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Freelance Artificial Intelligence Machine Learning information

What is a freelance artificial intelligence machine learning professional?

Freelance Artificial Intelligence (AI) Machine Learning (ML) professionals are independent experts who develop, implement, and optimize AI and ML models for various clients and projects. They often work remotely or on a contract basis, assisting organizations with tasks such as data analysis, algorithm development, model training, and deployment. Freelance AI/ML specialists typically possess skills in programming languages like Python or R, and are familiar with frameworks such as TensorFlow or PyTorch. Their work ranges from building predictive models and automating processes to consulting on AI strategy and integrating machine learning into business solutions.

What are the key skills and qualifications needed to thrive as a freelance artificial intelligence machine learning specialist?

To thrive as a Freelance Artificial Intelligence/Machine Learning Specialist, you need a strong background in mathematics, statistics, programming (Python or R), and a solid understanding of machine learning algorithms, usually backed by a relevant degree or certifications. Familiarity with tools and frameworks like TensorFlow, PyTorch, scikit-learn, and cloud platforms such as AWS or Google Cloud is typically required. Strong problem-solving abilities, self-motivation, and effective client communication are crucial soft skills for success in freelance work. These skills ensure you can deliver high-quality, innovative solutions independently and maintain strong client relationships in a rapidly evolving field.

How do freelance AI/ML professionals typically manage client expectations and project scope?

Freelance AI/ML professionals often work with clients who may not fully understand the technical possibilities or limitations of machine learning solutions. A key challenge is clearly communicating what can be achieved within a given timeframe and budget, and then setting realistic milestones. This often involves regular check-ins, well-documented progress updates, and educating clients about the iterative nature of AI/ML development. Building trust and transparency is essential, as is being flexible to adapt project goals based on data availability or evolving client needs.

What is the difference between Freelance Artificial Intelligence Machine Learning vs Data Scientist?

AspectFreelance Artificial Intelligence Machine LearningData Scientist
CredentialsTypically requires knowledge of AI/ML, programming, and relevant certificationsRequires degrees in data science, statistics, or related fields, often with certifications
Work EnvironmentIndependent, project-based, remote or on-siteUsually employed full-time in organizations, but also freelance options exist
Industry UsageUsed across tech, finance, healthcare, and startups for AI/ML projectsCommon in tech, finance, healthcare, and research sectors for data analysis

Freelance Artificial Intelligence Machine Learning professionals focus on developing AI/ML models independently, often on specific projects, while Data Scientists analyze data to extract insights, typically within organizations. Both roles require strong technical skills, but their work environments and project scopes differ.

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For Freelance Artificial Intelligence Machine Learning jobs in Oklahoma, the most frequently searched job titles are:

What job categories do people searching Freelance Artificial Intelligence Machine Learning jobs in Oklahoma look for?

The top searched job categories for Freelance Artificial Intelligence Machine Learning jobs in Oklahoma are:

What cities in Oklahoma are hiring for Freelance Artificial Intelligence Machine Learning jobs?

Cities in Oklahoma with the most Freelance Artificial Intelligence Machine Learning job openings:

Freelance Artificial Intelligence Machine Learning jobs near you
Infographic showing various Freelance Artificial Intelligence Machine Learning job openings in Oklahoma as of July 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

Senior Engineer - Data Science

Continental Resources, Inc.

