Data Scientist - Entry Level

Kotak Mahindra Bank Limited; Bengaluru; On-site; INR 700,000 - 1,100,000; Full time

Job summary

Kotak Mahindra Bank Limited is seeking a Data Scientist I to join our analytics team. You will build ML models, work with Python, ML frameworks, and governance guidelines to deliver scalable, secure AI solutions that add business value.

The role emphasizes responsible AI, RBI/MeitY compliance, data engineering collaboration, and hands-on model deployment, evaluation, and monitoring across regulated financial use cases. Growth opportunities and modern tooling are provided.

Qualifications

• Hands-on ML/AI project experience with model development and evaluation.
• Knowledge of LLMs/SLMs, prompt engineering, and RAG concepts.
• Awareness of Responsible AI, privacy techniques, and enterprise governance.
• Experience with AI/ML security, guardrails, and DLP.

Responsibilities

• Develop and maintain ML models including propensity models, classification, regression, and clustering.
• Work with Large Language Models (LLMs) and Small Language Models (SLMs) for enterprise use cases, including synthetic data pipelines.
• Design AI agents and automation workflows to support business processes.
• Implement evaluation frameworks, guardrails, and model risk assessment.
• Ensure compliance with RBI/MeitY guidelines; apply privacy and Explainable AI techniques.
• Participate in MLOps: deployment, monitoring, and CI/CD for reproducible pipelines.
• Collaborate with product, engineering, analytics, and compliance teams to translate problems into ML solutions.

Skills

Python

ML frameworks

LLMs/SLMs

Prompt engineering

RAG concepts

Model risk

AI security

XAI tools

DP/k-anonymity

AIOps/MLOps

Docker

Git

MLflow

Airflow

Geospatial analytics

Education

Bachelor's/Master's in CS/DS/Math/Stats

Tools

TensorFlow

PyTorch

scikit-learn

Pandas

NumPy

Docker

Git

MLflow

Airflow

Job description

Data Science I_SUPPORT SERVICES_0746_8147_1094934 Job Description – Data Scientist I Role Overview

We are seeking a highly motivated Data Scientist I with strong foundational knowledge in machine learning, modern AI techniques, and emerging Large Language Model (LLM) capabilities. The role requires hands‑on experience with model development, fine‑tuning, evaluation, and adherence to Responsible AI and regulatory guidelines (RBI/MeitY). You will collaborate with cross‑functional teams to build scalable, secure, and explainable AI systems that drive business value.

Key Responsibilities

1. 1. Machine Learning & Statistical Modeling Develop and maintain ML models including propensity models , classification, regression, and clustering. Perform data cleaning, feature engineering, and exploratory data analysis. Build models using Python, SQL, and leading ML frameworks (TensorFlow, PyTorch, Scikit‑learn).
2. 2. Generative AI & LLMs Work with Large Language Models (LLMs) and Small Language Models (SLMs) for enterprise use cases. Apply fine‑tuning, distillation, and model optimization techniques to adapt models to business needs. Create and manage synthetic data pipelines for training and evaluation.
3. 3. AI Agents & Workflows Assist in designing AI agents and agentic workflows to automate decision‑making processes. Contribute to building AI‑driven orchestration systems across business workflows.
4. 4. Model Evaluation & Guardrails Implement LLM-as-a-Judge , evaluation frameworks, prompt tests, and model benchmarking. Apply model risk assessment and mitigation strategies as per enterprise AI governance. Implement security guardrails , including DLP controls and content safety filters.
5. 5. Responsible AI & Compliance Ensure all models comply with: RBI – Financial Regulation for Emerging Entities (FREE) guidelines MeitY AI & Data Governance Guidelines Integrate Privacy Preservation , Explainable AI (XAI) , and Responsible AI techniques into model workflows.
6. 6. Engineering & MLOps Participate in AIOps/MLOps processes: model deployment, monitoring, versioning, CI/CD. Document experiments, track model performance, and support reproducible ML pipelines.
7. 7. Data Engineering & Domain Collaboration Work with structured, unstructured, and geospatial datasets (a plus). Collaborate closely with product, engineering, analytics, and compliance teams to translate business problems into ML solutions.

Required Skills

Strong proficiency in Python , ML libraries (scikit‑learn, pandas, NumPy), and deep learning frameworks. Knowledge of LLMs, SLMs, prompt engineering, and RAG concepts. Familiarity with fine‑tuning, quantization, pruning, and distillation methods. Understanding of model risks, adversarial ML, and mitigation strategies . Experience with AI/ML security, guardrails, and DLP principles . Understanding of XAI tools (SHAP, LIME, Integrated Gradients). Sound knowledge of Responsible AI, privacy techniques (DP, k‑anonymity) . Basic familiarity with AIOps/MLOps , Docker, Git, MLflow, Airflow (preferred). Exposure to geospatial analytics (nice to have).

Educational Background

Bachelor’s/Master’s in Computer Science, Data Science, Mathematics, Statistics, or related fields .

Experience Required

1–2 years of hands‑on experience in ML/AI projects, internships, research, or capstone projects. Nice-to-Have Experience with LangChain, LlamaIndex , or other agent frameworks. Participation in AI/ML competitions (Kaggle, Hackathons). Knowledge of BFSI domain analytics (advantage but not mandatory).