Manager I, Data Scientist

About the Role

Kroll is seeking a Data Science Manager to lead and expand its data science team within the Enterprise Data Group. This leadership position blends technical guidance with strategic execution, involving shaping data science practices, managing machine learning and AI projects, and developing data science professionals.

The role covers initiatives across fintech product innovation, digital transformation, automation using ML, business intelligence, data governance, and generative AI. The manager will oversee a team collaborating with engineering, product, and business units including major financial institutions, law enforcement, and governmental organizations. The aim is to provide solutions that help address complex governance, risk management, and transparency challenges for clients.

Responsibilities

• Lead, coach, and scale a diverse team of data scientists at various seniority levels, establishing technical directions and fostering a culture of high performance.
• Manage the data science roadmap end-to-end, including prioritizing projects, overseeing delivery, and reporting impact to leadership and clients.
• Collaborate with product, engineering, and business stakeholders to define problems, plan ML solutions, and convert data science efforts into tangible business results.
• Provide technical leadership across the machine learning lifecycle from problem framing through data validation, model creation, experimentation, deployment, and monitoring.
• Set and maintain standards for code quality, experimental rigor, model governance, and ethical AI practices within the team.
• Drive the adoption and advancement of ML infrastructure utilizing Databricks and Azure platforms, incorporating MLOps methodologies such as CI/CD, model versioning, and drift detection.
• Lead initiatives involving large language models (LLM) and generative AI including architectures like Retrieval-Augmented Generation (RAG), prompt engineering, fine tuning, and agentic frameworks, ensuring thorough evaluation and responsible deployment.
• Handle recruiting, onboarding, and performance evaluations to build and retain a top-tier data science team.
• Serve as an ambassador for data science, effectively communicating technical details and tradeoffs to both technical and non-technical stakeholders.

Qualifications

• Advanced degree (Master's or Doctorate) in computer science, statistics, mathematics, data science, or a closely related quantitative discipline.
• Over 7 years of hands-on experience in applied data science or machine learning, including a minimum of 2 years leading teams or acting as a technical lead.
• Demonstrated success deploying ML solutions to production environments and achieving verifiable business outcomes.
• Proficient in Python programming and experienced with key machine learning libraries including scikit-learn, PyTorch or TensorFlow, Hugging Face Transformers, and pandas.
• Practical experience using Databricks tools such as notebooks, job scheduling, MLflow, and data cataloging, along with Spark or PySpark.
• Production deployment experience within the Azure ecosystem, particularly Azure AI Foundry, Azure OpenAI Service, and Azure Data Lake.
• Comprehensive knowledge across ML disciplines: traditional machine learning, deep learning, natural language processing, and generative AI applications with experience in prompt engineering and agent-based workflows.
• Familiarity with establishing robust MLOps systems including continuous integration/continuous delivery (CI/CD), model performance monitoring, drift detection, and versioning.
• Excellent communication abilities to distill complex technical concepts into clear narratives for senior executives and clients.
• Sound decision-making skills for prioritizing tasks, managing trade-offs, and balancing stakeholder needs.

Preferred Qualifications

• Background in financial services, risk management, compliance, or regulatory environments.
• Experience with agentic AI development frameworks such as LangChain, LlamaIndex, Semantic Kernel, plus expertise in evaluating LLMs and deploying generative AI applications in production.
• Understanding of ethical AI considerations including fairness, interpretability, and data privacy.
• Knowledge of containerization technologies (Docker, Kubernetes) and CI/CD platforms like Azure DevOps or GitHub Actions.