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Temporary Data Scientist Machine Learning Jobs in Puerto Rico

Xtillion

AI Native Engineer - Senior Associate

San Juan, PR · On-site

$102K - $140K/yr

Whether your background is in software engineering, data science, machine learning engineering, or a blend of disciplines, this role is for builders who want to work at the frontier of applied AI ...

Xtillion

AI Native Engineer - Senior Associate

San Juan, PR · On-site

$120K - $158K/yr

Whether your background is in software engineering, data science, machine learning engineering, or a blend of disciplines, this role is for builders who want to work at the frontier of applied AI ...

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Temporary Data Scientist Machine Learning information

What does a temporary data scientist specializing in machine learning do?

A Temporary Data Scientist specializing in Machine Learning is responsible for designing, building, and deploying machine learning models to analyze data and generate insights, but works on a contract or short-term basis. Their duties often include data preprocessing, model selection and validation, and communicating results to stakeholders. They may also be tasked with automating processes, cleaning large datasets, and collaborating with other teams to implement solutions. The temporary nature of the job means they often focus on specific projects or provide support during peak periods.

What are the key skills and qualifications needed to thrive as a temporary data scientist specializing in machine learning?

To thrive as a Temporary Data Scientist Machine Learning, you generally need a strong background in statistics, programming (Python or R), and experience with machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with data visualization tools (like Tableau), machine learning libraries (such as scikit-learn, TensorFlow, or PyTorch), and version control systems (e.g., Git) is typically required. Strong problem-solving abilities, adaptability, and effective communication are crucial soft skills for collaborating with teams and translating technical findings to stakeholders. These skills ensure that temporary data scientists can quickly contribute actionable insights, drive data-driven decisions, and add value within a limited time frame.

What are some typical projects or tasks a temporary data scientist specializing in machine learning might work on?

As a temporary Data Scientist focusing on machine learning, you can expect to work on short-term, high-impact projects such as building predictive models, cleaning and preparing data, or developing automated analytics solutions. You may be brought in to support ongoing initiatives, provide expertise for a specific project phase, or help accelerate a backlog of tasks. Collaboration is common, and you'll likely work closely with data engineers, business analysts, and domain experts to understand requirements and deliver actionable insights within tight deadlines. This role offers exposure to diverse datasets and tools, and is an excellent opportunity to rapidly expand your experience and network.

What is the difference between Temporary Data Scientist Machine Learning vs Temporary Data Analyst?

AspectTemporary Data Scientist Machine LearningTemporary Data Analyst
Required CredentialsBachelor's/Master's in Data Science, Computer Science, or related fields; knowledge of ML algorithmsBachelor's in Statistics, Mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentProject-based, collaborative teams, tech-focused companiesBusiness units, reporting teams, data-driven departments
Employer & Industry UsageTech firms, finance, healthcare, e-commerceRetail, marketing, finance, consulting

Temporary Data Scientist Machine Learning roles focus on developing and deploying machine learning models, requiring advanced analytics skills. Temporary Data Analysts primarily interpret data, generate reports, and support decision-making. While both roles involve data handling, Data Scientists with ML expertise work on predictive modeling, whereas Data Analysts focus on descriptive analytics. The choice depends on the project needs and skill requirements.

What are popular job titles related to Temporary Data Scientist Machine Learning jobs in Puerto Rico?

For Temporary Data Scientist Machine Learning jobs in Puerto Rico, the most frequently searched job titles are:

What job categories do people searching Temporary Data Scientist Machine Learning jobs in Puerto Rico look for?

The top searched job categories for Temporary Data Scientist Machine Learning jobs in Puerto Rico are:

What cities in Puerto Rico are hiring for Temporary Data Scientist Machine Learning jobs?

Cities in Puerto Rico with the most Temporary Data Scientist Machine Learning job openings:

Temporary Data Scientist Machine Learning jobs near you
Infographic showing various Temporary Data Scientist Machine Learning job openings in Puerto Rico as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Principal Engineer, Data Analytics and Machine Learning (Hybrid - Aguadilla, PR)

Prattwhitney

Aguadilla, PR • On-site

$110 - $150/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 2 days ago

Pratt & Whitney rating

8.7

Company rating: 8.7 out of 10

Based on 157 frontline employees who took The Breakroom Quiz

16th of 72 rated aerospace companies

Job description

Date Posted: 2026-05-21
Location: Aguadilla, PR

Position

Principal Engineer – Data Analytics & Machine Learning for Mechanical Systems (Hybrid)

