RRS Group is seeking a motivated and analytical Associate Data Scientist to join our team as part of the 2026 New College Graduate hiring program. This role is ideal for candidates with a strong foundation in data analysis, machine learning, statistical modeling, and Generative AI applications. The Associate Data Scientist will work closely with cross-functional teams to analyze large datasets, develop predictive models, generate actionable insights, and support data-driven business decisions.The ideal candidate demonstrates technical proficiency in SQL and Python, hands-on experience with machine learning and analytics tools, and familiarity with modern AI-assisted workflows and responsible AI practices. Key ResponsibilitiesExtract, transform, aggregate, and analyze large and complex datasets using SQL, Python, R, Spark, and related technologies.Perform exploratory data analysis (EDA), feature engineering, and data validation to support analytics and modeling initiatives.Develop, evaluate, and maintain descriptive and predictive machine learning models using tools such as scikit-learn, Jupyter Notebooks, Python, R, and/or SAS.Apply statistical modeling and data mining techniques, including regression, classification, clustering, decision trees, and related methodologies.Utilize Generative AI and AI-assisted tools, including LLMs, coding assistants, and AutoML platforms, to improve analytical workflows and insight generation.Apply Generative AI techniques such as prompt engineering, text summarization, classification, and LLM-assisted analysis in business or research applications.Collaborate with stakeholders to translate business problems into analytical solutions and communicate findings effectively.Build and maintain business intelligence solutions and dashboards using Tableau, Power BI, or similar visualization platforms.Define metrics, validate data quality, and support semantic layer development to ensure accurate reporting and business insights.Follow responsible AI principles, including awareness of data privacy, bias mitigation, ethical AI usage, and model limitations.
