Responsibilities
- Develop robust Python applications leveraging NumPy and pandas for data analysis and transformation.
- Write and maintain Docker configurations to containerize applications and support CI/CD pipelines. Integrate with S3, Azure Data Lake, and Databricks Unity Catalog for efficient data ingestion and retrieval.
- Collaborate with data scientists to integrate machine learning models into production environments.
- Follow and contribute to coding standards, architecture guidelines, and best practices.
- Maintain SOC2 compliant SDLC practices and workflow using Git and Jira.
- Troubleshoot and resolve performance issues, ensuring code quality and maintainability.
- Participate in code reviews and share knowledge with team members.
Requirements
- 2–4 years of software development experience, preferably in Python.
- Proficiency in Python; familiarity with other languages (e.g., C++, JavaScript) is a plus.
- Solid understanding of NumPy, pandas, and their performance considerations.
- Familiarity with statistics, mathematics, or machine learning principles.
- Experience creating and managing Docker images; familiarity with CI/CD tools.
- Familiarity with Microsoft Azure (especially Blob Storage); experience with AWS or GCP is a plus.
- Experience in version control (Git), continuous integration, and automated testing frameworks.
- Experience supporting the deployment and maintenance of AI/ML models in production, working alongside data scientists.
- Solid analytical skills with the ability to break down business rules into clear, maintainable code.
- Good communication skills with an ability to collaborate effectively across teams.
Team
Structure: The Enverus Data Science team builds advanced data-driven products that drive value across the energy value chain. We combine AI, machine learning, statistical methods, advanced algorithms, and deep domain expertise to tackle complex industry challenges. Our models are deployed on a robust internal data science platform, enabling scalable solutions.
