Job Description:
About the Role
As an Energy Data Scientist, you will play a key role in analyzing and optimizing energy systems, predicting demand, and supporting sustainability initiatives through advanced data analytics and machine learning. This position requires a combination of strong analytical skills, knowledge of energy systems, and expertise in machine learning to enhance energy efficiency and innovation.
Responsibilities
- Collect, clean, and analyze large datasets from energy sources (e.g., grid data, renewable sources, smart meters).
- Develop predictive models for energy consumption, generation forecasting, and load balancing.
- Use machine learning and statistical models to optimize energy distribution and storage.
- Perform time-series analysis to predict demand fluctuations and prevent energy shortfalls or surpluses.
- Work closely with engineering teams to implement data-driven solutions across energy systems.
- Conduct exploratory data analysis (EDA) to uncover insights and recommend actionable strategies.
- Develop and maintain data pipelines, ensuring real-time access to data for model training and validation.
- Communicate findings and present data insights to stakeholders and decision-makers.
Required Qualifications
- Education: Bachelors degree in Data Science, Computer Science, Engineering, Statistics, or a related field (Master's or Ph.D. preferred).
- Experience: 3+ years of experience in data science, with a focus on energy or utilities preferred.
- Technical Skills:
- Proficiency in Python, R, SQL, and data visualization tools (e.g., Tableau, Power BI).
- Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
- Strong understanding of time-series analysis, statistical modeling, and predictive analytics.
- Familiarity with big data tools like Hadoop, Spark, or cloud-based solutions (e.g., AWS, Azure).
- Domain Knowledge: Understanding of energy systems, power grids, and renewable energy sources is a strong advantage.
Preferred Skills
- Experience with optimization algorithms and real-time data processing.
- Familiarity with industrial IoT data and sensor technologies.
- Knowledge of carbon emissions tracking and sustainability metrics.
- Ability to communicate complex technical findings to non-technical stakeholders.