Data Scientist

## Job Description Summary 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. You will contribute to practical solutions that are technically rigorous, understandable and ready to be used by the business. This role is designed for a recent graduate who brings strong fundamentals, curiosity and evidence of applied project work. ## What You Will Do - 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. ## Required Qualifications - 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.