Associate Data Scientist

Responsibilities Support the development and enhancement of fraud detection and risk analytics models, including statistical models, anomaly detection, and machine learning approaches. Perform exploratory data analysis on large and complex datasets to identify fraud patterns, trends, and data quality issues. Assist in training, evaluating, and monitoring machine learning models to ensure performance and stability in production environments. Contribute to Generative AI initiatives such as prompt engineering, RAG (Retrieval‑Augmented Generation) pipelines, or document‑understanding use cases under senior guidance. Help prepare and structure data for LLM‑based applications, including extracting information from complex documents (e.g., multi‑column text, tables). Stay curious and continue learning about advancements in machine learning, generative AI, and cloud‑based analytics tools. Partner with senior data scientists, engineers, and business stakeholders to understand requirements and deliver analytical solutions. Clearly document analyses, models, and assumptions to support knowledge sharing and auditability. Communicate insights and results to both technical and non‑technical audiences with guidance and support. Requirements Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field. 0–3 years of hands‑on experience in data science, machine learning, or AI. Proficiency in Python and experience with common data science libraries (e.g., pandas, scikit learn, PyTorch, or similar). Proficiency in SQL and relational databases; exposure to big data or cloud platforms is a plus. Familiarity with Generative AI concepts (LLMs, prompt engineering, embeddings, RAG) through coursework or projects. Experience with version control (e.g., Git) and basic software engineering best practices. Strong analytical and problem‑solving skills with attention to detail. Good communication skills and the ability to work effectively in cross‑functional teams. Eagerness to learn, take feedback, and grow in a collaborative environment. #J-18808-Ljbffr