Data Scientist

Insight Global is seeking a Data Scientist to support a Renewable Energy Client! This opportunity is based out of Charlotte, NC, and will be on a hybrid meeting schedule. Additionally, this opportunity offers quick interviews, competitive rates (ranging from $100K - $145K based off of years of experience) and there is lots of stability and room for growth, as this is a contract to hire opportunity.

Must Haves

Ability to work on a W2 basis without sponsorship

Bachelor’s or master's degree in data science, Computer Science, Engineering, Statistics, or a related field

5+ years of hands-on experience as a Data Scientist in an industrial setting.

Strong programming skills in Python, SQL.

Experience with machine learning frameworks (TensorFlow, scikit-learn, PyTorch, Keras) and data visualization tools (Tableau, Power BI, Matplotlib).

Expertise in working with time-series data and statistical modeling.

Familiarity with cloud platforms (AWS, Azure, Google Cloud) and big data technologies (Spark, Hadoop).

Knowledge of renewable energy systems, grid optimization, and forecasting models.

Strong problem-solving and communication skills.

Pluses:

Experience with IoT and sensor data analysis.

Background in energy market analysis and financial modeling.

Understanding of regulatory policies affecting renewable energy.

Job Description

We are seeking a highly skilled Data Scientist with expertise in the renewable energy sector to drive data-driven decision-making and optimize energy generation, storage, and distribution. The ideal candidate will leverage advanced analytics, machine learning, and deep learning modeling to improve efficiency, forecast demand, and support sustainability goals.

Responsibilities

Develop and deploy machine learning/deep learning models to predict energy production and enhance asset performance.

Analyze large datasets from solar, wind, battery storage, and other renewable energy sources to identify trends and actionable insights.

Collaborate with engineers, analysts, and business stakeholders to integrate data science solutions into operations and strategy.

Implement predictive maintenance models to reduce downtime and improve asset reliability.

Utilize geospatial and weather data for energy forecasting and site selection.

Design and optimize algorithms for energy trading, load balancing, and grid stability.

Build and maintain scalable data pipelines to process real-time and historical energy data.

Communicate findings through data visualizations, dashboards, and reports for executive decision-making.

Stay up to date with advancements in AI, machine learning, and energy analytics to drive innovation.