Sr Data Scientist

The Senior Data Scientist leads advanced analytics initiatives and collaborates with cross-functional partners-including commercial insights, manufacturing, supply chain, engineering, data teams, external vendors, service owners, and information systems-to develop analytical models and insights that solve complex business problems. This role drives end-to-end execution of data science projects, builds high-impact analytical solutions, and delivers measurable business value through machine learning, artificial intelligence, and statistical modeling.

KEY RESPONSIBILITIES

• Lead, design, and develop data science, machine learning, and AI capabilities across the organization.
• Build high-performance algorithms, prototypes, predictive models, and proof-of-concepts using Python and modern ML libraries.
• Work with SQL and other database query languages to extract, transform, and analyze large datasets.
• Apply statistical and analytical techniques to evaluate process variability, performance trends, capacity, and operational efficiency.
• Lead cross-functional analytics projects from concept to deployment with minimal supervision.
• Identify business needs, conduct SWOT analyses, propose analytical approaches, obtain stakeholder alignment, and execute solutions end-to-end.
• Manage multiple complex datasets, ensuring accuracy, consistency, and data integrity.
• Ensure compliance with regulatory, security, and privacy requirements related to data assets.
• Partner with manufacturing, supply chain, engineering, validation, quality, and digital/IS teams to develop methodologies that address specific business questions.
• Gather user requirements, translate business needs into analytical or digital tool specifications, and communicate findings clearly to technical and non-technical stakeholders.
• Collaborate with external vendors and digital partners to support model development, automation, and system integration.
• Present analytical concepts, project progress, and results in a clear, compelling, and actionable manner.
• Create strong data-driven narratives using PowerPoint, Excel, Power BI, Smartsheet, or similar visualization tools.
• Develop dashboards, reports, and visualizations to support decision-making across operations.
• Support characterization, validation, and GMP-related data evaluation activities.
• Apply statistical thinking to workload forecasting, resource planning, capacity modeling, and operational optimization.
• Support documentation practices, protocol/report development, discrepancy follow-up, and compliance-driven execution.

CORE COMPETENCIES & SKILLS

• Strong foundation in data science, machine learning, and AI methodologies.
• Proficiency in Python, R, SAS, and ML libraries (scikit-learn, TensorFlow, Keras, PyTorch, etc.).
• Experience with relational, SQL, and graph databases.
• Ability to write clean, reusable, well-abstracted code; comfortable working in Linux environments.
• Experience with distributed computing tools (Spark, Hive, etc.).
• Excellent analytical, logical reasoning, and problem-solving skills.
• Strong organizational and planning skills; ability to manage large datasets and multiple projects.
• Excellent communication skills with the ability to translate complex analysis into actionable insights.
• Passion for continuous learning and staying current with advanced analytics trends.
• Experience in biotech/pharma or regulated environments is a plus.

Requirements

EDUCATION REQUIREMENTS

One of the following is required:

• Doctorate, OR
• Master’s degree + 2 years of relevant experience, OR
• Bachelor’s degree + 4 years of relevant experience, OR
• Associate degree + 8 years of relevant experience, OR
• High school/GED + 10 years of relevant experience.

Relevant fields include: Data Science, Statistics, Data Mining, Applied Mathematics, Business Analytics, Engineering, Computer Science, or related technical disciplines.

PREFERRED QUALIFICATIONS

• Strong data analytics and visualization skills using Excel, Power BI, Smartsheet, JMP, Minitab, or similar tools.
• Ability to collect, clean, organize, analyze, and interpret complex operational or manufacturing datasets.
• Experience with automation or digital tools (Python scripting, AI-assisted coding, Power Automate, workflow development).
• Understanding of basic statistics, process variability, trending, capacity evaluation, and performance monitoring.
• Experience supporting characterization, validation, or GMP-related data evaluation.
• Familiarity with validation lifecycle activities, protocol/report development, documentation practices, data integrity, and compliance expectations.
• Strong stakeholder engagement skills; ability to gather requirements and communicate findings clearly to management and technical teams.
• Ability to work across manufacturing, engineering, quality, supply chain, and digital functions.

Benefits

• 6- month contract with possible extension
• Administrative Shift