Join EY's Global Delivery Services as a Senior Data Scientist, specializing in Pharmaceuticals and Life Sciences. This role offers a unique opportunity to blend Data Science, AI/ML, and pharmaceutical domain expertise to create impactful, data-driven solutions. You will contribute to transforming manufacturing, quality, supply chain, and business operations.

Our Global Delivery Services team is a vital part of EY's global network, supporting EY's growth agenda. We operate across multiple locations worldwide, providing crucial support to clients in Consulting, Assurance, Tax, Strategy & Transactions, and Knowledge services. Our innovation specialists are dedicated to fostering an environment of rapid prototyping and innovative thinking.

We are committed to your professional development, offering opportunities to build new skills, assume leadership roles, and grow through mentorship. EY champions diversity, equity, and inclusion, creating a supportive atmosphere where every individual can thrive.

Design, develop, and deploy advanced data science and machine learning solutions to tackle pharmaceutical manufacturing and business challenges. Analyze complex datasets to uncover patterns, identify trends, and derive actionable insights. Develop predictive and prescriptive models, forecasting solutions, optimization models, and statistical analyses.

Perform comprehensive data preprocessing, feature engineering, model development, evaluation, and ongoing performance monitoring. Collaborate closely with cross-functional teams, including business, manufacturing, quality, engineering, IT, data engineering, and subject matter experts, to translate business problems into analytical solutions.

Develop analytics solutions for critical areas such as manufacturing performance optimization, quality improvement, deviation analysis, predictive maintenance, supply chain efficiency, and operational excellence. Build reusable analytical pipelines and work with data engineering teams to ensure robust data pipelines and datasets. Evaluate opportunities for Generative AI and Large Language Models (LLMs) where applicable.

Create dashboards and visualizations to present data-driven insights for operational and management decision-making. Support the deployment and operationalization of models using MLOps practices, including versioning, monitoring, testing, and governance. Ensure solutions adhere to data governance, security, privacy, validation, and regulatory requirements. Clearly communicate complex analytical findings to both technical and non-technical stakeholders. Provide technical reviews and guidance to junior data scientists.

A minimum of 6 years of professional experience in Data Science, Machine Learning, AI, or Advanced Analytics is required. A Bachelor’s or Master’s degree in a relevant field such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or Bioinformatics is essential.

Demonstrated experience applying data science within the pharmaceutical, biotechnology, healthcare, or life sciences domain is critical. You should possess strong hands-on experience in developing statistical and machine-learning models using real-world datasets. Proficiency in Python and SQL is a must, with experience in R or SAS being an advantage.

Familiarity with structured and unstructured datasets, including data preparation, feature engineering, statistical analysis, and model evaluation, is necessary. A solid understanding of pharmaceutical processes and regulated data environments is expected. Experience collaborating with multidisciplinary business, scientific, engineering, and technology teams is highly valued.

Experience in taking data science solutions from proof of concept to production deployment is preferred. Specific experience with pharmaceutical manufacturing/MES data, process data, quality data, laboratory data, or supply-chain data is highly desirable for manufacturing-focused roles. Strong analytical and problem-solving capabilities are crucial, along with the ability to translate business problems into data science use cases and effectively manage stakeholders.