Data Annotation Program Manager Jobs in Cayey, PR

Inteldot has over 15 years in the life science industry with allocations across Puerto Rico, the United States, Europe, and Japan. This is a great opportunity for one of our leading clients in Puerto Rico.
Administrative Shift (8:00AM - 5:00PM)
Description:
The Data Scientist will lead projects and collaborate with business partners including commercial insights teams, manufacturing, supply chain, engineering, data teams, external vendor partners, service owners and IS partners to develop analytical models and insights across the PR Operations Organization to answer/solve specific business problems. This role will lead advanced analytics projects from the front and will be responsible for end-to-end execution. This role will innovate and create significant business impact through the strategic use of advanced analytics techniques.
Requirements:
The ideal candidate should demonstrate a strong combination of technical, analytical, and operational skills to support AI-enabled optimization, resource planning, and validation-related initiatives within Drug Product.
Knowledge of GMP expectations, validation lifecycle activities, protocol/report development, documentation practices, data integrity, discrepancy follow-up, and compliance-driven execution.
Strong communication and stakeholder engagement.
Ability to work with cross-functional teams, gather user requirements, translate business needs into tool requirements, and communicate findings clearly to management and technical stakeholders.
Be available to support non-standard shift when activities are required.
A standout candidate would have experience or demonstrated capability in the following areas:
Data analytics and visualization.
Ability to collect, organize, clean, analyze, and interpret complex operational or manufacturing data. Experience with tools such as Excel, Power BI, Smartsheet, JMP, Minitab, or similar platforms would be highly valuable.
Programming, automation, and AI-enabled tools.
Foundational programming or automation experience, including exposure to Python, Codex, AI-assisted coding tools, Power Automate, scripting, database structure, or digital workflow development. The candidate does not need to be an expert programmer but should be comfortable learning and applying digital tools to solve business problems.
Statistical and process evaluation mindset.
Understanding of basic statistics, process variability, trending, capacity evaluation, data comparison, and performance monitoring. This would support both workload forecasting and characterization/validation data evaluation.
Validation and/or GMP documentation experience.
Functions:
Leading, using and developing data science, machine learning, and artificial intelligence capabilities across commercial organization.
Leading the projects and be part of cross functional teams on projects and/or programs with aims to systematically derive insights that ultimately derive substantial business value.
Taking the initiative and work independently with minimal supervision.
Identifying business needs, doing SWOT analysis, proposing potential analytical approaches for solutions, obtain approvals and the execute the work end to end.
Building high-performance algorithms, prototypes, predictive models and proof of concepts using Python.
Working with SQL and other DB query languages.
Leading, collaborating and communicating cross-functionally with stakeholders to develop appropriate methodology to answer specific business questions.
Presenting analysis ideas, progress and results to business partners in clear and impactful manner.
Creating powerful stories in PowerPoint. Well versed in MS Office suite specifically Excel and PowerPoint.
Assuring compliance with regulatory, security, and privacy requirements as it relates to data assets.
Education:
Masters + 2 years of data science, business, statistics, data mining, applied mathematics, business analytics, engineering, computer science or related field experience OR Bachelors + 4 years of data science, business, statistics, data mining, applied mathematics, business analytics, engineering, computer science or related field experience
The following educational backgrounds may be considered, provided the candidate's experience meets the role requirements: Industrial Engineering, Systems Engineering, Computer Science, Chemical Engineering, Biomedical Engineering, Biotechnology, Manufacturing Engineering, or a related technical discipline.
A background in Engineering is highly preferred due to the project's focus on resource planning, workload modeling, capacity evaluation, process optimization, and operational efficiency. However, candidates from science, or data-focused backgrounds may also be strong fits if they demonstrate experience with data analytics, digital tools, GMP operations, and validation support.