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Part Time Data Scientist Machine Learning Jobs (NOW HIRING)

Data Scientist

Washington, DC · On-site

$112K - $257K/yr

Knowledge of statistical analysis, predictive modeling, and machine learning methods * Ability to ... Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible ...

As a Data Scientist at Key Cyber Solutions, your primary responsibility will be to analyze and ... Strong understanding of statistical analysis, data mining, and machine learning algorithms.

Data Scientist

Alexandria, VA · On-site

$77K - $176K/yr

Bachelor's degree Nice If You Have: * 2+ years of experience with Machine Learning, AI or NLP ... Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible ...

Data Scientist

Alexandria, VA · On-site

$77K - $176K/yr

Bachelor's degree Nice If You Have: * 2+ years of experience with Machine Learning, AI, or NLP ... Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible ...

Data Scientist

Reston, VA · On-site

$77K - $176K/yr

Knowledge of statistical analysis, predictive modeling, and machine learning methods * Ability to ... Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible ...

Data Scientist

Alexandria, VA · On-site

$99K - $225K/yr

Ability to apply supervised and unsupervised machine learning algorithms on structured and ... Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible ...

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Part Time Data Scientist Machine Learning information

See salary details

$37.5K

$122.7K

$196.5K

How much do part time data scientist machine learning jobs pay per year?

As of Jul 6, 2026, the average yearly pay for part time data scientist 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.

How do part-time data scientists specializing in machine learning typically collaborate with full-time teams and stakeholders?

Part-time data scientists in machine learning roles often work closely with full-time team members through regular meetings, collaborative project management tools, and clear documentation. They may be responsible for specific components of a project, such as data preprocessing, model development, or evaluation, and are expected to provide frequent updates and integrate their work with the broader team’s efforts. Effective communication and proactive time management are key, as part-time professionals usually need to balance their limited hours with project milestones and cross-functional collaboration. This structure allows part-time data scientists to contribute significant value while maintaining flexibility.

What is the difference between Part Time Data Scientist Machine Learning vs Part Time Data Analyst?

AspectPart Time Data Scientist Machine LearningPart Time Data Analyst
Required CredentialsDegree in Data Science, Computer Science, or related field; knowledge of machine learning algorithmsDegree in Statistics, Mathematics, or related field; proficiency in data visualization and basic analytics
Work EnvironmentFocus on developing predictive models, machine learning algorithms, and advanced analyticsData cleaning, reporting, and descriptive analysis of datasets
Employer & Industry UsageTech companies, finance, healthcare, and industries leveraging AI and predictive analyticsRetail, marketing, finance, and other sectors requiring data reporting and insights

Part Time Data Scientist Machine Learning roles focus on building predictive models and applying machine learning techniques, requiring specialized skills and advanced knowledge. In contrast, Part Time Data Analysts primarily handle data cleaning, reporting, and descriptive analysis. Both roles are essential but differ in complexity and technical depth.

What are the key skills and qualifications needed to thrive as a Part Time Data Scientist Machine Learning, and why are they important?

To thrive as a Part Time Data Scientist in Machine Learning, you need a solid background in statistics, programming (typically Python or R), and a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks like scikit-learn, TensorFlow, or PyTorch, as well as experience with data visualization tools and cloud platforms, is highly valued. Strong analytical thinking, problem-solving abilities, and effective communication skills help you translate complex data insights for stakeholders and collaborate within teams. These competencies ensure you can develop accurate models, deliver actionable results efficiently, and adapt to the dynamic needs of part-time project work.

What does a part time data scientist specializing in machine learning do?

A part time data scientist with a focus on machine learning uses statistical methods and algorithms to analyze data and build predictive models, typically on a flexible or reduced schedule. They work with datasets to extract insights, clean and prepare data, train machine learning models, and help organizations make data-driven decisions. Their responsibilities may include collaborating with teams, presenting findings, and deploying models, but on a part-time basis, allowing for work-life balance or the pursuit of additional projects. This role is ideal for those seeking to contribute their expertise without committing to a full-time position.
What cities are hiring for Part Time Data Scientist Machine Learning jobs? Cities with the most Part Time Data Scientist Machine Learning job openings:
What are the most commonly searched types of Data Scientist Machine Learning jobs? The most popular types of Data Scientist Machine Learning jobs are:
What states have the most Part Time Data Scientist Machine Learning jobs? States with the most job openings for Part Time Data Scientist Machine Learning jobs include:
Data Scientist, Reinforcement Learning

Data Scientist, Reinforcement Learning

ExxonMobil

Spring, TX

Part-time

Medical, Dental, Vision, Life, Retirement

Posted 7 days ago


ExxonMobil rating

6.1

Company rating: 6.1 out of 10

Based on 224 frontline employees who took The Breakroom Quiz

53rd of 74 rated oil and gas companies


Job description

Your role on our team

Pioneer the application of reinforcement learning (RL) and sequential decision-making to high-impact challenges across ExxonMobil's upstream, downstream, and commercial operations.


