Remote Data Scientist - Energy Analytics

Placements24; Roanoke (VA); Hybrid; USD 120,000 - 170,000; Full time

Benefits offered by this job

Remote work

Job summary

Placements24 is seeking an innovative Data Scientist with a strong background in energy analytics to join their fully remote team. You will extract actionable insights from energy consumption, production, and market data, using advanced modeling, ML, and visualization to optimize energy strategies for clients.

The role emphasizes collaboration with engineers and domain experts, experiments to validate models, and staying current with energy analytics research.

Qualifications

• Master's or PhD in Data Science, Statistics, Computer Science, Physics, or a related quantitative field.
• 5+ years of experience as a Data Scientist, with a significant focus on applying ML to real-world problems.
• Proficiency in Python or R, and libraries such as scikit-learn, TensorFlow, PyTorch.
• Strong experience with SQL and database management.
• Experience in energy data analysis, time-series forecasting, or related areas.

Responsibilities

• Develop and implement statistical models and machine learning algorithms for energy forecasting, anomaly detection, and optimization.
• Clean, process, and analyze large, complex datasets from diverse sources within the energy domain.
• Create compelling data visualizations and dashboards to communicate findings to technical and non-technical stakeholders.
• Collaborate with domain experts and engineers to define KPIs and business needs.
• Design and conduct experiments to test hypotheses and validate models.
• Stay current with advancements in data science, machine learning, and energy analytics research.

Skills

ML & DS

Energy analytics

Python

R

Time-series

SQL

Education

Master's degree

PhD

Tools

scikit-learn

TensorFlow

PyTorch

Job description

About the Role

Our client is seeking an innovative Data Scientist with a strong background in energy analytics to join their fully remote team. This role is central to extracting actionable insights from complex datasets related to energy consumption, production, and market dynamics. You will employ advanced statistical modeling, machine learning techniques, and data visualization tools to uncover patterns, predict future trends, and optimize energy strategies for clients. This position offers the ideal candidate the chance to work on impactful projects, leverage cutting-edge technology, and contribute significantly to the evolution of the energy sector , all while enjoying the benefits of remote work.

Key Responsibilities

• Develop and implement statistical models and machine learning algorithms for energy forecasting , anomaly detection, and optimization.
• Clean, process, and analyze large, complex datasets from diverse sources within the energy domain.
• Create compelling data visualizations and dashboards to communicate findings and insights to technical and non-technical stakeholders.
• Collaborate with domain experts and engineers to define key performance indicators (KPIs) and identify business needs.
• Design and conduct experiments to test hypotheses and validate models.
• Stay current with advancements in data science, machine learning, and energy analytics research.

Requirements

• Master's or Ph.D. in Data Science, Statistics, Computer Science, Physics, or a related quantitative field.
• 5+ years of experience as a Data Scientist , with a significant focus on applying machine learning to real-world problems.
• Proficiency in programming languages like Python or R, and relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Strong experience with SQL and database management.
• Demonstrated experience in energy data analysis , time-series forecasting, or related areas.
• Excellent analytical, problem-solving, and communication skills.

Benefits

• Exceptional salary package and year-end performance bonus.
• Fully remote work environment with flexible scheduling.
• Comprehensive medical, dental, and vision insurance plans.
• Generous professional development budget for courses and conferences.
• Opportunity to work on challenging problems at the intersection of data science and sustainable energy .