ML Engineer; Renewable Energy Systems

The client is a U.S.

-based renewable energy company founded in 2012, headquartered in Washington, D.C. Their mission is to make 100% clean, pollution‑free energy accessible to homes and businesses—without requiring hardware installations or utility provider changes. Customers continue using their existing utility, while the company ensures their electricity consumption is offset by renewable sources such as regional wind and solar.

The project focuses on building, maintaining, and optimizing data pipelines and machine learning models to support business intelligence, forecasting, and energy consumption analytics. The goal is to modernize data flows by transitioning pipelines from Redshift to Snowflake, enhance orchestration using modern workflow tools, and improve model accuracy and performance for better decision‑making and customer experience.

• SQL
• Snowflake
• Redshift
• Python
• Num Py
• Sci Py
• Scikit-learn
• Jupyter
• Git
• Git Hub
• FastAPI

• Rebuild and translate data flows from Redshift into Snowflake;
• Redesign and maintain data pipelines with workflow orchestration tools;
• Develop and enhance ML models including time series forecasting, classification, regression, and computer vision;
• Perform feature engineering and data preparation for ML tasks;
• Build and maintain APIs for model deployment and data access using FastAPI or similar;
• Conduct and participate in code reviews;
• Contribute to knowledge sharing through internal seminars and documentation;

• 3+ years of experience in data science or machine learning engineering roles;
• Strong proficiency in SQL (Snowflake, Redshift);
• Proficiency in Python data science stack:
  Pandas, Num Py, Sci Py, Scikit-learn, Jupyter;
• Hands‑on experience with Git version control (Git Hub);
• Experience developing and supporting ML pipelines and APIs (FastAPI, Flask);
• Strong written and verbal communication skills;
• English level: B2 or higher;

• Experience with Snowflake Snowpark and MLflow (or similar experiment tracking tools);
• Familiarity with dbt for data transformations;
• Knowledge of data QA tools (e.g., Great Expectations, Soda);
• Experience with AWS Lambda, Glue, and Docker;
• Experience with orchestration frameworks such as Dagster or Airflow;
• Strong business acumen and statistical reasoning, especially in causal inference;

The global benefits package includes:

• Technical and non-technical training for professional and personal growth;
• Internal conferences and meetups to learn from industry experts;
• Support and mentorship from an experienced employee to help you professional grow and development;
• Internal startup incubator;
• Health insurance;
• Sports activities to promote a healthy lifestyle;
• Flexible work options, including remote and hybrid opportunities;
• Referral program for bringing in new talent;
• Work anniversary program and additional vacation days.

Didn't find anything suitable?

We're always starting new projects and we'd love to work with you. Please send your CV and we'll get in touch.