Senior Data Scientist

Kroll; Bengaluru; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

Kroll is seeking a Senior Data Scientist in Bengaluru to lead end-to-end ML initiatives, mentor junior team members, and partner with business and engineering stakeholders to translate complex problems into production-grade data science solutions.

You will work across fintech product development, digital transformation, and generative AI, collaborating with teams from leading financial institutions and government bodies to deliver measurable business value.

Qualifications

• Advanced degree (MS or PhD) in computer science, statistics, mathematics, analytics, or related quantitative field.
• 5+ years of applied machine learning experience, including delivering models to production.
• Strong Python skills and experience with modern ML stack (scikit-learn, PyTorch or TensorFlow, pandas, Hugging Face Transformers).
• Hands-on experience with Databricks (notebooks, jobs, MLflow, Unity Catalog) and Spark/PySpark.
• Production experience on Azure — ideally including Azure AI Foundry, Azure OpenAI Service, and Azure Data Lake.

Responsibilities

• Design, research, implement, and evaluate machine learning solutions spanning traditional ML, deep learning, NLP, and LLM/GenAI applications.
• Build and fine-tune models — from gradient-boosted trees and classical statistical models to transformer-based architectures and retrieval-augmented generation (RAG) systems.
• Develop and optimize prompts, evaluation frameworks, and guardrails for LLM-powered applications.
• Engineer scalable data and ML pipelines in Databricks using PySpark, Delta Lake, and MLflow.
• Deploy, monitor, and maintain models in production on Azure (Azure AI Foundry, Azure OpenAI, Azure Functions, AKS), including CI/CD, model versioning, and drift detection.
• Validate model inputs, outputs, and business impact; establish robust testing and monitoring practices.
• Partner with engineering, product, and business stakeholders to scope problems and translate ML capabilities into measurable outcomes.
• Communicate technical concepts, tradeoffs, and results to non-technical audiences, including senior leadership and clients.

Skills

Python

ML/DL

NLP

Transformer models

Communication

Education

MS/PhD in CS/Statistics/Analytics

Tools

Databricks

PySpark

MLflow

Unity Catalog

Azure OpenAI

Job description

Kroll is hiring a Senior Data Scientist to join its Enterprise Data Group. This role is designed for an experienced practitioner who can lead end-to-end ML initiatives, mentor junior team members, and partner with business and engineering stakeholders to translate complex problems into production-grade data science solutions.

Our program spans fintech product development, digital transformation, process automation with machine learning, business intelligence, data governance, and generative AI. You will work alongside an advanced data science and engineering team — and collaborate with professionals from the world's largest financial institutions, law enforcement agencies, and government bodies.

At Kroll, your work will help deliver clarity to our clients' most complex governance, risk, and transparency challenges.

Responsibilities

• Design, research, implement, and evaluate machine learning solutions spanning traditional ML, deep learning, NLP, and LLM/GenAI applications
• Build and fine-tune models — from gradient-boosted trees and classical statistical models to transformer-based architectures and retrieval-augmented generation (RAG) systems
• Develop and optimize prompts, evaluation frameworks, and guardrails for LLM-powered applications
• Engineer scalable data and ML pipelines in Databricks using PySpark, Delta Lake, and MLflow
• Deploy, monitor, and maintain models in production on Azure (Azure AI Foundry, Azure OpenAI, Azure Functions, AKS), including CI/CD, model versioning, and drift detection
• Validate model inputs, outputs, and business impact; establish robust testing and monitoring practices
• Partner with engineering, product, and business stakeholders to scope problems and translate ML capabilities into measurable outcomes
• Communicate technical concepts, tradeoffs, and results to non-technical audiences, including senior leadership and clients
• Mentor junior data scientists and contribute to team standards around code quality, experimentation, and responsible AI

Requirements

• Advanced degree (MS or PhD) in computer science, statistics, mathematics, analytics, or a related quantitative field
• 5+ years of applied machine learning experience, including delivering models to production
• Strong Python skills and experience with the modern ML stack (scikit-learn, PyTorch or TensorFlow, pandas, Hugging Face Transformers)
• Hands-on experience with Databricks (notebooks, jobs, MLflow, Unity Catalog) and Spark/PySpark
• Production experience on Azure — ideally including Azure AI Foundry, Azure OpenAI Service, and Azure Data Lake
• Breadth across ML domains: traditional/statistical ML, deep learning, NLP, and LLM/GenAI applications, including hands-on experience with prompt engineering, RAG, embeddings, and agentic workflows
• Practical experience building LLM/GenAI applications — prompt engineering, RAG, fine-tuning, embeddings, vector databases, and evaluation
• Solid grounding in the full ML lifecycle: data validation, feature engineering, model design, experimentation, deployment, and monitoring
• Experience with structured and unstructured data, including text, documents, and semi-structured sources
• Strong statistical foundation and ability to reason about uncertainty, bias, and model risk
• Excellent technical and business communication skills

Preferred

• Experience in financial services, risk, compliance, or regulatory domains
• Familiarity with MLOps tooling (MLflow, Docker, Kubernetes, Azure DevOps or GitHub Actions)
• Hands-on experience with agentic AI frameworks (LangChain, LlamaIndex, Semantic Kernel), LLM evaluation tooling, and production deployment of GenAI applications
• Knowledge of responsible AI practices, including fairness, explainability, and data privacy