Data Scientist

About the Role

As an entry-level Data Scientist, you will work with engineers, data professionals, and product teams to frame problems, prepare data, build and evaluate analytical or machine-learning models, and communicate results. This role is designed for a recent graduate who brings strong fundamentals, curiosity, and evidence of applied project work.

Key Responsibilities

• Partner with engineering and business stakeholders to translate a question into a clear analytical problem, success criteria, and testable approach.
• Explore, clean, join, and validate structured and unstructured datasets; document assumptions, limitations, and data-quality issues.
• Build baseline and advanced models using statistical analysis, machine learning, optimization, or time-series methods as appropriate.
• Compare models using relevant performance metrics and evaluate uncertainty, bias, robustness, and generalization.
• Create clear visualizations, notebooks, and concise presentations that explain methods, findings, and recommended actions.
• Work with Data Engineers and AI Engineers to move useful analyses from prototypes toward reusable, monitored solutions.
• Use version control, code review, testing, and reproducible workflows to create maintainable analytical assets.
• Protect confidential information and follow cybersecurity, data governance, intellectual property, and Responsible AI requirements.
• Continue developing domain knowledge in energy, engineering, and industrial systems through hands-on work and mentorship.

Requirements & Tech Stack

• Bachelor's degree completed by the start date in Data Science, Statistics, Mathematics, Computer Science, Engineering, Operations Research, Physics, or a related quantitative field.
• Foundational knowledge of statistics, probability, experimental design, and machine-learning concepts.
• Hands-on experience with Python or R through coursework, research, internships, co-ops, or independent projects.
• Experience using common data analysis and visualization tools such as pandas, NumPy, scikit-learn, SQL, Jupyter, matplotlib, seaborn, Power BI, or equivalents.
• Ability to explain technical work clearly in writing and conversation to both technical and non-technical audiences.
• Demonstrated problem-solving, collaboration, attention to detail, and willingness to learn.

Preferred Qualifications

• Internship, co-op, research, capstone, or portfolio experience applying analytics or machine learning to a real problem.
• Exposure to cloud data platforms, distributed computing, data pipelines, Git, containers, or ML lifecycle tools.
• Experience with time-series, simulation, optimization, computer vision, natural language processing, or generative AI.
• Interest in renewable energy, physical systems, manufacturing, or engineering applications.
• Experience validating results with subject-matter experts and incorporating feedback into an improved solution.