Manager I, Data Scientist

Kroll is hiring a Data Science Manager to lead and grow our data science function within the Enterprise Data Group. This role sits at the intersection of technical leadership and strategic delivery — you will shape how data science is practised across the team, own the roadmap for ML and AI initiatives, and develop the talent responsible for bringing those initiatives to life.
Our program spans fintech product development, digital transformation, process automation with machine learning, business intelligence, data governance, and generative AI. You will lead a team of data scientists who partner with engineering, product, and business stakeholders — including 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
Lead, mentor, and grow a team of junior, intermediate, and senior data scientists — setting technical direction, enabling individual development, and cultivating a high-performance team culture
Own the end-to-end data science roadmap: prioritise initiatives, manage delivery, and communicate progress and impact to senior leadership and clients
Partner with product, engineering, and business stakeholders to define problems, scope ML solutions, and translate data science capabilities into measurable business outcomes
Provide technical oversight across the full ML lifecycle — from problem framing and data validation through model design, experimentation, production deployment, and monitoring
Establish and uphold team standards around code quality, experimentation rigour, model governance, and responsible AI practices
Drive adoption and evolution of ML infrastructure on Databricks and Azure (Azure AI Foundry, Azure OpenAI, AKS), including MLOps practices such as CI/CD, model versioning, and drift detection
Champion LLM and generative AI initiatives — including RAG architecture, prompt engineering, fine-tuning, and agentic frameworks — ensuring they are evaluated rigorously and deployed responsibly
Recruit and retain top data science talent; lead hiring, onboarding, and performance management processes
Represent data science internally and externally, communicating technical concepts and tradeoffs clearly to both technical and non-technical audiences
Requirements
Advanced degree (MS or PhD) in computer science, statistics, mathematics, data science, or a related quantitative field
7+ years of applied data science or machine learning experience, including at least 2 years in a people management or technical lead capacity
Proven track record of delivering ML solutions to production and driving measurable business impact
Strong Python skills and fluency with the modern ML stack (scikit-learn, PyTorch or TensorFlow, Hugging Face Transformers, pandas)
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
Experience establishing MLOps practices including CI/CD, model monitoring, drift detection, and model versioning
Excellent communication skills — able to translate complex technical work into clear business narrative for senior leadership and clients
Strong judgment in prioritisation, tradeoffs, and managing competing stakeholder demands
Preferred
Experience in financial services, risk, compliance, or regulatory domains
Hands-on experience with agentic AI frameworks (LangChain, LlamaIndex, Semantic Kernel), LLM evaluation tooling, and production deployment of GenAI applications
Knowledge of responsible AI principles, including fairness, explainability, and data privacy
Experience with Docker, Kubernetes, and Azure DevOps or GitHub Actions

Work arrangement
No