Kroll is hiring a Data Scientist to join its Enterprise Data Group. This role is ideal for an early- to mid-career practitioner who is eager to develop deep expertise across the ML lifecycle while contributing to high-impact work in a sophisticated, collaborative data science team.
Our program spans fintech product development, digital transformation, process automation with machine learning, business intelligence, data governance, and generative AI. You will be embedded in a team of experienced data scientists and engineers who will invest in your growth — and work alongside 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. Apply now to join One team, One Kroll.
Responsibilities
- Build, train, and evaluate machine learning models across traditional ML, NLP, and LLM/GenAI use cases, under the guidance of senior team members
- Contribute to data pipelines and feature engineering workflows in Databricks using PySpark and Delta Lake
- Support model deployment and monitoring on Azure — including Azure AI Foundry, Azure OpenAI, and Azure Functions — and help maintain production model health
- Contribute to LLM and generative AI workflows — including prompt engineering, RAG pipelines, and agentic applications built on frameworks such as LangChain or LlamaIndex — under the guidance of senior team members
- Conduct exploratory data analysis and communicate findings clearly through code, documentation, and team presentations
- Write clean, well-tested, and reproducible Python code; contribute to shared codebases and track experiments via MLflow
- Partner with senior data scientists and cross-functional stakeholders to understand business problems and translate them into analytical approaches
- Participate actively in code reviews, team rituals, and knowledge-sharing sessions
- Develop your skills proactively — engage with new tools, research, and techniques relevant to the team's work
Requirements
- Bachelor's or Master's degree in computer science, statistics, mathematics, data science, or a related quantitative field
- 1–3 years of practical data science or ML experience (internships, co-ops, research, and strong project work all count)
- Proficiency in Python and familiarity with core ML libraries (scikit-learn, pandas, NumPy)
- Solid understanding of foundational ML concepts: supervised and unsupervised learning, model evaluation, cross-validation, and feature engineering
- Exposure to at least one deep learning or NLP framework (PyTorch, TensorFlow, or Hugging Face Transformers) and familiarity with LLM concepts such as prompt engineering, embeddings, or retrieval-augmented generation
- Comfort working with structured and unstructured data, including text and document-based sources
- Basic understanding of the ML lifecycle — from data preparation and experimentation through evaluation and handoff
- Clear, organised communication skills — able to document work and explain methods to peers and stakeholders
- Curiosity, rigour, and a strong drive to learn in a fast-paced, collaborative environment
Preferred
- Hands-on experience with Databricks, Spark/PySpark, or cloud ML platforms (Azure AI Foundry, AWS SageMaker, or GCP Vertex AI)
- Hands-on experience with LLM/GenAI and agentic workflows — prompt engineering, RAG, embeddings, vector databases, or building with frameworks such as LangChain, LlamaIndex, or Semantic Kernel
- Familiarity with MLflow or other experiment tracking and model versioning tools
- Experience in financial services, risk, compliance, or a regulated industry
- Knowledge of responsible AI principles, including fairness, transparency, and data privacy
