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Data Science Machine Learning Jobs in Pennsylvania

Cmu

Data Scientist

Pittsburgh, PA

Requirements: * BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with eight (8) years of experience or equivalent combination of training ...

SoTalent

Data Science Manager

Philadelphia, PA · On-site

Lead, mentor, and develop teams of Data Scientists, Machine Learning Engineers, Researchers, and AI specialists. * Define and execute data science strategies aligned with product and business ...

Cmu

Senior Data Scientist

Pittsburgh, PA

Requirements: * BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with ten (10) years of experience or equivalent combination of training ...

Cmu

Associate Data Scientist

Pittsburgh, PA

$57K - $57K/yr

Requirements: * BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with three (3) years of experience or equivalent combination of training ...

TeleTracking

Machine Learning Engineer III

Pittsburgh, PA · On-site

$111K - $133K/yr

... data scientists, software engineers, clinicians, hospital administrators, and experts in TeleTracking Technologies to identify and develop high-impact machine learning solutions. • Work with large ...

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All JobsData Science Machine Learning JobsData Science Machine Learning Jobs in Pennsylvania

Data Science Machine Learning information

See Pennsylvania salary details

$37.6K

$123K

$197K

How much do data science machine learning jobs pay per year?

As of Aug 12, 2026, the average yearly pay for data science machine learning in Pennsylvania is $123,033.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,700.00 and $136,300.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a data science machine learning professional?

To thrive as a Data Science Machine Learning professional, you need a strong background in statistics, programming (usually Python or R), and a solid understanding of machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with tools like TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications such as AWS Certified Machine Learning, are typically valuable. Critical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These skills enable professionals to develop robust models, extract actionable insights, and drive data-driven decision-making in organizations.

What are some common challenges faced when deploying machine learning models as a data science machine learning professional?

A frequent challenge in this role is bridging the gap between building accurate models in a controlled environment and deploying them effectively in production systems. Issues such as data drift, model performance degradation, and integration with existing IT infrastructure often arise. Collaboration with engineering and IT teams is crucial to ensure models are scalable, maintainable, and secure. Regular monitoring and updating of deployed models are also essential responsibilities to sustain their value to the business.

What is the difference between Data Science Machine Learning vs Data Analyst?

AspectData Science Machine LearningData Analyst
Required SkillsProgramming (Python, R), statistics, machine learning algorithmsData visualization, SQL, basic statistics
Work EnvironmentDeveloping models, coding, experimenting with algorithmsData reporting, dashboard creation, data cleaning
Industry UsageTech, finance, healthcare, where predictive models are neededBusiness intelligence, marketing, operations

Data Science Machine Learning professionals focus on building predictive models and algorithms using programming and advanced statistics, often working on complex projects. Data Analysts primarily interpret data through visualization and reporting to support business decisions. While both roles require data skills, Data Science Machine Learning involves more technical programming and modeling, whereas Data Analysts focus on data interpretation and presentation.

What is data science machine learning?

Data science machine learning refers to the use of algorithms and statistical models to analyze and draw insights from complex data sets. In this field, professionals use machine learning techniques to build predictive models, automate decision-making processes, and uncover patterns in data. Machine learning is a core component of data science, enabling systems to improve their performance over time without being explicitly programmed. Data scientists with machine learning expertise are in high demand across industries like healthcare, finance, and technology.
Data Science Machine Learning jobs near you
Infographic showing various Data Science Machine Learning job openings in Pennsylvania as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 17% Part Time, 3% Contract, and 1% Nights. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $123,033 per year, or $59.2 per hour.

Data Scientist

Carnegie Mellon University

Pittsburgh, PA • On-site

Full-time

Re-posted 6 days ago

Carnegie Mellon University rating

8.6

Company rating: 8.6 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

67th of 618 rated colleges and universities

Job description

Job Summary:
Carnegie Mellon University is a leading institution focused on innovation and research. They are seeking a Data Scientist to leverage advanced statistics, data analytics, machine learning, and artificial intelligence to address cybersecurity challenges for government and industry clients.
Responsibilities:
• Work with customers to identify areas where advanced statistical techniques can help tackle problems, plan and develop prototype solutions, and build out final products.
• Co-author research proposals, execute studies, and present findings to DoW sponsors and at academic conferences.
• Work on a wide range of projects including research in generative AI and large language models, computer vision, multimodal AI, agentic AI, and assurance of AI systems.
• Craft metrics and experimental designs for large-scale cybersecurity research programs, develop human-in-the-loop machine learning solutions, and build classifiers to identify security vulnerabilities.
Qualifications:
Required:
• BS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with eight (8) years of experience or equivalent combination of training or experience; or MS in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with five (5) years of experience; or PhD in data science, machine learning, computer science, statistics, or related highly-quantitative discipline with two (2) years of experience.
• Willingness to complete modest travel to various locations to support the SEI’s overall mission.
• You will be subject to a background check and must be able obtain and maintain a U.S. Department of War security clearance.
• Experience in predictive modeling, data science, and/or AI & machine learning
• Deep understanding of statistical modeling techniques and advanced data analytics
• Proficient with at least one mathematical/statistical programming package (e.g., R, python numpy/scipy/pandas/polars, MATLAB, etc.)
• Innovative and inquisitive with ability to imagine novel analytical solutions to problems Thrives in a multi-disciplinary environment
• Strong communication skills
• Expertise in one or more of the following: Recommendation systems, Time-series forecasting (Prophet, NeuralProphet, Chronos, Lag-Llama, etc.), NLP / LLMs (fine-tuning, RAG, evaluation, prompt engineering), Causal inference / uplift modeling / synthetic controls, Modern ML frameworks: LightGBM/XGBoost, CatBoost, PyTorch,JAX, TensorFlow), LLMs / agentic workflows (LangChain/LlamaIndex/Haystack), Experience deploying models (FastAPI, Triton, KServe, SageMaker, Vertex AI, or similar), Experience working with big data (Spark, Trino, Snowflake, BigQuery, Databricks)
Preferred:
• Experience in cybersecurity and privacy is a plus
• Experience in U.S. Government work and/or with FFRDCs, UARCs an National Labs is a plus
• Demonstrated ability to learn new concepts and grow into new areas of work
Company:
Carnegie Mellon University is a research university offering programs and research across engineering, science, arts, and business. Founded in 1900, the company is headquartered in Pittsburgh, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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