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.

• 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.