Data Scientist

Job Title: Data Scientist Engagement Type: (Long Term Contract) Onsite Requirement: Raleigh, NC – 3 days/week Start Date: ASAP Role Summary We are seeking a hands-on Data Scientist, with potential extension based on budget and performance. This role focuses on developing analytics and machine learning solutions using large, complex datasets. The ideal candidate will work closely with engineering and product teams to deliver production-ready models and actionable insights. Experience in utility/energy data or demography is highly preferred. Key Objectives • Analyze large datasets to identify patterns, trends, and anomalies • Develop and validate statistical and machine learning models • Deliver insights to support product, operational, and engineering decisions • Collaborate with engineering teams for model deployment and operationalization • Work under guidance of a senior PhD-level Data Scientist • Provide clear documentation and knowledge transfer Core Responsibilities • Perform data exploration, cleaning, and feature engineering • Build and evaluate models (regression, classification, clustering, anomaly detection, time series) • Design and analyze experiments as needed • Translate business problems into analytical solutions • Communicate insights to both technical and non-technical stakeholders • Follow best practices (version control, documentation, reproducibility) Required Skills & Experience • Strong experience in data science or advanced analytics roles • Proficiency in Python (pandas, NumPy, scikit-learn or equivalent) • Solid understanding of statistics and machine learning fundamentals • Experience with SQL and structured datasets • Ability to work independently and ramp up quickly in a contract environment Preferred Qualifications • Experience with utility, energy, or industrial data (electric, gas, water, AMI, IoT) • OR demography-related datasets • Experience with time series analysis and anomaly detection • Exposure to big data platforms (Spark, Databricks, cloud platforms) • Experience deploying or supporting models in production environments