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Data Science Machine Learning Jobs in Pennsylvania

RELX Group plc

Senior Data Scientist

Philadelphia, PA · On-site

Significant hands-on experience in Data Science, Machine Learning, Artificial Intelligence, NLP, Information Retrieval, Statistics, Computer Science, or a related quantitative discipline. * Advanced ...

Elsevier

Senior Data Scientist

Philadelphia, PA · On-site

Required : • Significant hands-on experience in Data Science, Machine Learning, Artificial Intelligence, NLP, Information Retrieval, Statistics, Computer Science, or a related quantitative ...

Motional

Machine Learning Engineer, Data Mining

Pittsburgh, PA · On-site +1

$111K - $133K/yr

Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery ... What We're Looking For (Must-Haves): * BS or MS in Computer Science, Machine Learning, or a related ...

Soni Resources

Data Scientist

Conshohocken, PA · On-site +1

$175K/yr

Qualifications Required * 3+ years of hands-on experience in Data Science, Analytics, Machine Learning, or a related quantitative field. * Proven experience developing predictive models or machine ...

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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 Jul 24, 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, and why are they important?

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 July 2026, with employment types broken down into 1% As Needed, 81% Full Time, 14% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $123,033 per year, or $59.2 per hour.
Senior Data Scientist

Senior Data Scientist

RELX Group plc

Philadelphia, PA • On-site

Full-time

Posted 13 days ago

Job description

Senior Data Scientist
AI for Science, Research Intelligence & Knowledge Discovery
Build AI That Helps Advance Human Knowledge
What if your next AI model could help accelerate a medical breakthrough, uncover a critical scientific insight, or help researchers solve some of humanity's greatest challenges?
At Elsevier, data science is about far more than algorithms and model performance. It is about applying advanced AI to help researchers, clinicians, educators, and institutions discover knowledge, assess evidence, generate insights, and advance science for the benefit of society.
Every day, millions of researchers rely on our products to navigate an ever-growing universe of scientific information. As a Senior Data Scientist, you will help build the intelligent systems that make scientific knowledge more discoverable, trustworthy, connected, and actionable.
This is AI with purpose. This is technology in service of scientific progress.
About the Role
As a Senior Data Scientist, you will design, build, evaluate, and scale advanced AI solutions that power scientific discovery, research intelligence, knowledge enrichment, and decision support across the global research ecosystem.
You will work on some of the most challenging problems in applied AI, combining machine learning, natural language processing, large language models, retrieval systems, knowledge graphs, and generative AI to help researchers uncover insights faster and make better decisions.
Success in this role requires deep technical expertise, sound judgment, scientific rigor, and the ability to transform complex problems into trusted, production-ready AI solutions that create measurable impact.
About the team
As part of a growing team of Data Scientists, you will take on some of the hardest problems in science. This team is building intelligent systems that can reason across scientific publications, research data, knowledge graphs, ontologies, metadata, taxonomies, citations, and content spanning every scientific discipline
What You'll Do
  • Design, develop, and deploy advanced machine learning, NLP, retrieval, and generative AI solutions that support scientific discovery and knowledge exploration.
  • Build and optimize LLM-powered applications, including question answering, literature summarization, semantic search, research insight generation, and evidence-grounded AI experiences.
  • Develop retrieval-augmented generation (RAG) systems that connect AI models with trusted scientific and scholarly content.
  • Create intelligent capabilities for search, ranking, recommendation, entity extraction, classification, enrichment, and decision support.
  • Design evaluation frameworks that measure quality, relevance, reliability, grounding, trustworthiness, and user impact.
  • Integrate knowledge graphs, ontologies, taxonomies, citations, metadata, and scientific domain knowledge into AI workflows.
  • Partner with engineering teams to produce, monitor, optimize, and continuously improve AI systems at scale.
  • Lead technical discovery, influence solution architecture, and guide methodological decisions across initiatives.
  • Mentor fellow data scientists and contribute to a culture of technical excellence, experimentation, and responsible AI.
  • Collaborate closely with Product, Engineering, Research, Editorial, UX, and domain experts to solve complex scientific and business challenges.

What We're Looking For
  • Significant hands-on experience in Data Science, Machine Learning, Artificial Intelligence, NLP, Information Retrieval, Statistics, Computer Science, or a related quantitative discipline.
  • Advanced expertise in developing and deploying machine learning, NLP, retrieval, and generative AI solutions in production environments.
  • Experience working with modern LLMs, prompt engineering, model evaluation, retrieval systems, and AI-powered workflows.
  • Extensive Python programming skills and a track record of building maintainable, production-quality software.
  • Experience designing and implementing RAG systems, semantic search, vector retrieval, embeddings, ranking, or recommendation solutions.
  • Deep understanding of machine learning fundamentals, experimentation, model evaluation, statistical analysis, and performance measurement.
  • Experience with modern AI and ML frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, LangGraph, or equivalent technologies.
  • Experience working with large-scale structured, semi-structured, and unstructured datasets, particularly text-rich or content-heavy data.
  • A passion for advancing science, expanding access to knowledge, and building AI systems that create meaningful real-world impact.

Why Join Elsevier
Because your work will matter.
You will help build AI systems that enable researchers to discover knowledge faster, uncover hidden connections, assess evidence more effectively, and accelerate scientific progress around the world.
You will have the opportunity to:
  • Solve some of the most challenging AI problems in science and knowledge discovery.
  • Work with one of the world's richest collections of scientific, biomedical, and scholarly data.
  • Build next-generation AI systems using LLMs, retrieval, knowledge graphs, semantic search, and generative AI.
  • Create trusted technologies that support researchers, clinicians, educators, institutions, and innovators worldwide.
  • Influence how AI is designed, evaluated, governed, and trusted in high-impact scientific environments.
  • Collaborate with exceptional colleagues across data science, engineering, product, research, editorial, and domain expertise.
  • Mentor others while helping shape the future of AI-powered scientific discovery.
  • Contribute directly to a mission dedicated to advancing science, improving health outcomes, and expanding human knowledge.

At Elsevier, AI is not just about what technology can do. It is about what humanity can achieve when knowledge becomes more accessible, discoverable, and actionable.
That is the impact of your work.
U.S. National Base Pay Range: $95,300 - $158,800. Geographic differentials may apply in some locations to better reflect local market rates.If performed in Maryland, the base pay range is $100,100 - $166,800.If performed in New York, the base pay range is $104,800 - $174,700.If performed in New York City, the base pay range is $114,300 - $190,500.If performed in Rochester, NY, the base pay range is $95,300 - $158,800.If performed in New Jersey, the base pay range is $112,574 - $179,826.This job is eligible for an annual incentive bonus.
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