Tenova, a Techint Group company, is a worldwide partner for sustainable, innovative and reliable solutions in the metals and also through the well-known TAKRAF and DELKOR brands in the mining industries.

Your role in Tenova: R&D; Engineer- Data Scientist

Description:

Job Summary

The R&D; Engineer - Data Science, under the direction of the R&D; Manager, 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.

The R&D; Engineer - Data Science must possess a degree or diploma in a related field and a minimum of three years of related experience.

Job Duties

Predictive Modeling and Machine Learning

- 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.

Data Analysis and Data Management





- 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.
- Develop and maintain data processing, cleaning, and validation routines to ensure data quality, consistency, and usability.
- Troubleshoot data collection and data quality issues across development and production environments.

Tools and Reporting

- 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.

Improvement and Innovation

- Investigate current applications of data science towards the improvement of process efficiency and plant automation.
- Identify opportunities for improved data acquisition and expanded use of existing data.
- Support the development and improvement of project standards, procedures, documentation, and best practices.

Technical Expertise and Collaboration





- Stay current with relevant technologies, tools, industry trends, and best practices in data science, machine learning, and process automation.
- Provide technical guidance to team members, internal stakeholders, and customers.
- Present technical findings, research outcomes, and project results at meetings, workshops, conferences, or client sessions as required.
- Support recruitment, onboarding, and training of new members of the Project Operations team as needed.

Client and Project Support

- Build and maintain strong working relationships with internal teams, external clients, and project stakeholders.
- Collaborate with senior engineers and project managers to support timely, cost-effective project execution.
- Communicate regularly with clients, provide updates and recommendations, and upscale risks, concerns, or major issues when required.
- Participate in onsite engineering visits to customer locations, including international travel, to support project development and implementation.

Job Requirements

- Master's or PhD degree in Electrical, Computer Engineering or a related field
- 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 their practical advantages, limitations, and appropriate use cases.
- 3-5 years of experience using Python for data analysis, algorithm development, and machine learning model development.
- Hands-on experience with common data science, machine learning, and dat

📌 R&D Engineer- Data Scientist (Mississauga)
🏢 Tenova
📍 Mississauga