R&D Engineer- Data Scientist

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 new 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 escalate 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 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 is an asset Willingness to travel internationally. Must be able to travel to foreign countries including the United States without restrictions. Competencies Adaptability - Adapts and responds to changing conditions, priorities, technologies, and requirements. Attention to Detail - Attends to details and pursues quality in the accomplishment of tasks, regardless of the volume of duties encountered. Communication - Expresses and transmits information with consistency and clarity. Continuous Improvement - Strives to improve job responsibilities through developing skills and increasing knowledge. Judgement - Ability to use sound reasoning when faced with various issues. Has the ability to make quick, effective decisions. Organization - Able to create or maintain processes to ensure all relevant information or tools are easily accessible. Ownership - Takes pride in the work that is accomplished, and understands the function of tasks within the larger picture of the organization. Ensures deadlines are met and work is completed properly. Problem Solving - Able to break down a situation into smaller pieces to identify key issues and figure out cause and effect relationships in order to solve. Use logic and analytical methods to come to realistic solution. Resourceful - Responds to difficult situations or workplace requirements by using the available tools and information to support decisions and solutions. Results Orientation - Able to focus on desired outcomes, and the means by which they are achieved by meeting and or exceeding standards based on past performance, goals, and objectives, as well as the performance and/or achievements of others. Time Management - Balances a myriad of tasks; prioritizes duties as needed. Commitment to Health and Safety - Works in compliance with all applicable health and safety legislation and established policies and procedures. Proficiency in MATLAB for data analysis, modeling, simulation, or algorithm development Experience visualizing, manipulating, and analyzing large datasets Ability to effectively communicate both verbally and in writing High flexibility with strong interpersonal skills that allow one to work effectively in a diverse environment Ability to work individually as well as part of a team Ability to prioritize and manage conflicting demands Demonstrated time management skills High level of integrity and work ethic