Senior Data Scientist

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 worlds 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. Apply now to join One team One Kroll.

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

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