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 Responsibilities
- Extract, 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.