Manager I, Data Scientist

Job Summary

Kroll is seeking an experienced Data Science Manager to lead and expand their data science division within the Enterprise Data Group. This role combines technical leadership with strategic project execution, directing the machine learning and AI roadmap, and fostering the growth of data science professionals who will deliver these initiatives. The focus areas include fintech product development, digital transformation, process automation through machine learning, business intelligence, data governance, and generative AI.

You will manage a team of data scientists collaborating with engineering, product, and business partners such as major financial institutions, law enforcement, and government agencies to tackle complex governance, risk, and transparency issues.

Key Responsibilities

• Lead and mentor data scientists at various career stages to set technical strategy and nurture a high-achieving team environment.
• Own and prioritize the complete data science project pipeline, overseeing timely delivery and conveying progress and impact to executives and clients.
• Collaborate across product, engineering, and business units to define problems and deliver ML solutions that translate data science efforts into tangible business benefits.
• Ensure technical quality throughout the ML lifecycle, covering problem framing, data validation, model design, experimentation, deployment, and ongoing monitoring.
• Develop and enforce standards regarding code quality, experimental rigor, model governance, and ethical AI usage.
• Lead advancement and use of ML infrastructure leveraging Databricks and Azure platforms, including MLOps practices like CI/CD, model versioning, and drift detection.
• Drive generative AI and large language model (LLM) efforts, applying techniques such as retrieval-augmented generation (RAG) architecture, prompt engineering, fine-tuning, and agentic AI frameworks with strict evaluation and responsible deployment.
• Handle recruitment, onboarding, and performance management to attract and retain top-tier data science talent.
• Act as an ambassador for the data science function, communicating technical information clearly to both technical and business stakeholders.

Candidate Requirements

• Advanced degree (Master's or Ph.D.) in computer science, statistics, mathematics, data science, or a related quantitative discipline.
• Over 7 years of experience applying data science or machine learning techniques, including a minimum of 2 years in managerial or technical leadership roles.
• Proven success in deploying ML solutions into production environment with measurable business results.
• Expertise in Python and familiarity with current ML libraries such as scikit-learn, PyTorch or TensorFlow, Hugging Face Transformers, and pandas.
• Practical experience working with Databricks, including notebooks, jobs, MLflow, and Unity Catalog, as well as Spark/PySpark.
• Hands-on production experience with Azure cloud services, especially Azure AI Foundry, Azure OpenAI Service, and Azure Data Lake.
• Comprehensive knowledge of ML domains: classical/statistical ML, deep learning, natural language processing (NLP), and generative AI applications including prompt engineering, RAG, embeddings, and autonomous workflows.
• Established MLOps capabilities such as continuous integration/deployment, model lifecycle monitoring, drift detection, and version control.
• Excellent communication skills for articulating complex technical issues and business impacts to varied audiences.
• Strong decision-making capabilities for prioritizing tasks, managing tradeoffs, and balancing multiple stakeholder needs.

Preferred Experience and Skills

• Background in financial services, risk, compliance, or regulatory sectors.
• Direct experience with agentic AI frameworks like LangChain, LlamaIndex, Semantic Kernel, plus tools for evaluating large language models and deploying generative AI solutions in production.
• Knowledge of responsible AI principles such as fairness, explainability, and privacy.
• Familiarity with containerization and orchestration technologies including Docker and Kubernetes, and experience with CI/CD tools such as Azure DevOps or GitHub Actions.