Oklahoma City, OK • On-site

Full-time

Re-posted 21 days ago

Job description

Job Summary
The Senior Engineer, Data Science is a hands-on technical role who designs, builds, and operationalizes advanced analytics and Artificial Intelligence/Machine Learning solutions that drive measurable value across subsurface, drilling and completions, production operations, HSE, and commercial functions at Continental Resources. This role partners with multidisciplinary stakeholders to translate business problems into data-driven solutions, develop robust models and pipelines, and deploy them to production with strong Machine Learning Ops and governance practices. The ideal candidate combines a Master of Science in Data Science with strong applied analytics capability, solid data engineering skills, and practical oil and gas domain experience comparable to a seasoned upstream engineering background.
Duties and Responsibilities
  • Leads the design, development, and deployment of Artificial Intelligence/Machine Learning solutions for upstream subsurface and well operations, including physics-informed and hybrid modeling approaches for reservoir, drilling, and production optimization.
  • Builds advanced Artificial Intelligence/Machine Learning solutions for commercial analytics use cases such as pricing, supply chain, marketing, and trading to improve profitability and decision speed.
  • Executes complex AI initiatives from ideation and discovery through model development, deployment, and sustainment as part of integrated, enterprise-level teams.
  • Architects and implements reliable data pipelines and features using modern data platforms (e.g., Databricks, cloud services), ensuring data quality, lineage, and performance for analytics workloads.
  • Applies Machine Learning Ops best practices to automate training, testing, deployment, monitoring, and model lifecycle management at scale in production environments.
  • Translates complex business problems into analytical approaches with clear hypotheses, success criteria, and measurable outcomes across upstream and commercial domains.
  • Develops and delivers communications that convey a clear understanding of technical concepts, model results, and business implications to diverse technical and non-technical audiences.
  • Builds strong partnerships and cross-functional relationships with geoscience, engineering, operations, commercial, IT, and leadership stakeholders to drive adoption and sustain business impact.
  • Gains the confidence and trust of others through honesty, integrity, and follow-through while championing responsible and secure use of data and AI.
  • Actively seeks new ways to grow and be challenged by staying current on emerging Artificial Intelligence/Machine Learning, generative AI, optimization, and computational techniques relevant to energy and integrating them where they add value.
  • Other duties as assigned.

Skills and Competencies
  • Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.
  • Action oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
  • Drives results - Consistently achieving results, even under tough circumstances.
  • Self-development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
  • Nimble learning - Actively learning through experimentation when tackling new problems, using both successes and failures as learning fodder.
  • Situational adaptability - Adapting approach and demeanor in real time to match the shifting demands of different situations.
  • Instills trust - Gaining the confidence and trust of others through honesty, integrity, and authenticity.

Required Qualifications
  • Bachelor of Science in Petroleum, Mechanical, Chemical, or related Engineering discipline from an accredited college or university and Master of Science in Data Science, or a closely related data science or analytics field, from an accredited college or university.
  • Minimum five (5) years of hands-on experience delivering production-grade data science/Machine Learning solutions, including end-to-end lifecycle from discovery to deployment and sustainment.
  • Proficiency in Python and SQL; experience with Machine Learning frameworks and tooling (e.g., scikit-learn, PyTorch/TensorFlow), and data platforms such as Databricks and cloud services.
  • Experience building and maintaining data pipelines and features and applying Machine Learning Ops practices for model deployment and monitoring in enterprise environments.
  • Demonstrated ability to partner with technical and business domains in energy, including upstream subsurface, drilling/completions, production operations, and/or commercial analytics such as pricing, supply chain, marketing, or trading.
  • An acceptable pre-employment background and drug test.

Preferred Qualifications
  • Oil and gas industry experience, particularly in upstream engineering, subsurface, drilling and completions, production operations, or commercial energy analytics.
  • Background in computational sciences, optimization, or high-performance computing for engineering applications.
  • Familiarity with enterprise data governance, security, and responsible AI practices in regulated environments.
  • Five (5) or more years of combined oil and gas engineering/domain experience and applied data science experience.

Physical Requirements and Working Conditions
  • Requires prolonged sitting, some bending and stooping.
  • Occasional lifting up to 25 pounds.
  • Manual dexterity sufficient to operate a computer keyboard and calculator.

Continental Resources, Inc. provides equal employment opportunities and access for all applicants and employees without regard to race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, national origin, age, disability, genetic information, veteran status, or any other category protected by law.

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