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Data Science Machine Learning Jobs in Kentucky (NOW HIRING)

SynergisticIT

Full Stack Java Developer

Richmond, KY · On-site

$48.50 - $62.75/hr

Currently, We are looking for entry-level software programmers, Java Full stack developers, Python/Java developers, Data analysts/ Data Scientists, Machine Learning engineers. Who Should Apply Recent ...

PNC Bank

Fraud Model Analyst

Louisville, KY · On-site

$58K - $134K/yr

Preferred Skills Analytical Thinking, Competitive Advantages, Data Analytics, Data Mining, Data Science, Machine Learning (ML), Python (Programming Language), R Programming, Structured Query Language ...

OneSix - External

Senior Data Scientist

Canada, KY · On-site +1

$140K - $180K/yr

Its team of skilled Data Engineers, Data Scientists, Machine Learning (ML) Experts, and AI Engineers seamlessly integrate with client teams to solve their most challenging business problems.

Xometry

Senior Machine Learning Engineer

Lexington, KY · Hybrid

$103K - $142K/yr

Xometry is seeking a Senior Machine Learning Engineer to join our growing organization. The right ... Experience with large scale data processing (e.g., Hands-on experience training and applying models ...

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Showing results 1-20

All JobsData Science Machine Learning JobsData Science Machine Learning Jobs in Kentucky

Data Science Machine Learning information

See Kentucky salary details

$32.6K

$106.6K

$170.7K

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 Kentucky is $106,602.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,500.00 and $118,100.00 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 Kentucky as of June 2026, with employment types broken down into 56% Full Time, 38% Part Time, and 6% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $106,602 per year, or $51.3 per hour.

Sr Data Scientist

Constellation Energy Generation, LLC.

KY • On-site

$130K - $145K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 17 days ago

Key responsibilities

  • Develop predictive models and apply advanced analytics techniques to extract insights from large and complex data sets.

  • Collaborate with stakeholders and internal teams to understand business needs and translate them into data science projects.

  • Collect, cleanse, standardize, and analyze data from a variety of internal and external sources.

Job description

WHO WE ARE
As the largest private-sector power producer in the world and the nation's largest producer of clean and reliable energy, Constellation is focused on our purpose: lighting the way to a brilliant tomorrow for all. We have been the leader in clean energy production for more than a decade, and we are cultivating a workplace where our employees can grow, thrive, and contribute. Now integrated with Calpine, our portfolio includes 55 gigawatts of capacity from nuclear, natural gas, geothermal, hydro, wind and solar facilities, with the generating capacity to power the equivalent of 27 million homes.
Our culture and employee experience make it clear: We are powered by passion and purpose. Together, we're creating healthier communities and a cleaner planet, and our people are the driving force behind our success. At Constellation, you can build a fulfilling career with opportunities to learn, grow and make an impact. By doing our best work and meeting new challenges, we can accomplish great things. Join us in meeting the country's energy needs today and tomorrow.
TOTAL REWARDS
Constellation offers an extensive selection of benefits and rewards to help our employees thrive professionally and personally. We provide competitive compensation and a wide-range of benefits that support both employees and their families, helping them prepare for the future. In addition to highly competitive salaries, eligible employees are offered a bonus program, 401(k) with company match, employee stock purchase program; comprehensive medical, dental and vision benefits, including robust wellbeing programs; disability and life insurance benefits; paid time off for vacation, holidays, and sick days; and much more.
Expected salary range of $130,500 to $145,000, varies based on experience, along with comprehensive benefits package that includes bonus and 401(k).
PRIMARY PURPOSE OF POSITION
Apply the appropriate data science or analytical methods to extract knowledge and insights from data, which may take the form of time-series (power plant equipment data, environmental data or other), structured (relational data stores), and unstructured (text and multi-media) data sets. Closely collaborate with various internal stakeholders, information architects, data engineers, project/program managers, and other teams to turn data into critical information to inform decision making. This requires understanding business needs, providing and receiving regular feedback, and planning the proper transfer of developed solutions. Mine big and small data for insights, using advanced statistic and machine learning methods. Validate findings with the business by sharing analysis outputs in a way that can be understood by business stakeholders. Fill role of a subject matter expert in the areas of artificial intelligence, machine learning, feature engineering, data mining, and data manipulation/storage. Demonstrate commitment to continuous learning and professional development in technical subject matter. Share knowledge with team members, and business stakeholders, and IT partners. Collect, cleanse, standardize and analyze data from a variety of internal and external sources. Produce novel insights to help inform business actions using statistical modeling and machine learning techniques on complex data-sets on the order of several terabytes or petabytes.
PRIMARY DUTIES AND ACCOUNTABILITIES
  • Develop key predictive models that lead to delivering reduced overall annual expense for nuclear, performance improvement, and optimize specific performance criteria. Develop and recommend data sampling techniques, data collections, and data cleaning specifications and approaches. Apply missing data treatments as needed.
  • Analyze data using advanced analytics techniques in support of process improvement efforts using modern analytics frameworks, including but not limited to Python, R, Scala, or equivalent; Spark, Hadoop file system and others
  • Access and analyze data sourced from various Company systems of record. Support the development of strategic business and program implementation plans.
  • Access and enrich data warehouses across multiple Company departments. Build, modify, monitor and maintain high-performance computing systems.
  • Provide expert data and analytics support to multiple business units
  • Works with stakeholders and subject matter experts to understand business needs, goals and objectives. Work closely with business, engineering, and technology teams to develop solution to data-intensive business problems and translates them into data science projects. Collaborate with other analytic teams across Exelon on big data analytics techniques and tools to improve analytical capabilities.

MINIMUM QUALIFICATIONS
  • Education: Bachelor's degree in a Quantitative discipline. Ex: Data Science, Data Analytics, Applied Mathematics, Statistics, Computer Science, Operations Research, or related field
  • Experience: Between 5-8 years of relevant experience developing hypotheses, applying machine learning algorithms, validating results to analyze large datasets and extract actionable insights is required. Previous research or professional experience applying advanced analytic techniques to large, complex datasets.
  • Analytical Abilities: Strong knowledge in at least two of the following areas: machine learning, artificial intelligence, statistical modeling, data mining, information retrieval, or data visualization.
  • Technical Knowledge: Proven experience in developing and deploying predictive analytics projects using one or more leading languages (Python, R, Scala, etc.).
  • Communication Skills: Ability to translate data analysis and findings into coherent conclusions and actionable recommendations to business partners, practice leaders, and executives. Strong oral and written communication skills.

PREFERRED QUALIFICATIONS
  • Education:
    • Masters, or PhD in a Quantitative discipline.
  • Experience:
    • Strong Python proficiency with scikit-learn, pandas, numpy
    • 3+ years experience building and shipping real-world business applications of classification/regression models in production
    • Expertise in feature engineering and model evaluation and tuning
    • Experience with MLOps tools (MLFlow or Azure ML)for model tracking and deployment
    • Ability to communicate technical concepts clearly to non-technical stakeholders
    • Familiarity with time series or volatility analysis , clustering, decision tree learning, artificial neural networks etc.
    • Experience with imbalanced classification problems
    • Nice to have - Domain knowledge in energy, finance, or commodity pricing

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