About the Role
As a Senior Data Scientist, you will be responsible for designing, developing, and delivering machine learning and Generative AI solutions that enhance fraud detection capabilities. You will work closely with Product, Engineering, and domain experts to translate business challenges into scalable AI-driven products, combining LLMs, classical ML techniques, and domain knowledge into production-grade systems.
Responsibilities
- Design and develop end-to-end machine learning solutions – from data collection and preprocessing to model development, evaluation, and deployment.
- Build predictive and generative models to extract actionable insights from large and complex datasets.
- Apply statistical analysis and quantitative techniques to uncover patterns, trends, and correlations in data.
- Utilize statistical techniques and quantitative analysis to identify trends, patterns, and correlations within the data.
- Design and build agentic AI systems, including:
- multi-step reasoning pipelines
- tool-using agents
- autonomous workflows for investigation and decision support
- Develop RAG-based architectures, integrating LLMs with structured and unstructured data sources.
- Translate business problems into analytical solutions in partnership with Product, Engineering, and domain SMEs.
- Stay current with advances in Agentic AI, LLM architectures, reasoning systems, and ML research.
Requirements
- Minimum of 4-8 years of hands-on experience in data science and machine learning, with at least 1 year of experience in Generative AI development.
- Proficiency in programming languages such as Python, as well as experience with data manipulation and analysis libraries (e.g., pandas, NumPy, scikit-learn, Hugging Face - Transformers, LangChain etc.).
- Strong understanding of machine learning techniques and algorithms, including supervised and unsupervised learning, regression, classification, clustering, and deep learning.
- Hands-on experience with:
- LLMs and prompt engineering
- frameworks like Hugging Face, LangChain, LangGraph / similar
- evaluation techniques for generative models
- Excellent problem-solving skills and ability to work independently as well as collaboratively in a fast-paced environment.
- Strong communication and interpersonal skills, with the ability to effectively communicate complex technical concepts to diverse audiences.
Advantageous Skills
- Experience working in industries such as finance, banking.
- Familiarity with cloud computing platforms (e.g., AWS, Azure, Google Cloud) and related services for building and deploying machine learning models.
- Publications or contributions to the data science community, such as conference presentations, research papers, or open-source projects.
- Experience in implementing RAG pipelines, combining LLMs with external knowledge bases or vector databases.
- Experience building agent-based systems (planning, tool use, memory, reflection loops).
- Hands-on experience with vector databases (e.g. Pinecone) and embedding techniques.