Manager Quality Systems & Data Analytics, R&D GMP Quality

3316 Takeda Development Center Americas•Lexington, MA

4d•Onsite

About The Position

As a Manager, Quality Systems & Data Science, you will play a critical role in transforming R&D GMP Quality from a reactive compliance-focused organization into a proactive and predictive quality organization through advanced analytics, data science, and digital innovation. You will develop and deploy advanced analytics, predictive models, and AI-enabled solutions that identify emerging quality risks, compliance trends, and operational opportunities before they impact product quality, patients, or regulatory outcomes. You will transform enterprise quality data into actionable insights that enable data-driven decision-making, continuous improvement, and strategic prioritization across R&D GMP Quality. You will lead the development of Quality data science capabilities by integrating data from Quality Management Systems, audits, deviations, CAPAs, inspections, supplier quality, laboratories, manufacturing, and development activities. You will partner up with business, quality, and digital stakeholders to establish a modern analytics ecosystem that supports Takeda's vision of becoming a predictive and performance-driven quality organization.

Requirements

Bachelor's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, Analytics, Information Systems, or related quantitative field.
Minimum 5 years of experience in data science, advanced analytics, business intelligence, or related discipline.
Experience supporting Quality, Manufacturing, Supply Chain, or R&D organizations.
Experience applying AI and predictive analytics to quality or operational processes.
Demonstrated experience building predictive models, machine learning solutions, and analytical frameworks.
Experience applying data science, advanced analytics, or statistical modeling in a regulated industry.
Proficiency in Python, SQL, and modern data analytics platforms.
Experience in predictive models, machine learning algorithms, and statistical analyses.
Experience transforming large, complex datasets into actionable business insights.
Strong knowledge of data visualization and executive storytelling techniques.

Nice To Haves

Master's degree in Data Science, Analytics, Statistics, Computer Science, Artificial Intelligence, or related field.
Minimum 5 years of experience within pharmaceutical, biotechnology, healthcare, or regulated industries.
Experience with Databricks, Azure Data Services, Snowflake, Power BI, Tableau, or Qlik.
Experience with AI/ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or equivalent.
Experience with Generative AI, NLP, or intelligent automation solutions.
Knowledge of pharmaceutical quality systems, GxP regulations, and quality management processes.
Experience supporting digital transformation initiatives.
Experience working within complex/matrixed organizations.
Experience working with international customers.

Responsibilities

Develop in partnership with R&D Quality Compliance Systems & Data and Digital, Data & Technology (DD&T), predictive analytics models to identify potential quality, compliance, operational, and supply risks before occurrence.
Apply statistical modeling, machine learning, and AI methodologies to analyze quality data and generate actionable business insights.
Build and maintain quality risk indicators, leading indicators, and predictive metrics that complement traditional lagging KPIs.
Identify hidden patterns, correlations, and emerging trends across deviations, CAPAs, audits, complaints, laboratory data, supplier quality, and manufacturing quality systems.
Develop advanced analytical frameworks to support quality decision-making, risk assessments, inspection readiness, and continuous improvement initiatives.
Design and implement scalable data science solutions leveraging enterprise platforms such as Databricks, Power BI, Azure, SQL, Python, and AI/ML technologies.
Partner with R&D Quality Compliance Systems & Data and Digital, Data & Technology (DD&T), business leaders, and quality stakeholders to establish data pipelines, governance models, and analytical capabilities.
Evaluate emerging AI technologies and identify opportunities to automate quality monitoring, signal detection, trend analysis, and business processes.
Lead proof-of-concept initiatives and pilot programs focused on predictive quality and intelligent automation.
Translate complex quality findings into clear, actionable recommendations for senior leadership.
Deliver executive-level insights that support strategic decision-making, resource prioritization, and performance management.
Create advanced visualizations and storytelling tools that communicate business risks, opportunities, and trends effectively.
Support organizational transformation initiatives through data-driven identification of efficiency opportunities and process improvements.
Establish standards, methodologies, and best practices for quality analytics and data science across R&D GMP Quality.
Promote data literacy and analytical thinking within the organization.
Mentor stakeholders on effective use of data, analytics, and visualization tools.
Ensure compliance with data governance, privacy, validation, and regulatory requirements.
Independently identifies analytical opportunities and develops solutions that influence strategic quality decisions.
Recommends predictive risk mitigation strategies based on advanced analytics and business insights.
Operates with a high degree of autonomy in a complex, global environment.
Leads the adoption of advanced analytics, artificial intelligence, and predictive quality methodologies.
Champions new ways of leveraging enterprise data to improve quality performance and patient outcomes.
Identifies innovative opportunities to automate, simplify, and optimize quality processes through digital technologies.
Drives the evolution of R&D GMP Quality toward a predictive and intelligence-driven organization.
Analytical capabilities with the ability to analyze a wide variety of information and data to determine potential risks.
Ability to anticipate potential problems and risks related to quality expectations and regulatory compliance, formulate action plans, and implement solutions.
Ability to work effectively in a fast paced, rapidly changing environment and to work on multiple work streams and projects simultaneously.