Data Scientist

Context Labs • Houston, TX

13d • 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 works across data science, engineering, scientific computing, product development, and emissions-domain applications. 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 involves developing and validating reproducible emissions calculations, investigating customer data, implementing scalable analytical workflows, and helping turn customer-specific approaches and research into reusable product capabilities. It also applies statistical and machine-learning methods where appropriate and works within an ontology- and knowledge-graph-based framework. Context Labs welcomes candidates with relevant applied research or project experience, including those from engineering or scientific backgrounds who have developed programming and data-analysis skills.

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.
• 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 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.
• 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 limitations in 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.

What This Job Offers

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