AIM Fraud analytics team is seeking an experienced and innovative Data Scientist to join our Fraud Modeling / Gen AI team to transform fraud detection and prevention. As a subject matter expert in data science, you will be responsible for the development and deployment of cutting-edge data science solutions that enhance fraud detection and reduce false positives and optimize operational efficiency.

Key Responsibilities:

Lead the design and implementation of generative AI use cases, from ideation to production.
Build and tune LLM-based applications using platforms like Vertex, GPT, Hugging Face, etc.
Create robust prompt engineering strategies and reusable prompt templates.
Integrate generative AI with enterprise applications using APIs, knowledge graphs, vector databases (e.g., PG Vector, Pinecone, FAISS, Chroma), and orchestration tools.
Collaborate with Tech and business teams to identify opportunities and build solutions.
Responsible for model development, validation and testing of fraud detection models, including statistical analysis, data validation and model performance evaluation.
Ensure compliance with regulatory requirements and industry best practices in fraud detection and model validation.
Responsible for documenting the model development documents for MRM approvals and governance purposes.
Stay current on GenAI trends, frameworks (LangChain, LlamaIndex), and research.
Mentor junior team members and promote a culture of experimentation and learning.
Stay abreast of advancements in Gen AI, LLMs, and fraud prevention technologies, applying relevant innovations to projects.
Specialization in fraud or risk domain preferred

Qualifications:

11+ years of hands-on experience in data science, or AI/ML roles.
7+ years’ experience in statistical analysis with working knowledge of at least one of the following statistical software packages: Python (Must), Hive, Spark, SAS Strong quantitative, analytical, and problem-solving skills; knowledge of probability theory, statistics, mathematical finance, econometrics, numerical methods
2+ years of hands-on experience specifically with generative AI or LLMs.
Strong understanding of statistical concepts and modelling techniques including traditional and advanced ML algorithms
Strong programming skills in Python, and familiarity with libraries like Transformers, LangChain, LlamaIndex, PyTorch, or TensorFlow.
Working knowledge of retrieval-augmented generation (RAG) pipelines and vector databases.
Understanding of MLOps / LLM Ops, model evaluation, prompt tuning, and deployment pipelines
Experience building applications with OpenAI, Anthropic Claude, Google Gemini, or open-source LLMs.
Familiarity with regulatory requirements and guidelines related to risk model validation
Strong communication skills and the ability to partner with both technical and non-technical stakeholders.

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Job Family Group:

Decision Management

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Job Family:

Specialized Analytics (Data Science/Computational Statistics)

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Time Type:

Full time

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Most Relevant Skills

Please see the requirements listed above.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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