AI Scientist

• JOB TITLEAI Scientist
• GRADESM
• DEPARTMENTDATA SCIENCE AND DECISION MANAGEMENT
• LOCATIONHO(Bengaluru)
• SUB-DEPARTMENTDATA SCIENCE AND DECISION MANAGEMENT
• TYPE OF POSITIONFull-time
• REPORTS TO
• REPORTING INTONA ROLE PURPOSE & OBJECTIVE- 1. To research, design, and develop advanced AI/ML models that address the Bank's core business challenges including credit risk, fraud, customer segmentation, and collections optimization.
• 2. To drive applied AI research and translate cutting-edge methods into production-ready solutions across the Bank's analytics ecosystem.
• 3. To spearhead Generative AI and LLM adoption, establishing best practices and guardrails for responsible, explainable, and compliant AI deployment in a regulated financial environment.
• KEY DUTIES & RESPONSIBILITIES OF THE ROLE

1. Business / Financials

Design, develop, and deploy ML/AI models (classification, regression, clustering, NLP, deep learning) for credit scoring, risk, fraud detection, and customer analytics

Own the full model lifecycle: problem framing, data preparation, feature engineering, model training, validation, deployment, and monitoring

Build and fine-tune Large Language Models (LLMs) and Generative AI applications for internal use cases (e.g., document intelligence, credit memo automation, Q&A bots)

Conduct experiments and A/B tests to validate model performance and measure business impact

Optimize model inference for scalability, latency, and cost-efficiency in production environments

Publish model performance metrics and KPIs, and report findings to senior stakeholders and the Model Risk Committee

2. Customer (Both Internal & External)

Collaborate with Risk, Credit, Operations, Collections, and Product teams to translate business problems into AI/ML solutions

Ensure models are explainable and meet RBI/regulatory expectations for fairness, transparency, and auditability (aligned with FREE-AI framework)

Communicate complex model results in a clear, business-friendly manner to both technical and non-technical audiences

Work with external vendors and third-party AI solution providers to evaluate tools, partnerships, and model integrations

3. Internal Process

Establish and enforce model governance standards: model documentation, validation reports, champion-challenger frameworks, and model risk registers

Implement MLOps pipelines to automate model training, versioning, CI/CD deployment, and drift monitoring

Partner with Data Engineering and IT teams to ensure model-ready data availability and robust feature stores

Contribute to the Bank's AI Governance Policy, ethical AI guidelines, and responsible AI practices

4. Innovation & Learning

Stay current with state-of-the-art research in deep learning, NLP, reinforcement learning, and Generative AI; evaluate applicability to banking use cases

Drive a culture of experimentation and innovation within the Advanced Analytics Centre of Excellence (AACoE)

Mentor junior data scientists and ML engineers; conduct knowledge-sharing sessions on new AI techniques and tools

Represent the Bank in external AI forums, conferences, and regulatory consultations as required

• MINIMUM REQUIREMENTS OF KNOWLEDGE & SKILLS

Educational

Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or related STEM field
• PhD in Machine Learning, AI, or Quantitative discipline preferred

Certifications in ML/AI (e.g., Google Professional ML Engineer, AWS ML Specialty, Coursera Deep Learning Specialization) are an advantage

Experience Range (Years and Core Experience Type)

• 3-5 years of hands-on experience in building and deploying ML/AI models in production environments

Prior experience in BFSI / FinTech domain preferred, especially in credit risk, fraud, or collections analytics

Certifications

• Proficiency in Python (mandatory) and R
• Experience with deep learning frameworks: TensorFlow, PyTorch, Keras
• Familiarity with LLM tooling: Hugging Face, LangChain, OpenAI API, RAG pipelines

Experience with MLOps platforms: MLflow, Kubeflow, Vertex AI, SageMaker

F****unctional Skills

• Strong foundations in statistics, probability, linear algebra, and optimization
• Expertise in supervised, unsupervised, and reinforcement learning algorithms
• Experience with NLP techniques: text classification, NER, summarization, embeddings, semantic search
• Proficiency in feature engineering, model selection, hyperparameter tuning, and cross-validation
• Hands-on experience with model explainability tools: SHAP, LIME, Anchors
• Familiarity with fairness/bias detection methods and regulatory model validation standards
• Exposure to cloud ML platforms (GCP / AWS / Azure) and containerization (Docker, Kubernetes)
• Experience with SQL, Spark, and working on large-scale structured and unstructured datasets
• Knowledge of Generative AI architectures: Transformers, Diffusion Models, GANs

Experience building RAG-based or agent-based LLM applications is highly desirable