Lead Data Scientist - Energy Forecasting & RealTime

Compunnel Inc.; Florida; On-site; USD 120,000 - 150,000; Full time

Job summary

A leading technology firm is seeking a Lead Data Scientist to develop and deploy advanced forecasting models for renewable energy generation. This role involves designing machine learning pipelines, integrating data sources, and collaborating with cross-functional teams. Candidates should have advanced Python skills, experience in time-series forecasting, and knowledge of AWS services. Strong problem-solving and communication skills are also essential. Join in a dynamic and innovative environment focused on renewable energy solutions.

Qualifications

• Proven experience in machine learning model deployment with monitoring.
• Strong understanding of data pipelines and MLOps practices.
• Excellent analytical and problem-solving skills.

Responsibilities

• Lead forecasting model development for load, solar, and wind generation.
• Design and implement machine learning pipelines from data ingestion to deployment.
• Integrate external data sources into forecasting workflows.
• Develop scalable data pipelines for real-time operations.
• Build interactive dashboards for stakeholder communication.
• Collaborate with cross-functional teams in an Agile environment.

Skills

Python

Time-series forecasting

Machine learning deployment

SQL

Data visualization

APIs integration

AWS services

Problem-solving

Communication skills

Tools

pandas

NumPy

scikit-learn

statsmodels

Streamlit

Plotly

Jira

Confluence

Job description

We are seeking a Lead Data Scientist to design, develop, and deploy advanced forecasting models for load, solar, and wind generation.

This role involves end-to-end ownership of the model lifecycle, from data ingestion and feature engineering to production deployment and monitoring, supporting real-time operational and strategic decision-making.

Key Responsibilities

• Lead the development and deployment of forecasting models for load, solar, and wind generation.
• Design and implement end-to-end machine learning pipelines, including feature engineering, model training, and evaluation.
• Integrate external data sources such as weather APIs into forecasting workflows.
• Develop scalable data pipelines for real-time and batch forecasting operations.
• Deploy models to production environments with monitoring, alerting, and recovery mechanisms.
• Build automated retraining and evaluation frameworks to ensure model performance and accuracy.
• Create interactive dashboards and visualizations for stakeholder communication.
• Collaborate with cross-functional teams including operations, engineering, and business stakeholders.
• Participate in Agile development processes and contribute to continuous improvement initiatives.
• Ensure delivery of cost-effective, high-quality forecasting solutions within operational timelines.

Required Qualifications

• Advanced proficiency in Python, including libraries such as pandas, NumPy, scikit-learn, and statsmodels.
• Strong experience in time-series forecasting techniques such as ARIMA, SARIMAX, or gradient boosting methods.
• Proven experience deploying machine learning models into production environments with monitoring and maintenance.
• Experience integrating APIs and external data sources into data pipelines.
• Working knowledge of AWS services such as EC2, S3, Lambda, or SageMaker.
• Strong SQL skills with experience handling large datasets and optimizing queries.
• Experience building dashboards using tools such as Streamlit, Plotly, or similar.
• Strong understanding of data pipelines, version control (e.g., Git), and MLOps practices.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills in cross-functional environments.

Preferred Qualifications

• Experience with advanced feature engineering, uncertainty quantification, and probabilistic forecasting.
• Domain knowledge in energy markets or renewable generation forecasting.
• Experience working with Agile tools such as Jira or Confluence.
• Familiarity with evaluation metrics such as MAE, RMSE, and MAPE.