Data Sciences Specialist

Thermo Fisher Scientific•Whitby, ON

3d•CA$56,400 - CA$84,600•Onsite

About The Position

Our Whitby Site specializes in commercial manufacturing for a full range of conventional dosage forms with specialized capabilities, and houses a fully integrated pharmaceutical development services (PDS) facility. Discover Impactful Work: The Data Sciences Specialist exists to support MSAT operations by leveraging data analytics and digital tools to monitor process performance, identify trends, and support data-driven decision-making. The role focuses on developing data automation tools, collecting, analyzing, and visualizing manufacturing and quality data to improve operational efficiency, process understanding, and product quality. This position collaborates with cross-functional teams to support investigations, reporting, continuous improvement initiatives, and digital transformation efforts within a Good Manufacturing Practices (GMP) environment. It also enables the adoption of advanced analytics, dashboards, and AI-driven monitoring to proactively detect risks and optimize operations. Ultimately, the position bridges manufacturing, quality, and digital teams to deliver scalable, data-driven decision-making across the process lifecycle.

Requirements

Bachelor’s or Master’s Degree in Engineering, Data Science, Statistics, Pharmaceutical Sciences, or a related field
Training or certification in data analytics, statistical analysis, or data science tools (e.g., Python, Minitab, Discoverant, JMP, PowerBI, or SAS)
Knowledge of regulatory guidelines and frameworks (e.g., Food and Drug Administration (FDA), European Medicines Agency (EMA), and International Council for Harmonisation (ICH))
Familiarity or certification in Good Manufacturing Practices (GMP) and data integrity principles (e.g., ALCOA+)
Minimum of 1 year experience in pharmaceutical manufacturing, Manufacturing Science and Technology (MSAT), process validation, or data analytics within a Good Manufacturing Practices (GMP) environment
Experience performing statistical analysis and using data science tools (e.g., Python, R, JMP, or SAS) in manufacturing or quality settings
Experience working with manufacturing data systems such as Manufacturing Execution Systems (MES), Laboratory Information Management Systems (LIMS), or data historians
Experience supporting investigations (e.g., deviations or Out-of-Specification (OOS) results) using data-driven approaches, including internships or co-op placements in relevant industries
Good knowledge of Continued Process Verification (CPV), process validation lifecycle, and regulatory expectations (e.g., Food and Drug Administration (FDA), European Medicines Agency (EMA), and International Council for Harmonisation (ICH))
Expertise in statistical analysis, data science methodologies, and multivariate analysis techniques
Proficiency with data analytics and visualization tools (e.g., Python, R, JMP, SAS, Discoverant, and Power BI)
Understanding of manufacturing data systems, including Manufacturing Execution Systems (MES) and Laboratory Information Management Systems (LIMS)
Knowledge of data integration and data modeling
Strong analytical and problem-solving skills with the ability to interpret complex datasets and identify root causes
Ability to translate technical data into clear, actionable insights for cross-functional stakeholders
Strong leadership, collaboration, and influencing skills in a cross-functional environment
Ability to manage multiple priorities and drive projects in a fast-paced, regulated environment
Commitment to Good Manufacturing Practices (GMP) and data integrity principles (e.g., ALCOA+)
Ability to support continuous improvement initiatives and drive digital transformation and innovation
Detail oriented and organized, with a high degree of accuracy and thoroughness.
Good/Excellent organizational skills and ability to prioritize in a face-pace environment.
Demonstrated computer proficiency with Microsoft Office programs.
Proficiency with the English Language.

Responsibilities

Developing tools to analyze manufacturing and quality data to identify trends, variability, and potential risks impacting process performance.
Develop and maintain dashboards and reports using digital tools (e.g., Discoverant and Power BI) to enable real-time process monitoring and decision-making.
Oversee data collection, integration, and trending from manufacturing systems such as Manufacturing Execution Systems (MES),QEMS, Laboratory Information Management Systems (LIMS), and SAP
Support investigations related to deviations, Out-of-Specification (OOS) results, and process trends by providing data-driven insights.
Collaborate with Manufacturing Science and Technology (MSAT), Quality, Engineering, Automation, and Information Technology (IT) teams to align data-driven strategies with site and global objectives.
Act as the site Subject Matter Expert (SME) for Advanced analytics, Dashboards, Artificial Intelligence-enabled systems, including system configuration, user support, troubleshooting, and continuous improvement.
Implement and support advanced analytics, including artificial intelligence (AI) and machine learning (ML) models, to enhance process monitoring and predictive capabilities.
Translate complex datasets into clear, actionable insights and documentation to support regulatory submissions, audits, and inspections.
Define and track key performance indicators (KPIs) to ensure ongoing process control, compliance, and continuous improvement.
Mentor and provide technical guidance to peers about data analytics and digital tools.
Drive adoption of digital manufacturing solutions and promote a culture of data-driven decision-making across the organization.
Ensure all activities comply with Good Manufacturing Practices (GMP), data integrity principles, and internal quality systems.