AI Scientist

Ujjivan Small Finance Bank; Bengaluru; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

Ujjivan Small Finance Bank seeks an AI Scientist to research, design, and deploy advanced AI/ML models addressing credit risk, fraud, and customer analytics. The role drives Generative AI adoption, ensures explainability, and aligns with regulatory guidelines within the Bank's AI governance framework.

You will lead end-to-end model lifecycles, collaborate with Risk, Credit, Operations, and Product teams, and mentor junior data scientists in a data-driven analytics ecosystem.

Qualifications

• Bachelor's or Master's in CS, Statistics, Mathematics, Data Science, or related STEM field
• PhD in ML/AI preferred
• 3-5 years of hands-on ML/AI model experience in production
• Experience in BFSI/FinTech credit risk, fraud, or collections analytics
• Proficiency in Python (mandatory) and R; 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
• Knowledge of Generative AI architectures: Transformers, Diffusion Models, GANs
• Exposure to cloud ML platforms and containerization (Docker, Kubernetes)
• SQL, Spark; large-scale structured/unstructured data handling
• Experience building RAG-based or agent-based LLM apps desired

Responsibilities

• Research, design, and develop advanced AI/ML models for credit risk, fraud, customer segmentation, and collections optimization
• Own full model lifecycle: problem framing, data prep, feature engineering, training, validation, deployment, monitoring
• Build and fine-tune LLMs and Generative AI apps for internal use cases
• Conduct experiments and A/B tests to validate model performance and business impact
• Optimize inference for scalability, latency, and cost in production
• Publish performance metrics and report findings to senior stakeholders and Model Risk Committee
• Collaborate with Risk, Credit, Operations, and Product teams to translate problems into AI/ML solutions
• Ensure models are explainable and meet RBI/regulatory expectations
• Establish model governance standards and ML lifecycle processes
• Contribute to AI Governance Policy and responsible AI practices
• Mentor junior scientists and represent the Bank in AI forums

Skills

Python

TensorFlow

PyTorch

Keras

LangChain

RAG pipelines

MLOps

NLP

Explainability

Cloud platforms

Education

PhD in ML / AI

MS in CS / Data Science

Bachelors in STEM

Tools

MLflow

Kubeflow

Vertex AI

SageMaker

Job description

• 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

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

Functional 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