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Data Science Machine Learning Jobs in Pennsylvania

PPG Industries, Inc.

Data Scientist

Pittsburgh, PA · On-site

S. degree preferred in Data Science, Data Analytics, Machine Learning or equivalent * 1+ years of experience applying data science or analytics to real-world problems * Proficiency in Python

PPG

Data Scientist

Pittsburgh, PA · On-site +1

S. degree preferred in Data Science, Data Analytics, Machine Learning or equivalent * 1+ years of experience applying data science or analytics to real-world problems * Proficiency in Python

Varsity Tutors

Data Science Tutor

Harrisburg, PA · Remote

$18 - $40/hr

What We Look For In a Data Science Tutor * Advanced Subject Mastery ... Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ...

Varsity Tutors

Data Science Tutor

Allentown, PA · Remote

$18 - $40/hr

What We Look For In a Data Science Tutor * Advanced Subject Mastery ... Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ...

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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 Jun 29, 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.

Which has more salary, CS or AI?

Data Science and Machine Learning roles in AI generally have higher salaries than traditional computer science positions due to specialized skills in deep learning, neural networks, and advanced algorithms. AI roles often require expertise in programming languages like Python and frameworks such as TensorFlow, which are highly valued in the job market. Salaries vary by experience, location, and industry, but AI-focused positions tend to offer higher compensation on average.

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 engineers make $500,000?

Senior data science and machine learning engineers with extensive experience, advanced skills in programming, statistical analysis, and deep learning, and often working in high-demand industries or at large tech companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at executive or specialized levels.

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.

Do data scientists work with machine learning?

Data scientists often work with machine learning as a core part of their role, developing models to analyze data and make predictions. They use tools like Python, R, and libraries such as scikit-learn or TensorFlow to build and deploy machine learning algorithms. Knowledge of statistics, programming, and data manipulation is essential for this work.

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.

Which 3 jobs will survive AI?

Data science and machine learning roles are expected to persist as they require complex problem-solving, domain expertise, and creativity that AI tools currently cannot fully replicate. Jobs involving strategic decision-making, ethical considerations, and interpersonal skills, such as data analysts, AI ethics specialists, and AI system trainers, are also likely to remain in demand. Continuous learning and proficiency with AI tools will be essential for these roles to adapt and thrive.
Data Science Machine Learning jobs near you
Infographic showing various Data Science Machine Learning job openings in Pennsylvania as of June 2026, with employment types broken down into 56% Full Time, 42% Part Time, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $123,033 per year, or $59.2 per hour.
Senior Data Scientist- Gen AI

Senior Data Scientist- Gen AI

Tiger Analytics Inc.

Malvern, PA • On-site

Full-time

Posted 21 days ago

Key responsibilities

  • Design and implement data science solutions to address business challenges using advanced problem-solving methodologies and statistical techniques.

  • Develop, train, and deploy machine learning models with a focus on generative AI and large language models (LLMs).

  • Work with text and transcription data to build NLP-based solutions that enhance product capabilities and user experience.

Job description

Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

We are seeking an experienced Senior Data Scientist with 6-10 years of expertise to join our team. You will leverage advanced data science techniques, generative AI concepts, and NLP expertise to solve complex business problems and drive measurable impact aligned with our leadership's vision.

Key Responsibilities

  • Design and implement data science solutions to address business challenges using advanced problem-solving methodologies and statistical techniques.
  • Develop, train, and deploy machine learning models with a focus on generative AI and large language models (LLMs)
  • Work with text and transcription data to build NLP-based solutions that enhance product capabilities and user experience.
  • Partner with cross-functional teams including engineering, product, and business stakeholders to identify process and workflow gaps, analyze root causes using data science techniques, and architect scalable solutions that drive operational efficiency and business value.
  • Stay at the forefront of AI/NLP advancements and evaluate new algorithms for potential business applications
  • Create comprehensive documentation and present findings to stakeholders, ensuring alignment with organizational objectives

Requirements

  • 6-10 years of professional experience as a Data Scientist or in a closely related role
  • Strong Problem-Solving Skills: Demonstrated ability to approach complex business and technical challenges using data science methodologies and statistical techniques
  • Proficiency in Python and SQL: Hands-on experience in writing production-grade code, data manipulation, and querying large databases
  • Expertise in NLP: Proven experience working with text and transcription data, including preprocessing, feature engineering, and model development
  • Generative AI Knowledge: Solid understanding of latest-generation AI concepts including LLMs, prompt engineering, retrieval-augmented generation (RAG), and other contemporary generative AI applications
  • Curiosity and Continuous Learning: Passionate about staying current with emerging trends, research papers, and advancements in NLP and AI
  • Strategic Alignment: Ability to understand organizational vision and strategy, translating it into data-driven initiatives that create positive business impact
  • Strong Communication: Ability to articulate complex technical concepts to both technical and non-technical audiences.

Benefits

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.