Data Scientist 35511
Title: Data Scientist – Job Opportunity 35511
Location: Juncos, Puerto Rico
Work Arrangement: Onsite | Administrative Shift
Contract Duration: 6 Months
Eligibility: Puerto Rico Residents Only
Ideal Candidate Profile
The ideal candidate combines technical expertise, analytical thinking, and operational knowledge to support AI-enabled optimization, resource planning, and data-driven improvements within a biopharmaceutical manufacturing environment.
This individual should be comfortable working with diverse teams, translating business needs into analytics solutions, and communicating complex findings in a clear and impactful manner.
Position Overview
Our client is seeking a Data Scientist to support digital transformation initiatives within Operations by leveraging advanced analytics, data modeling, and data-driven solutions to improve business performance.
This role will focus on analyzing complex operational data, identifying improvement opportunities, and developing analytics solutions that enable better decision-making across manufacturing operations. The successful candidate will collaborate with business leaders, technical teams, and subject matter experts to transform data into actionable insights that support process optimization, resource planning, capacity evaluation, and operational excellence.
The position requires strong analytical capabilities, technical curiosity, and the ability to apply data science methodologies within a regulated biopharmaceutical manufacturing environment.
Schedule Flexibility:
The primary schedule is an administrative shift; however, availability to support non-standard shifts may be required based on business needs.
Key Responsibilities
- Support end-to-end analytics projects, including data collection, analysis, interpretation, and presentation of insights to support business decisions.
- Partner with cross-functional teams and business leaders to understand operational challenges, define requirements, and develop data-driven solutions.
- Analyze manufacturing and operational data to identify trends, opportunities, risks, and performance improvement initiatives.
- Develop analytics solutions ranging from descriptive analytics to advanced modeling approaches, including machine learning-based applications.
- Create dashboards, visualizations, and reporting tools to provide visibility into operational performance.
- Support data management, governance, architecture, and modeling initiatives to improve data accessibility and reliability.
- Apply statistical analysis, process evaluation techniques, and data science methodologies to solve complex business problems.
- Assist in developing business cases, strategic recommendations, and operational improvement initiatives.
- Support validation-related activities through data analysis, documentation review, and compliance-focused execution.
- Collaborate with Information Systems, Operations, Manufacturing, Engineering, Finance, and other stakeholders to implement analytics solutions.
- Prepare executive-level communications and present findings to technical teams and leadership.
- Perform ad hoc analyses and support special projects as needed.
Preferred Qualifications and Experience
The ideal candidate will have a background in Data Science, Engineering, Computer Science, or a related technical discipline, combined with experience applying analytics within manufacturing or operational environments.
Preferred educational backgrounds include:
- Data Science
- Industrial Engineering
- Systems Engineering
- Computer Science
- Chemical Engineering
- Biomedical Engineering
- Biotechnology
- Manufacturing Engineering
- Other related technical fields
Engineering experience is highly preferred due to the focus on:
- Resource planning and workload modeling
- Capacity evaluation
- Process optimization
- Operational efficiency improvements
Candidates from science or data-focused backgrounds may also be considered if they demonstrate experience with:
- Data analytics and visualization
- Digital transformation initiatives
- GMP-regulated operations
- Validation support
- Manufacturing data analysis
Technical Skills and Competencies
Data Analytics and Visualization
- Ability to collect, organize, clean, analyze, and interpret complex operational or manufacturing datasets.
- Experience with analytics and visualization tools such as:
- Microsoft Excel
- Power BI
- Smartsheet
- JMP
- Minitab
- Tableau
- Spotfire
- Similar data analytics platforms
Programming, Automation, and Digital Tools
- Foundational experience with programming, automation, or digital workflow development.
- Familiarity with tools and technologies such as:
- Python
- SQL
- AI-assisted coding tools
- Power Automate
- Scripting
- Database structures
- Digital transformation solutions
Advanced programming expertise is not required; however, the candidate should demonstrate the ability and willingness to learn and apply digital tools to solve business challenges.
Statistical Analysis and Process Evaluation
- Understanding of statistical concepts, process variability, trending, and performance monitoring.
- Experience with:
- Statistical modeling
- Data comparison and interpretation
- Capacity analysis
- Workload forecasting
- Operational performance evaluation
GMP and Validation Experience
- Knowledge of GMP requirements and regulated manufacturing environments.
- Experience supporting:
- Validation lifecycle activities
- Protocol and report development
- Data integrity practices
- Documentation review
- Discrepancy investigations and follow-up
- Characterization studies
- Engineering runs
- Process Performance Qualification (PPQ) activities
Additional Preferred Qualifications
- Experience supporting Operations functions such as Manufacturing, Supply Chain, Engineering, or Technical Operations.
- Experience working with large, complex, or unstructured datasets.
- Ability to harmonize data from multiple operational systems and sources.
- Familiarity with manufacturing and operational systems, including:
- SAP
- MES
- LIMS
- Other enterprise data platforms
- Experience with advanced analytics tools, including:
- R
- Python
- SQL
- Alteryx
- Experience processing, filtering, and presenting large datasets from data warehouses or data lake environments.
- Exposure to cloud platforms such as AWS or Azure.
- Familiarity with DevOps technologies.
- Strong communication and collaboration skills across technical and business teams.
- Ability to manage multiple projects simultaneously in a fast-paced environment.
- Detail-oriented, adaptable, and comfortable working through ambiguity.
Education Requirements
Candidates must meet one of the following requirements:
Option 1:
- Master’s degree in Science, Engineering, Data Science, Business Analytics, Statistics, Computer Science, Applied Mathematics, or a related field.
Option 2:
- Bachelor’s degree in Science or Engineering with at least 2 years of experience in:
- Data Science
- Business Analytics
- Statistics
- Data Mining
- Applied Mathematics
- Engineering
- Computer Science
- Related analytical fields