What You Will Do
  • Lead and oversee the analysis of engineering and aircraft performance datasets, delivering actionable insights to improve product performance and reliability.
  • Drive the development, training, and validation of machine learning models for predictive maintenance, service time estimation, and performance forecasting.
  • Define and standardize validation approaches, acceptance criteria, and performance metrics for analytical and ML models across multiple programs.
  • Guide and mentor junior engineers on data analytics and machine learning methodologies, fostering skill development within the team.
  • Serve as the primary technical liaison with engineering discipline owners, design teams, and external stakeholders to address critical engineering challenges.
  • Develop best practices for data curation, cleaning, integration, and preparation for analytics and ML workflows.
  • Oversee the analysis of strain gauge, structural, thermal, and flight/aircraft data to identify trends and predictive indicators.
  • Collaborate with design, reliability, service engineering, and customer support teams to provide data‑driven recommendations that enhance product performance.
  • Conduct advanced statistical analyses to assess quality, completeness, and integrity of engineering and enterprise data.
  • Research, evaluate, and implement cutting‑edge ML algorithms (e.g., supervised learning, unsupervised learning, clustering, anomaly detection) to solve engineering challenges.
  • Represent the organization in executive‑level presentations, communicating technical recommendations and insights to leadership and customers.
  • Develop and maintain technical documentation, presentations, and reports for technical and non‑technical audiences.
  • Advocate for and lead digital transformation efforts, leveraging digital thread, PLM systems, and model‑based engineering workflows.
  • Occasionally travel domestically and/or internationally to support project requirements, supplier engagements, or customer needs.
Qualifications
  • Bachelor’s or Master’s degree in Science, Technology, Engineering or Mathematics (STEM).
  • 8+ years of relevant experience, or 5+ years with an advanced degree.
  • Demonstrated professional communication skills in English (verbal and written).
  • U.S. citizenship required.
Preferred Qualifications
  • Expertise in data analytics workflows, statistical methods, and engineering data interpretation.
  • Advanced knowledge of machine learning concepts and experience deploying and validating models for prediction, classification, and anomaly detection.
  • Advanced degree in Mechanical Engineering, Aerospace Engineering, Data Science, or related field.
  • Deep understanding of mechanical engineering principles: structural behavior, dynamics, thermal concepts, and aircraft systems.
  • Strong experience with Python for data analysis (NumPy, Pandas, PySpark) and familiarity with TensorFlow or Scikit‑learn.
  • Experience with finite element analysis (FEA), structural analysis, thermal dynamics, or instrumentation data interpretation.
  • Proficiency with database tools and SQL.
  • Advanced experience with data visualization tools (Tableau, Power BI).
  • Experience with Agile methodologies and tools (JIRA, Confluence) and cross‑functional collaboration.
  • Knowledge of digital thread, PLM systems, or model‑based engineering workflows.
What We Offer
  • Medical, dental, and vision insurance.
  • Three weeks of vacation for newly hired employees.
  • Generous 401(k) plan with employer matching.
  • Participation in the Employee Scholar Program (ESP).
  • Life insurance and disability coverage.
  • Employee Assistance Plan with up to 8 free counseling sessions.
Equal Opportunity Employer

RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans’ Readjustment Assistance Act.

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What Pratt & Whitney employees say

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Benefits

Hours and flexibility

Workplace

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About Pratt & Whitney

Sourced by ZipRecruiter

Pratt & Whitney has a long history of leadership and innovation in the field of aviation propulsion. Our story begins in the 1920s and has flourished throughout the decades. We have continuously transformed and reinvented our businesses to offer superior products and services to our customers. In 1925, the Pratt & Whitney Aircraft Company was founded by Frederick B. Rentschler, pioneer of the air-cooled radial engine design which enabled unprecedented power-to-weight ratio. Its first engine, the R-1340 Wasp engine, transformed military and commercial aviation and is still in use today. In 1928, the Canadian division of the Pratt & Whitney Aircraft Company was established. In 1944, Pratt & Whitney began its gas turbine and jet propulsion initiative. The company constructed a wind tunnel, laboratory and engineering center to support our allies’ efforts in World War II. In 1945, wartime production included more than 300,000 Pratt & Whitney engines, touted by service members as extremely dependable – a legacy that continues today. From there, Pratt & Whitney continued to design and innovate more powerful, agile, and reliable engines becoming a leader in the aerospace industry. Today, Pratt & Whitney has more than 85,000 engines in service and more than 16,000 customers worldwide.

Industry

Engineering professional services

Company size

10,000+ Employees

Headquarters location

East Hartford, CT, US

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