, PyTorch, Matplotlib, Seaborn, or equivalent tools.
• Experience designing and implementing end‑to‑end machine learning solutions, including data ingestion, preprocessing, validation, training, evaluation, deployment, and inference.
• Strong understanding of data pipeline stages and practical experience developing data processing and cleaning routines.
• Experience with time‑series analysis, forecasting, and regression‑based modeling.
• Experience with spectral analysis and strong understanding of Fourier Transform concepts.
• Knowledge of statistical techniques and concepts, including regression, probability distributions, statistical testing, and proper application of statistical methods.
• Experience with object‑oriented programming and application development.
• Experience with C#, C++ or VB.net is an asset.
• Willingness to travel internationally; ability to travel to foreign countries including the United States without restrictions.

Benefits

• Annual salary: $100,000.00 – $110,000.00.
• Health & dental benefits.
• Vacation allotment.