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Temporary Data Scientist Machine Learning Jobs in Juncos, PR

ECHO Consulting Group

Sr Data Scientist

Juncos, PR · On-site

$85 - $120/hr

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 ...

Vast

Data Scientist

San Juan, PR · On-site

$95K - $165K/yr

Underperforming machines, wrong collection cadences, equipment to repair or replace, ground lost to ... data science, analytics engineering, or quantitative analysis with real operational impact.

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 ...

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All JobsData Scientist Machine Learning JobsTemporary Data Scientist Machine Learning JobsTemporary Data Scientist Machine Learning Jobs in Juncos, PR

Temporary Data Scientist Machine Learning information

See Juncos, PR salary details

$36.5K

$119.5K

$191.4K

How much do temporary data scientist machine learning jobs pay per year?

As of Sep 7, 2026, the average yearly pay for temporary data scientist machine learning in Juncos, PR is $119,534.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,900.00 and $132,400.00 per year, depending on experience, location, and employer.

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 Juncos, PR?

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

Temporary Data Scientist Machine Learning jobs near you
Infographic showing various Temporary Data Scientist Machine Learning job openings in Juncos, PR as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $119,534 per year, or $57.5 per hour.

Sr Data Scientist

BioPharma Consulting JAD Group

Juncos, PR • On-site

$110 - $170/hr

Other

Re-posted 23 days ago

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.
  • 6- month contract with possible extension
  • Administrative Shift
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