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Data Science Machine Learning Jobs (NOW HIRING)

Shamrock

Data Science Analyst

Phoenix, AZ · On-site

The Data Science Analyst is responsible for using data science, machine learning, statistical modeling, and advanced analytics to solve complex business problems across Supply Chain and Operations.

Parsons

Machine Learning / Data Scientist

Washington, DC · On-site

$88K - $154K/yr

The Machine Learning / Data Scientist is a hands-on practitioner with strong capabilities in model ... Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, or a closely related ...

Carnegie Mellon University

Data Scientist

Arlington, VA · On-site

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 ...

Rule14

Be Seen First

Data Scientist Machine Learning

Santa Monica, CA · On-site

$75K - $100K/yr

As a Data Scientist Machine Learning, you will work within a small data ... science team focusing on predictive modeling, natural language processing, computer vision ...

Cmu

Data Scientist

Arlington, VA

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 ...

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 ...

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Data Science Machine Learning information

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$37.5K

$122.7K

$196.5K

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 the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 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.
More about Data Science Machine Learning jobs
What cities are hiring for Data Science Machine Learning jobs? Cities with the most Data Science Machine Learning job openings:
What states have the most Data Science Machine Learning jobs? States with the most job openings for Data Science Machine Learning jobs include:
Data Science Machine Learning jobs near you
Infographic showing various Data Science Machine Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Data Science Analyst

Shamrock

Phoenix, AZ • On-site

Full-time

Medical, Retirement, PTO

Posted 14 days ago

Job description

The Data Science Analyst is responsible for using data science, machine learning, statistical modeling, and advanced analytics to solve complex business problems across Supply Chain and Operations. This role works at the intersection of data, technology, and business operations to develop predictive insights, analytical models, and decision-support tools that improve planning, efficiency, cost management, and service performance.
The Data Science Analyst partners with business leaders, operations teams, IT, data engineering, and analytics stakeholders to identify high-value use cases, build and test analytical models, and translate technical outputs into actionable business recommendations. This role is well suited for a candidate who has strong technical skills but also enjoys applying those skills to practical operational challenges.
Essential Duties:
  • Develop, test, and validate predictive models and machine learning solutions for Supply Chain and Operations use cases.
  • Apply statistical modeling, forecasting, classification, regression, clustering, optimization, and other data science techniques to solve business problems.
  • Support use cases such as demand forecasting, labor forecasting, inventory risk, transportation optimization, order volume prediction, customer behavior trends, productivity analysis, and operational exception detection.
  • Develop and deploy anomaly detection models to identify operational exceptions, service disruptions, inventory irregularities, equipment failures, process deviations, and emerging business risks before they impact performance.
  • Support transportation and logistics optimization initiatives by applying advanced analytics and machine learning techniques to improve route efficiency, reduce fuel consumption, optimize network flows, and enhance service performance.
  • Clean, transform, structure, and analyze large datasets from multiple business systems.
  • Perform exploratory data analysis to identify relationships, trends, anomalies, and improvement opportunities.
  • Collaborate with business stakeholders to understand operational processes, pain points, and decision-making needs.
  • Translate business problems into data science questions, analytical methods, and measurable outcomes.
  • Evaluate model accuracy, performance, stability, and business value.
  • Partners with data engineering and IT teams to access, prepare, and improve data sources needed for modeling and analysis.
  • Create dashboards, visualizations, and presentations to communicate model outputs and recommendations.
  • Document model logic, assumptions, data sources, limitations, and business applications.
  • Support the deployment, monitoring, and ongoing refinement of analytical and machine learning solutions.
  • Stay current on data science, machine learning, AI, and analytics methods that may benefit the business.
  • Other duties as assigned.

Qualifications:
  • Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Business Analytics, Economics, Operations Research, Supply Chain, or a related field required.
  • Master's degree in Data Science, Analytics, Statistics, Operations Research, Computer Science, Industrial Engineering, or related discipline preferred.
  • 2-5 years of experience in data science, analytics, statistical modeling, machine learning, business intelligence, or related analytical roles.
  • Experience applying data science methods to real-world business or operational problems.
  • Experience building predictive models, forecasts, or machine learning prototypes preferred.
  • Proficiency in Python, R, SQL, or similar analytical programming languages.
  • Familiarity with machine learning libraries and methods such as scikit-learn, pandas, NumPy, regression models, classification models, clustering, time series forecasting, or optimization techniques.
  • Strong understanding of statistics, probability, data modeling, feature engineering, and model evaluation.
  • Ability to work with structured and unstructured data from multiple systems.
  • Experience with Power BI, Tableau, Databricks, Azure Machine Learning, Snowflake, or similar platforms preferred.
  • Ability to explain technical concepts to non-technical business stakeholders.
  • Strong problem-solving, critical thinking, and analytical reasoning skills.
  • Strong communication and data storytelling skills.
  • Ability to balance technical depth with practical business applications.
  • Strong attention to detail, data quality, and model reliability.
  • Must be flexible and willing to work the demands of the department which is generally limited to weekdays but may be subject to evenings or weekends due to project or department needs.

Corporate Summary:
At Shamrock Foods Company, people come first - our associates, our customers, and the families we serve across the nation. A privately-held, family-owned and -operated Forbes 500 company, Shamrock is an innovator in the food industry and has been since being founded in Arizona in 1922.
Our Mission: At Shamrock Foods Company, we live by our founding family's motto to "treat associates like family and customers like friends."
Why work for us?
Benefits are a major part of your overall compensation, and we believe offering them at an affordable cost is not only the right thing to do, but it helps keep you and your family healthy. That's why Shamrock Foods pays for the majority of your health insurance, allowing you to take home more of your paycheck. And it doesn't stop there - our associates also enjoy additional benefits such as 401(k) Savings Plan, Profit Sharing, Paid Time Off, as well as our incredible growth opportunities, continued education, and wellness programs.
Equal Opportunity Employer
At Shamrock Foods Co all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, veteran status, sexual orientation, gender identity or any other basis protected by applicable law.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.

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