Data Scientist

Description We are seeking a Junior Data Scientist to join our large Utility client in downtown Indianapolis. This position will be hired as a Full-Time employee. This entry-level position is perfect for individuals eager to tackle real-world energy challenges through data exploration, predictive modeling, and collaborative problem-solving. As part of our team, you'll work closely with seasoned data scientists, analysts, architects, engineers, and governance specialists to generate insights that power smarter decisions and help shape the future of energy. Key Responsibilities Partner cross-functionally with data scientists, data architects and engineers, machine learning engineers, data analysts, and data governance experts to deliver integrated data solutions. Collaborate with business stakeholders and analysts to define clear project requirements. Collect, clean, and preprocess both structured and unstructured data from utility systems (e.g., meter data, customer data). Conduct exploratory data analysis to uncover trends, anomalies, and opportunities to enhance grid operations and customer service. Apply traditional machine learning techniques and generative AI tools to build predictive models that address utility-focused challenges, particularly in the customer domain (e.g., outage restoration, program adoption, revenue assurance). Present insights to internal stakeholders in a clear, compelling format, including data visualizations that drive predictive decision-making. Document methodologies, workflows, and results to ensure transparency and reproducibility. Serve as a champion of data and AI across all levels of the client's US Utilities organization. Stay informed on emerging industry trends in utility analytics and machine learning. Requirements Bachelor's degree in data science, statistics, computer science, engineering, or a related field. Master's degree or Ph.D. is preferred. 1–3 years of experience in a data science or analytics role. Strong applied analytics and statistics skills, such as distributions, statistical testing, regression, etc. Proficiency in Python or R, with experience using libraries such as pandas, NumPy, and scikit-learn. 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. Familiarity with generative AI tools and techniques, including large language models (LLMs) and Retrieval-Augmented Generation (RAG), with an understanding of how these can be applied to enhance contextual relevance and integrate enterprise data into intelligent workflows. Proficiency in SQL, with experience writing complex queries and working with relational data structures. Google BigQuery experience is preferred, including the use of views, tables, materialized views, stored procedures, etc. Proficient in Git for version control, including repository management, branching, merging, and collaborating on code and notebooks in data science projects. Experience integrating Git with CI/CD pipelines to automate testing and deployment is preferred. Experience with cloud computing platforms (GCP preferred). Ability to manage multiple priorities in a fast-paced environment. Interest in learning more about the customer-facing side of the utility industry. Compensation: Up to $130,000 per year annual salary. Exact compensation may vary based on several factors, including skills, experience, and education. Benefit packages for this role may include healthcare insurance offerings and paid leave as provided by applicable law.