Senior Data Governance Expert

Kroll is hiring a Senior Data Scientist for its Enterprise Data Group.

This program involves building fintech products, driving digital transformation of Kroll services, process automation with machine learning, business intelligence, data governance, and the development of generative AI solutions. You will work alongside an advanced engineering and data science team and collaborate with professionals from the world's largest financial institutions, law enforcement agencies, and government bodies.

This is a senior-level position for an experienced practitioner who can lead end-to-end ML initiatives, mentor junior team members, and partner with stakeholders to translate complex business problems into production-grade data science solutions.

At Kroll, your work will help deliver clarity to our clients' most complex governance, risk, and transparency challenges. 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
Engineer scalable data and ML pipelines in Databricks using PySpark, Delta Lake, and MLflow
Deploy, monitor, and maintain models in production on Azure (Azure ML, Azure OpenAI, Azure Functions, AKS), including CI/CD, model versioning, and drift detection
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

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 Machine Learning, Azure OpenAI Service, and Azure Data Lake
• Demonstrated experience across multiple ML domains: traditional/statistical ML, deep learning, and NLP
• 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

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

Join the global leader in risk and financial advisory solutions—Kroll.