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Data Science Machine Learning Jobs (NOW HIRING)

Nordic

Effectively communicates the data science / machine learning approach and how it will meet and address objectives to business partners. * Advocates and educates business partners on the machine ...

Nordic

Effectively communicates the data science / machine learning approach and how it will meet and address objectives to business partners. * Advocates and educates business partners on the machine ...

Beth Page tech

Data Scientist

Houston, TX · On-site

Data Science & Machine Learning: * Strong foundation in mathematics, statistics, and machine learning * Experience with exploring and extracting insights from multi-dimensional datasets * Proficiency ...

Vultus Inc

Preferred Experience * 3-6 years of experience in Data Science, Machine Learning, or a related field. * Strong understanding of statistics and machine learning algorithms. * Experience working with ...

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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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 Sep 2, 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 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.

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.

Is data science machine learning a high paying job?

Data science and machine learning roles are generally high-paying within the tech industry due to the specialized skills required, such as programming, statistical analysis, and experience with tools like Python or TensorFlow. Salaries vary based on experience, location, and company size but tend to be above average compared to many other professions.
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, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Senior Data Scientist

Nordic

Full-time

Re-posted 25 days ago

Job description

Make a difference. Be happy. Grow your career.

The Role

Nordic, repeat Best in KLAS IT Services Firm and solely serving the healthcare industry, strives to empower healthcare providers to leverage technology and to realize digital transformation. All Nordic staff embrace Nordic's maxims and mission to serve our customers who care so well for us.

Key Responsibilities

The Senior Data Scientist applies strong expertise in machine learning, data science, and other artificial intelligence techniques to design, prototype and build next generation advanced analytics engines and services to solve complex healthcare industry problems. The Senior Data scientist should also have awareness about Gen AI & Agentic AI concepts and have a point of view on how traditional data science workflows can evolve harnessing the power of Generative AI & Agentic AI. The data scientist collaborates with business partners (clinicians / supply chain / HR across healthcare organizations) to define the technical problem statement and hypothesis to test and develops efficient and accurate analytical models that mimic business decisions and incorporates them into analytical data products or tools with the support of a cross-functional team.
Essential Job Functions

  • Collaborates with business partners to develop novel ways to meet business objectives utilizing machine learning and AI techniques and tools.

  • Effectively communicates the data science / machine learning approach and how it will meet and address objectives to business partners.

  • Advocates and educates business partners on the machine learning process and reasoning for solving problems with data-driven decision making.

  • Leads analytic approaches to integrate machine learning output into applications and tools with data engineers, business leads, analysts, and developers.

  • Creates repeatable, interpretable, dynamic, and scalable models that are seamlessly incorporated into analytic data products.

  • Engineers features by using business acumen (including healthcare industry knowledge) to find new ways to combine disparate internal and external data sources.

  • Identifies and develops scalable processes, frameworks, tools, methods and standards.

  • Collaborates and learns with a growing team of experienced data scientists.

  • Stays connected with external sources of ideas through conferences and community engagements.

Skills and Experience

  • Generally, requires a Bachelor's degree and 12 years of related experience or a Master's degree and 8 years of related experience.

  • Experience building end to end data science / machine learning solutions that have delivered tangible business value (required)

  • Strong communication and stakeholder management skills (required)

  • Masters, Data Science, Computer Science, Engineering, or Statistics (preferred)

  • Doctorate, Data Science, Computer Science, Engineering, or Statistics (preferred)

  • Experience working in Healthcare industry (preferred)

  • Understanding of EPIC data model (preferred)

Minimum Years of Experience

  • 8+ years' experience in a data science role (required)

  • 8+ years' experience in a data engineering, analyst, or architecture role (required)

Other Knowledge, Skills and Abilities Required

  • Machine learning concepts and principles

  • Good grasp of Generative AI and Agentic AI concepts

  • Strong communication skills

  • Python coding

  • SQL, Databricks

  • Understanding of machine learning operations

  • Attention to detail

  • Ability to work with technical and non-technical stakeholders

  • Requirements translation

  • Agile work process

  • Continuous learner

Candidates located in the United States or Canada are encouraged to apply!

Applicants must be legally eligible to work in their respective country without current or future sponsorship requirements.

#LI-JD1

Nordic is an equal opportunity employer. We are committed to creating an inclusive environment for all employees and applicants. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, genetic information, marital or veteran status, or any other protected status under applicable federal, state, or local laws. We encourage individuals of all backgrounds to apply, including women, minorities, individuals with disabilities, and veterans.

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