Data Scientist

Insight Global • Hingham, MA • 5h ago

Job Description

The Junior Data Scientist will support a large Utility customer in Indianapolis. They will use data science techniques to improve grid reliability, customer experience, and operational efficiency. This entry-level role is ideal for candidates passionate about solving energy challenges. You'll work alongside our team of experienced data scientists, data analysts, data architects and engineers, and data governance experts to deliver insights that drive smarter decisions and accelerate the future of energy.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills and Requirements

-Sociable, energetic, and eager to be a part of a growing team!

-Bachelor's degree in data science, statistics, computer science, engineering, or a related field. Master's degree or PhD is preferred.

-Strong applied analytics and statistics skills, such as distributions, statistical testing, regression, etc.

-Proficiency in Python or R, with experience using libraries

-Proficiency in traditional machine learning algorithms and techniques, including k-nearest neighbors (k-NN), naive Bayes, support vector machines (SVM), convolutional neural networks (CNN), random forest, gradient-boosted trees, etc.

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