Job description

Data Scientist - Energy (Oil & Gas) Houston, TX (Hybrid) About this position: Context Labs is seeking an early-career Data Scientist to support the deployment of Context Labs’ Decarbonization as a Service™ platform, built on our Immutably™ Data Fabric technology. This role sits at the intersection of data science, engineering, scientific computing, product development and real-world emissions domain application. You will work with complex, real-world energy and emissions data and collaborate with application engineers, data engineers, and product teams to translate engineering methodologies and research ideas into robust, scalable analytical capabilities. We do not expect you to arrive knowing every technology, emissions methodology, or aspect of the energy industry. We seek strong quantitative reasoning, Python and SQL proficiency, and a self-driven curiosity to learn new technologies and own complex challenges. This position reports to the Data Science Architect. In this role you will : Work with domain experts to understand engineering and emissions methodologies, assumptions, and requirements and translate them into computational solutions. Develop, implement, validate, and document robust and reproducible emissions calculations and analytical methodologies. Explore complex customer datasets, understand their structure and meaning, identify data-quality issues, and investigate discrepancies. Implement scalable analytical workflows using Python, SQL, and PySpark, working with data engineers to move solutions into repeatable production workflows Help generalize customer-specific calculations, research ideas, and analytical prototypes into reusable software and product capabilities. Apply statistical methods to validate results, quantify uncertainty, and understand the limitations of available data and methodologies. Work within an ontology- and knowledge-graph-based framework to connect data to the entities, relationships, and concepts they represent. Write maintainable, modular Python code and contribute through Git, code review, testing, and documentation. Apply machine learning methods, including regression, classification, time-series analysis, and anomaly detection, when appropriate to the problem. Collaborate across Data Science, Data Engineering, Application Engineering, Product, R&D, and Customer Experience teams. What we’re looking for : Relevant professional experience in data science, scientific computing, engineering analytics, or related quantitative work. Candidates with an advanced degree and substantial applied research or project experience will also be considered. Strong Python and SQL skills, including experience developing maintainable, modular code beyond exploratory notebooks and working with complex, real-world datasets. Strong quantitative and analytical problem-solving skills, including comfort reasoning about equations, assumptions, data relationships, and statistical results. Working knowledge of statistics, including distributions, descriptive statistics, regression, and statistical validation. Experience investigating messy or heterogeneous datasets, identifying data-quality issues, and developing defensible analytical approaches. Familiarity with collaborative software-development practices such as Git, code review, testing, and documentation. Strong initiative and demonstrated ability to learn unfamiliar technical subjects, ask effective questions, seek out relevant expertise, and progressively take ownership of new problems. Ability to communicate analytical approaches, assumptions, and results clearly to technical and non-technical colleagues. BS or MS degree in Engineering, Physics, Applied Mathematics, Data Science, or another related quantitative scientific or technical discipline. Bonus if you have : Experience working with energy, utilities, industrial, environmental, or emissions-related data, including greenhouse-gas or methane analytics. Familiarity with engineering or physical-system concepts such as mass and energy balances, thermodynamics, or process modeling. Experience with PySpark, Spark, Delta Lake, or other distributed data-processing technologies. Experience with uncertainty quantification, Monte Carlo methods or implementing scientific, engineering, or regulatory calculation methodologies in software. Knowledge of ontologies, knowledge graphs, or semantic modeling. Why you’ll like it here: Context Labs is collaborative at its core. You’ll work within your team and across the organization allowing for continuous learning and discovery. We set goals that matter and provide value in all that we do, from building meaningful products to positively impacting carbon reduction and climate change. Context Labs’ mission is to transform complex data into continuously proven information. Powered by Immutably™, an innovative, trusted data fabric platform, our integrated solutions deliver context-driven insights that enable human networks to spot patterns, correlate trends - and reveal the ground truth hidden inside the world’s most complicated, persistent problems. We understand that applying for a job can be intimidating. Applicants rarely meet every single job requirement, and we know there are many skills and backgrounds that will contribute to success in this role. If you’re interested in applying, please do so. If you come from an engineering or scientific background and have developed strong programming and data-analysis skills, we encourage you to apply even if your previous title has not been “Data Scientist.” Context Labs embraces diversity and equal opportunity. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. We believe the more inclusive we are, the better our products will be. Context Labs will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please contact Human Resources at HR@contextlabs.com. Who we are: Context Labs builds industrial AI data infrastructure for global markets, giving energy and industrial companies trusted data and AI outputs they can defend. Its platform turns fragmented operational and emissions data into continuously proven information, while sensitive data stays within each company’s own environment. Because the data is verified and the workflows validated, one platform can support any output, from carbon management and compliance to commercial use. The company grew out of research at MIT and is led by a team that helped build the internet at scale through earlier companies. Context Labs has offices in Amsterdam, Cambridge, Mass., and Houston, TX. Learn more at www.contextlabs.com .

Requirements

Must-haves

Data Scientist - Energy (Oil & Gas)
Houston, TX (Hybrid)
Context Labs is seeking an early-career Data Scientist to support the deployment of Context Labs’ Decarbonization as a Service™ platform, built on our Immutably™ Data Fabric technology.
Work with domain experts to understand engineering and emissions methodologies, assumptions, and requirements and translate them into computational solutions.
Develop, implement, validate, and document robust and reproducible emissions calculations and analytical methodologies.
Explore complex customer datasets, understand their structure and meaning, identify data-quality issues, and investigate discrepancies.
Implement scalable analytical workflows using Python, SQL, and PySpark, working with data engineers to move solutions into repeatable production workflows
Help generalize customer-specific calculations, research ideas, and analytical prototypes into reusable software and product capabilities.
Apply statistical methods to validate results, quantify uncertainty, and understand the limitations of available data and methodologies.
Work within an ontology- and knowledge-graph-based framework to connect data to the entities, relationships, and concepts they represent.
Write maintainable, modular Python code and contribute through Git, code review, testing, and documentation.
Apply machine learning methods, including regression, classification, time-series analysis, and anomaly detection, when appropriate to the problem.
Collaborate across Data Science, Data Engineering, Application Engineering, Product, R&D, and Customer Experience teams.
Strong Python and SQL skills, including experience developing maintainable, modular code beyond exploratory notebooks and working with complex, real-world datasets.
Strong quantitative and analytical problem-solving skills, including comfort reasoning about equations, assumptions, data relationships, and statistical results.
Working knowledge of statistics, including distributions, descriptive statistics, regression, and statistical validation.
Experience investigating messy or heterogeneous datasets, identifying data-quality issues, and developing defensible analytical approaches.
Familiarity with collaborative software-development practices such as Git, code review, testing, and documentation.
Strong initiative and demonstrated ability to learn unfamiliar technical subjects, ask effective questions, seek out relevant expertise, and progressively take ownership of new problems.
Ability to communicate analytical approaches, assumptions, and results clearly to technical and non-technical colleagues.
BS or MS degree in Engineering, Physics, Applied Mathematics, Data Science, or another related quantitative scientific or technical discipline.
Experience working with energy, utilities, industrial, environmental, or emissions-related data, including greenhouse-gas or methane analytics.
Familiarity with engineering or physical-system concepts such as mass and energy balances, thermodynamics, or process modeling.
Experience with PySpark, Spark, Delta Lake, or other distributed data-processing technologies.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

pythonsqlmachine learningai

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