Collaborate with engineers, scientists, and business stakeholders to turn complex operational and planning problems into deployable, production-grade RL solutions.


Advance the organization's capabilities in reinforcement learning, decision optimization, and autonomous control as part of the Modeling, Optimization, and Data Science (MODS) team.

What you will do
  • Design, develop, and deploy reinforcement learning solutions for real-world energy applications such as production optimization, process control, supply chain scheduling, drilling optimization, and resource allocation.
  • Formulate sequential decision problems by defining state spaces, action spaces, reward structures, transition dynamics, and operational constraints with domain experts.
  • Develop RL agents using model-free methods (e.g., PPO, SAC, TD3, DQN where appropriate) and model-based approaches, selecting methods based on problem requirements, safety, and data availability.
  • Build and use simulation environments and digital twins for offline training, policy evaluation, and validation before real-world deployment.
  • Apply safe and constrained RL techniques to ensure agents operate within operational and safety limits.
  • Integrate RL solutions with existing optimization, simulation, and control systems across real-time and planning use cases.
  • Partner with data scientists and ML engineers to operationalize solutions, including training pipelines, monitoring, retraining, and performance tracking.
  • Benchmark RL against traditional methods such as LP, MIP, heuristic search, MPC, and stochastic optimization to identify best-fit approaches.
  • Stay current with advances in offline RL, safe RL, multi-agent RL, hierarchical RL, and model-based RL.
  • Share knowledge, publish findings where appropriate, and mentor peers on RL best practices.
About you

Desired Skills:

  • Experienced AI/ML professional with strong expertise in reinforcement learning, sequential decision-making, optimization, and real-world deployment.
  • 5+ years of experience in AI/ML, optimization, or related fields, including at least 2 years in reinforcement learning, sequential decision-making, or optimal control.
  • Master's or PhD in Computer Science, Machine Learning, Operations Research, Control Theory, Robotics, Applied Mathematics, Engineering, or a related quantitative field.
  • Deep understanding of RL fundamentals, including MDPs, dynamic programming, temporal-difference learning, policy gradients, and actor-critic methods.
  • Proven experience building RL systems end-to-end, from environment and reward design through training, evaluation, and deployment.
  • Experience with simulation environments, digital twins, or system models.
  • Strong background in statistics, probability, optimization, control theory, and algorithm design.
  • Proficiency in Python, PyTorch and/or TensorFlow, plus RL tools such as Stable Baselines3, RLlib, and Gymnasium.
  • Strong communication and collaboration skills, including the ability to explain technical concepts to non-technical stakeholders.

Preferred Skills:

  • Experience applying RL or decision optimization in industrial domains such as process control, robotics, autonomous systems, supply chain, energy systems, or operations research.
  • Familiarity with offline (batch) RL, safe RL, and multi-agent RL.
  • Knowledge of model-based RL, MPC, and hybrid RL-control approaches.
  • Understanding of classical optimization methods and how RL complements them.
  • Experience with physics-informed or hybrid mechanistic/ML modeling and domain-informed reward or constraint design.
  • Familiarity with platforms such as Azure ML, Azure OpenAI, Databricks, and MLOps tools such as MLflow or Weights & Biases.
  • Experience in the energy industry or other asset-intensive, safety-critical sectors.
Your benefits

An ExxonMobil career is one designed to last. Our commitment to you runs deep: our employees grow personally and professionally, with benefits built on our core categories of health, security, finance, and life.
 

We offer you: 
 

  • Pension Plan: Enrollment is automatic and at no cost to you. The basic benefit is a monthly annuity to be paid to you in retirement for the rest of your life. 
  • Savings Plan: You can contribute between 6% and 20% of your pay and are encouraged to enroll right away. If you contribute at least 6% to your savings plan, the Company will contribute a 7% match. 
  • Workplace Flexibility: We have several programs such as “Flex your Day”, providing ad-hoc flexibility around when and where you work, as well as longer-term programs such as leaves of absence and part-time work.
  • Comprehensive medical, dental, and vision plans. 
  • Culture of Health: Programs and resources to support your wellbeing. 
  • Employee Health Advisory Program: Provides confidential professional counseling for you and your family, including tools and resources promoting mental health and resiliency at no additional cost to you. 
  • Disability Plan: Income replacement for when you cannot work due to illness or injury occurring on or off the job. Enrollment is automatic and at no cost to you.
     

More information on our Company’s benefits can be found at  www.exxonmobilfamily.com.
 

Please note benefits may be changed from time to time without notice, subject to applicable law.

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