Data Scientist

Entry-Level Data Scientist

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.

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.

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.