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Temporary Data Scientist Machine Learning Jobs in Pittsburgh, PA

Cmu

Data Scientist

Pittsburgh, PA · On-site

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and artificial intelligence to help our government and industry clients research and solve cybersecurity ...

Carnegie Mellon University

Data Scientist

Pittsburgh, PA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and artificial intelligence to help our government and industry clients research and solve cybersecurity ...

Carnegie Mellon University

Data Scientist

Pittsburgh, PA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and artificial intelligence to help our government and industry clients research and solve cybersecurity ...

Cmu

Associate Data Scientist

Pittsburgh, PA · On-site

$57K - $57K/yr

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and artificial intelligence to help our government and industry clients research and solve cybersecurity ...

Cmu

Senior Data Scientist

Pittsburgh, PA

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and artificial intelligence to help our government and industry clients research and solve cybersecurity ...

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All JobsData Scientist Machine Learning JobsTemporary Data Scientist Machine Learning JobsTemporary Data Scientist Machine Learning Jobs in Pittsburgh, PA

Temporary Data Scientist Machine Learning information

See Pittsburgh, PA salary details

$36.4K

$119.2K

$190.8K

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

As of Aug 20, 2026, the average yearly pay for temporary data scientist machine learning in Pittsburgh, PA is $119,156.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,600.00 and $132,000.00 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 the most commonly searched types of Data Scientist Machine Learning jobs in Pittsburgh, PA?

The most popular types of Data Scientist Machine Learning jobs in Pittsburgh, PA are:

What are popular job titles related to Temporary Data Scientist Machine Learning jobs in Pittsburgh, PA?

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

What job categories do people searching Temporary Data Scientist Machine Learning jobs in Pittsburgh, PA look for?

The top searched job categories for Temporary Data Scientist Machine Learning jobs in Pittsburgh, PA are:

What cities near Pittsburgh, PA are hiring for Temporary Data Scientist Machine Learning jobs?

Cities near Pittsburgh, PA 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 Pittsburgh, PA 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, with an average salary of $119,156 per year, or $57.3 per hour.

Data Scientist- Process Modeling & Machine Learning

SMS group Inc

Pittsburgh, PA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 28 days ago

Job description

Data Scientist- Process Modeling & Machine Learning

Summary

We are seeking a Data Scientist who combines strong machine learning expertise with a genuine understanding of the processes behind the data. In this role, you will collaborate closely with process engineers and domain experts to understand how our machinery and production processes behave and apply that knowledge to develop models. The central focus of the position is enhancing our existing process models with data-driven techniques and machine learning. By integrating domain knowledge with advanced modeling methods, you will build solutions that perform robustly in production and earn the confidence of the engineers, operators, and customers who depend on them.

Who we are

At SMS group, our people are our greatest asset. We offer an entrepreneurial environment that promotes a culture of innovation, growth, and inclusion. We offer company events, activities, and opportunities to participate in charitable initiatives that benefit the communities where we are located.

www.sms-group.us

What you’ll do

Process Understanding & Domain Collaboration

  • Partner closely with process engineers, metallurgists, and domain experts to develop a deep, working understanding of the underlying processes and the data they generate.
  • Translate process know-how into model structure — constraints, features, and
  • relationships — rather than treating the process as a black box.
  • Spend time where the data comes from: participate in site visits to connect raw signals to real physical behavior.

Hybrid & Process-Informed Modeling

  • Enhance existing process models with data-driven techniques, replacing weak assumptions or unmodeled effects with learned components while preserving the physics that already works.
  • Design and implement hybrid models that combine domain/first-principles sub-models with machine learning (gray-box, physics-informed, and residual-modeling approaches).
  • Develop virtual/soft sensors to estimate quantities that are hard or expensive to measure directly.

End-to-End Data Science Delivery

  • Own data science problems end to end: scoping, data analysis (time-series and relational), feature engineering, modeling, validation, and deployment into production.
  • Serve as the algorithmic point of contact for your solutions, choosing the right tool for the problem — robust feature-based methods (e.g., scikit-learn) as well as deep learning (e.g., TensorFlow/Keras) where it adds value.
  • Build engineering prototypes and turn promising experiments into reliable, maintainable production solutions.

Production, Monitoring & Maintenance

  • Monitor model performance on live data, diagnose drift, and retrain or recalibrate as conditions change.
  • Continuously optimize and maintain deployed solutions to improve accuracy, robustness, and runtime performance.

Collaboration & Adoption

  • Work with cross-functional teams (product, engineering, project management) to align data science work with product roadmaps and project goals.
  • Engage with customers to gather feedback, refine solutions, and ensure that data-driven approaches are accepted and adopted by the people who use them.


What you’ll need

Required

  • Master’s degree in data science, Machine Learning, Statistics, Applied Mathematics, a quantitative engineering discipline (e.g., process modeling, mechanical, control), or a related field - or 2+ years of relevant experience.
  • Proven experience taking AI/ML solutions into real production environments (not just notebooks and prototypes).
  • Demonstrated ability to understand a problem domain and incorporate that understanding into models - comfort working alongside engineers and domain experts and learning the underlying process.
  • Understanding of software development practices: Python, SQL, Git, code review, and familiarity with container technologies.
  • Strong communication skills for working with other departments, customers, and stakeholders, and the ability to explain technical choices to non-specialists.
  • Ability to plan over longer horizons and coordinate work packages effectively.
  • Willingness to work on-site at the office and to travel to customer sites.

Preferred

  • Hands-on experience with hybrid / gray-box / physics-informed modeling, or with enhancing first-principles or simulation models using data-driven methods.
  • Experience building virtual/soft sensors, digital twins, or model-based monitoring for industrial or physical processes.
  • Proficiency with deep learning methods and frameworks.
  • Background in or exposure to an industrial / manufacturing / process domain (steel, metals, chemical, energy, or similar).
  • Experience researching and benchmarking existing solutions and algorithms before building from scratch.

Benefits and Opportunities

  • Open and Collaborative Culture: Work in a flat hierarchy where honest feedback and direct communication are valued. Join an international team and participate in bi-weekly company-wide open Fridays to discuss new tools, technologies, and approaches.
  • Professional Development: Contribute to scientific papers, collaborate with renowned research institutes on long-term projects, and access company-supported learning opportunities.
  • Real-World Impact: Have the opportunity to visit customer sites and witness the impact of your work on large machinery and steel production processes.
  • Continuous Learning: Engage in everyday learning opportunities, regular data science meetings, and paper discussions to stay updated on projects and scientific developments in data science and metallurgy.
  • Contributing to Industry Standards: Play an integral role in setting digitalization standards for the metals industry.

What we offer

  • Competitive compensation, medical/dental/vision coverage, paid vacation, paid holiday time, 401k with a company match, training, a tuition reimbursement program and more!

What we do

SMS group is the leading partner in the world of metals. We are an original equipment supplier offering comprehensive maintenance and spare part services for metals production, continuous casting and rolling (flat and long products), tubes, welded pipes, forging, non-ferrous technology, and heat treatment plants - all from a single source.

SMS group Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, sex, religion, national origin, age, sexual orientation, disability, veteran status, gender identity or other categories protected by law. Employment is contingent upon successful completion of a drug screen and physical capacity profile test.