Data Scientist
Overview:
The information below covers the role requirements, expected candidate experience, and accompanying qualifications. As a Data Scientist at Context Labs, you will help deploy our Decarbonization as a Service platform built on Immutably Data Fabric. You’ll bridge data science, engineering, and emissions domain work to build scalable analytical capabilities from real-world energy data. You’ll partner with engineers, product teams, and domain experts to translate methodologies into robust solutions, while learning new tools and advancing our mission to transform complex data into proven information. You’ll contribute to cross-functional projects that drive carbon reduction and credible analytics.
Responsibilities:
- Translate engineering methodologies and emissions requirements into computational solutions
- Develop, validate, and document robust emissions calculations and analyses
- Explore and clean complex datasets, identify data quality issues, and resolve discrepancies
- Build scalable analytics workflows using Python, SQL, and PySpark with data engineers
- Generalize customer-specific approaches into reusable software 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
- Write modular Python code, participate in Git workflows, testing, and documentation
- Apply machine learning (regression, classification, time-series, anomaly detection) where appropriate
- Collaborate with Data Science, Data Engineering, Application Engineering, Product, R&D, and Customer Experience teams
Key requirements:
- Professional experience in data science, scientific computing, or related quantitative work
- Strong Python and SQL skills with production-quality code
- Solid quantitative and analytical problem-solving abilities
- Knowledge of statistics (distributions, descriptive stats, xaygatp regression, validation)
- Experience investigating messy datasets and data-quality issues
- Familiarity with Git, code review, testing, and documentation practices
- Strong initiative and ability to learn new technical topics and own new problems
- Ability to communicate analytical approaches and results to both technical and non-technical stakeholdersBS or MS in Engineering, Physics, Applied Mathematics, Data Science, or related quantitative fieldstrong initiativeeffective communicationcuriosity and self-motivation
PythonSQLPySpark