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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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.