Data Scientist

Context Labs • Houston, TX

20h • Hybrid

About The Position

Context Labs is seeking an early-career Data Scientist to support deployment of its Decarbonization as a Service™ platform, built on Immutably™ Data Fabric technology. The role sits at the intersection of data science, engineering, scientific computing, product development, and emissions-domain application. The Data Scientist will work with complex energy and emissions data and collaborate with application engineers, data engineers, and product teams to turn engineering methodologies and research ideas into robust, scalable analytical capabilities. The position reports to the Data Science Architect.

The role calls for strong quantitative reasoning, Python and SQL proficiency, and the curiosity and initiative to learn unfamiliar technologies, emissions methodologies, and energy-industry concepts. Applicants with engineering or scientific backgrounds and strong programming and data-analysis skills are encouraged to apply, including those whose previous title was not Data Scientist.

Context Labs builds industrial AI data infrastructure for global markets, helping energy and industrial companies produce trusted, defensible data and AI outputs. Its platform turns fragmented operational and emissions data into continuously proven information while sensitive data remains within each company’s environment. The company grew out of research at MIT and has offices in Amsterdam, Cambridge, Massachusetts, and Houston, Texas.

Requirements

• 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 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.

Nice To Haves

• 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.

Responsibilities

• 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, 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.
• 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 limitations of available data and methodologies.
• Work within an ontology- and knowledge-graph-based framework to connect data to the entities, relationships, and concepts it represents.
• 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.
• Collaborate across Data Science, Data Engineering, Application Engineering, Product, R&D, and Customer Experience teams.
• Perform other duties as may reasonably be required by the company.

Benefits

• Reasonable accommodation for individuals with disabilities to participate in the job application or interview process, perform essential job functions, and receive other benefits and privileges of employment.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level