Data Scientist I

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 membersContribute to data pipelines and feature engineering workflows in Databricks using PySpark and Delta LakeSupport model deployment and monitoring on Azure — including Azure AI Foundry, Azure OpenAI, and Azure Functions — and help maintain production model healthContribute 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 membersConduct exploratory data analysis and communicate findings clearly through code, documentation, and team presentationsWrite clean, well-tested, and reproducible Python code; contribute to shared codebases and track experiments via MLflowPartner with senior data scientists and cross-functional stakeholders to understand business problems and translate them into analytical approachesParticipate actively in code reviews, team rituals, and knowledge-sharing sessionsDevelop 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 field1–3 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 engineeringExposure 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 generationComfort working with structured and unstructured data, including text and document-based sourcesBasic understanding of the ML lifecycle — from data preparation and experimentation through evaluation and handoffClear, organised communication skills — able to document work and explain methods to peers and stakeholdersCuriosity, 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 KernelFamiliarity with MLflow or other experiment tracking and model versioning toolsExperience in financial services, risk, compliance, or a regulated industryKnowledge of responsible AI principles, including fairness, transparency, and data privacy