Sr Data Scientist

BioPharma Consulting JAD Group

About The Position

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.

Requirements

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.
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.

Nice To Haves

Experience in biotech/pharma or regulated environments is a plus.
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.

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.