Manager I, Data Scientist

About the Role

Kroll is seeking an experienced Data Science Manager to lead and expand its data science team within the Enterprise Data Group. This pivotal role combines technical leadership with strategic execution, overseeing the direction and development of machine learning (ML) and artificial intelligence (AI) initiatives. The successful candidate will manage a diverse team of data scientists and collaborate with engineering, product managers, and business stakeholders, including clients from top financial institutions, government agencies, and law enforcement.

Responsibilities

• Guide and develop a team of data scientists at various experience levels, fostering technical growth and a high-performance culture.
• Own the comprehensive data science roadmap, prioritizing projects, managing delivery timelines, and reporting progress and outcomes to senior leaders and clients.
• Collaborate with product, engineering, and business teams to identify challenges, define machine learning solutions, and align data science initiatives with business objectives.
• Oversee the full machine learning lifecycle from problem definition and data preparation to model design, experimentation, deployment, and monitoring.
• Implement and maintain standards for code quality, rigorous experimentation, model governance, and ethical AI use.
• Promote and enhance ML infrastructure on Databricks and Microsoft Azure platforms, including embracing MLOps methodologies such as continuous integration/continuous deployment (CI/CD), model version control, and drift detection.
• Lead initiatives involving large language models (LLMs) and generative AI, covering technologies such as retrieval-augmented generation (RAG), prompt engineering, fine-tuning, and agent-based frameworks, ensuring responsible evaluation and deployment.
• Recruit, onboard, and retain top data science talent, handling performance management and growth opportunities.
• Represent the data science team in both internal and external communications, translating complex technical concepts into clear messages accessible to both technical and non-technical audiences.

Requirements

• Master's or PhD in computer science, statistics, mathematics, data science, or a closely related quantitative discipline.
• Minimum of 7 years’ hands-on experience in applied data science or machine learning, with at least 2 years in a leadership or technical lead role.
• Proven experience delivering machine learning solutions in production environments that generate measurable business results.
• Advanced proficiency in Python and familiarity with modern machine learning frameworks such as scikit-learn, PyTorch or TensorFlow, Hugging Face Transformers, and pandas.
• Practical experience using Databricks including notebooks, jobs, MLflow, and Unity Catalog; skilled in Spark and PySpark.
• Demonstrated knowledge of Microsoft Azure production environments, preferably including Azure AI Foundry, Azure OpenAI Service, and Azure Data Lake.
• Comprehensive expertise across machine learning domains, including traditional/statistical ML, deep learning, natural language processing (NLP), and applications of LLMs/generative AI such as prompt engineering, RAG systems, embeddings, and agentic workflows.
• Experience establishing MLOps best practices: CI/CD pipelines, monitoring models, detecting model drift, and maintaining model versioning.
• Excellent communicator capable of simplifying complex technical information for senior executives and clients.
• Strong decision-making skills related to prioritization, balancing trade-offs, and managing competing stakeholder expectations.

Preferred Qualifications

• Background in financial services, risk management, compliance, or regulatory frameworks.
• Hands-on experience with agentic AI frameworks such as LangChain, LlamaIndex, and Semantic Kernel, as well as tools for LLM evaluation and production deployment of generative AI applications.
• Familiarity with responsible AI principles emphasizing fairness, explainability, and data privacy.
• Experience using containerization and orchestration tools like Docker and Kubernetes, along with Azure DevOps or GitHub Actions for CI/CD.