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Part Time Data Scientist Jobs in Spring, TX (NOW HIRING)

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Part Time Data Scientist information

See Spring, TX salary details

$33.4K

$109.2K

$174.9K

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

As of Jun 22, 2026, the average yearly pay for part time data scientist in Spring, TX is $109,224.00, according to ZipRecruiter salary data. Most workers in this role earn between $87,700.00 and $121,000.00 per year, depending on experience, location, and employer.

What Does a Part-Time Data Scientist Do?

A part-time data scientist’s skills and responsibilities are very similar to those of a data analyst. Like a data analyst, you have to collect and review company or industry data and then perform an analysis based on a business question the company wants to answer. You share this information with other parts of the enterprise. Where data scientists’ duties are different is that you use past data to build machine learning models. These machine learning models make predictions, and the goal is to identify trends that lead to a more thorough understanding of the business and its customers.

What is a part time data scientist?

A part time data scientist is a professional who applies statistical analysis, machine learning, and data processing skills to extract insights from data, but works fewer hours than a full-time employee—typically under 35 hours per week. Part time data scientists may work for one or more companies, often on flexible schedules or as contractors. Their responsibilities can include data cleaning, building predictive models, and presenting data-driven insights to help organizations make informed decisions. This role is ideal for those seeking work-life balance, students, or professionals looking to supplement their income.

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

To thrive as a Part Time Data Scientist, you need strong analytical skills, proficiency in statistics, and experience with data modeling, typically supported by a degree in a quantitative field. Familiarity with tools like Python, R, SQL, and data visualization platforms, as well as knowledge of machine learning libraries, is often required. Excellent problem-solving, time management, and communication skills help you effectively deliver insights and collaborate despite reduced hours. These skills ensure you can provide high-impact, actionable analyses efficiently within a limited work schedule.

How does working part-time as a data scientist typically impact project involvement and collaboration with full-time team members?

As a part-time data scientist, you may be assigned to specific projects or tasks that fit within your available hours, often focusing on defined deliverables or analytical support roles. Collaboration with full-time colleagues is common, usually through regular meetings, shared documentation, and communication tools to ensure alignment. It's important to clearly communicate your schedule and capacity, as timely updates and hand-offs help maintain project momentum. While you might not be involved in every stage of a project, your specialized contributions are highly valued, and many organizations foster flexible, inclusive environments to integrate part-time professionals effectively.

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

AspectPart Time Data ScientistData Analyst
Required CredentialsBachelor's or Master's in Data Science, Statistics, or related fieldBachelor's degree in Data Analysis, Statistics, or related field
Work EnvironmentFlexible hours, project-based, often remoteOffice or remote, regular hours, structured projects
Employer & Industry UsageTech companies, startups, consulting firmsBusiness, finance, marketing, healthcare
Common Search & ComparisonPart Time Data Scientist vs Data Analyst

Part Time Data Scientists focus on advanced analytics, machine learning, and modeling, often requiring higher technical skills and specialized knowledge. Data Analysts typically handle data cleaning, reporting, and visualization. While both roles analyze data, Part Time Data Scientists work on complex models and predictive analytics, whereas Data Analysts focus on descriptive insights. The choice depends on your skills and career goals within data roles.

More about Part Time Data Scientist jobs
What are the most commonly searched types of Data Scientist jobs in Spring, TX? The most popular types of Data Scientist jobs in Spring, TX are:
What are popular job titles related to Part Time Data Scientist jobs in Spring, TX? For Part Time Data Scientist jobs in Spring, TX, the most frequently searched job titles are:
What cities near Spring, TX are hiring for Part Time Data Scientist jobs? Cities near Spring, TX with the most Part Time Data Scientist job openings:
Part Time Data Scientist jobs near you
Infographic showing various Part Time Data Scientist job openings in Spring, TX as of June 2026, with employment types broken down into 100% Part Time. Highlights an 92% In-person, and 8% Remote job distribution, with an average salary of $109,224 per year, or $52.5 per hour.
Data Scientist, Reinforcement Learning

Data Scientist, Reinforcement Learning

ExxonMobil

Spring, TX • On-site

Part-time

Medical, Dental, Vision, Life, Retirement

Posted 22 days ago

ExxonMobil rating

6.1

Company rating: 6.1 out of 10

Based on 221 frontline employees who took The Breakroom Quiz

55th 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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