Data Scientist

• Design, build, and optimize Generative AI, large language models, and multimodal foundation models for enterprise fintech applications.
• Fine-tune and adapt open-source and proprietary models for various use cases.
• Build high-performance models for natural language processing, document intelligence, anomaly detection, risk scoring, predictive analytics, and decision-making applications.
• Lead experimentation to evaluate model accuracy, scalability, and fairness.
• Collaborate with engineering teams to deploy models on cloud-based machine learning pipelines and data platforms.
• Work with large-scale structured and unstructured datasets across financial and payments ecosystems.
• Implement model monitoring, drift detection, and continuous retraining strategies.
• Evaluate and operationalize new AI technologies, foundation model architectures, responsible AI frameworks, and emerging research.
• Drive proof-of-concept initiatives and innovation efforts to enhance AI capabilities and product differentiation.

Responsibilities:

• Design, build, and optimize advanced AI and machine learning models.
• Fine-tune and adapt models for enterprise applications.
• Conduct experiments to evaluate model performance and fairness.
• Deploy models using cloud-based ML pipelines and data platforms.
• Work with large-scale structured and unstructured datasets.
• Implement monitoring, drift detection, and retraining strategies.
• Evaluate emerging AI technologies and frameworks.
• Drive innovation initiatives and proof-of-concept projects.

Should Have:

• Experience working in fintech, payments, banking, or fraud and risk environments.
• Background in vector databases, retrieval-augmented generation pipelines, and knowledge graph integration.
• Experience with data privacy, model governance, and responsible AI frameworks.
• Contributions to open-source AI or machine learning communities or research publications.

Skills:

• Strong proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow.
• Experience with transformer architectures, transfer learning, and model fine-tuning.
• Experience with cloud ML platforms, containerization, and MLOps tools.
• Solid understanding of statistical modeling, optimization, and evaluation methodologies.
• Strong communication and collaboration skills in cross-functional environments.

Qualification And Education:

• Master s or PhD in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or a related field.
• 8+ years of hands-on experience building and deploying machine learning models in production.