Energy Analytics Data Scientist | Decarbonization Platform

Context Labs BV; Houston (TX); On-site; USD 90,000 - 120,000; Full time

Job summary

Context Labs seeks an early-career Data Scientist to support the Decarbonization as a Service platform built on the Immutably Data Fabric. You will join a cross-disciplinary team to translate engineering methods into robust, scalable analytical capabilities using Python, SQL, and PySpark on real energy and emissions data.

The role emphasizes learning and ownership, with collaboration across Data Science, Engineering, Product, and R&D teams, reporting to the Data Science Architect.

Qualifications

• Strong Python and SQL skills, with maintainable, modular code beyond notebooks.
• Experience with real-world datasets and applying quantitative methods.
• Familiarity with statistics, regression, distributions and validation.
• Knowledge of collaborative software practices (Git, code reviews, testing, documentation).
• Ability to learn unfamiliar technical subjects and own new problems.

Responsibilities

• Translate engineering methodologies and research ideas into computational solutions.
• Develop, validate, and document robust emissions calculations and analyses.
• Explore complex customer datasets and identify data-quality issues.
• Implement scalable analytical workflows using Python, SQL, and PySpark with data engineers.
• Generalize calculations and prototypes into reusable software and product capabilities.
• Apply statistical methods to validate results and quantify uncertainty.
• Work within an ontology- and knowledge-graph framework to connect data to entities and concepts.

Skills

Python

SQL

PySpark

Statistics

Data science

Problem solving

Education

BS or MS in Engineering/Physics/Applied Math/Data Science

Tools

Git

Spark

Delta Lake

Job description

Context Labs seeks an early-career Data Scientist to support the Decarbonization as a Service platform built on the Immutably Data Fabric. You will join a cross-disciplinary team to translate engineering methods into robust, scalable analytical capabilities using Python, SQL, and PySpark on real energy and emissions data.

The role emphasizes learning and ownership, with collaboration across Data Science, Engineering, Product, and R&D teams, reporting to the Data Science Architect.