Sr Data Scientist

Biopharma-Consulting-Jad-Group; Juncos (PR); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Contract position

Administrative Shift

Job summary

Biopharma-Consulting-Jad-Group is seeking a Senior Data Scientist to lead advanced analytics initiatives and collaborate with commercial, manufacturing, supply chain, and data teams. You will drive end‑to‑end data science projects, build high‑impact ML solutions, and deliver measurable business value through AI and statistical modeling.

You will develop models, dashboards, and visualizations, while ensuring regulatory compliance and data integrity across datasets.

Qualifications

• Doctorate or Master’s with relevant experience required.
• Strong background in data science, statistics, and ML.
• Experience with biotech/pharma or regulated environments is a plus.

Responsibilities

• Lead design and development of data science and AI capabilities.
• Build predictive models and prototypes using Python and ML libraries.
• Collaborate with cross‑functional teams to deploying analytics solutions.
• Manage multiple datasets with data integrity and governance.
• Present findings clearly to technical and non‑technical stakeholders.

Skills

Data science

Machine learning

AI methodologies

Python

R

SAS

scikit-learn

TensorFlow

Keras

PyTorch

SQL

Graph databases

Linux

Spark

Hive

BI/data visualization

Biotech/pharma exp

Analytical thinking

Communication

Education

Doctorate

Master’s + 2 years

Bachelor’s + 4 years

Associate + 8 years

High school/GED +10 years

Tools

Power BI

Smartsheet

Excel

JMP

Minitab

Power Automate

Job description

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.

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.

• Contract position
• Administrative Shift