Senior Data Scientist

Kroll is hiring a Senior Data Scientist to join its Enterprise Data Group. This
role is designed for an experienced practitioner who can lead end-to-end ML
initiatives, mentor junior team members, and partner with business and
engineering stakeholders to translate complex problems into production-grade
data science solutions.

Our program spans fintech product development, digital transformation, process
automation with machine learning, business intelligence, data governance, and
generative AI. You will work alongside an advanced data science and engineering
team — and collaborate with 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

* Design, research, implement, and evaluate machine learning solutions spanning
traditional ML, deep learning, NLP, and LLM/GenAI applications

* Build and fine-tune models — from gradient-boosted trees and classical
statistical models to transformer-based architectures and retrieval-augmented
generation (RAG) systems

* Develop and optimize prompts, evaluation frameworks, and guardrails for
LLM-powered applications

* Engineer scalable data and ML pipelines in Databricks using PySpark, Delta
Lake, and MLflow

* Deploy, monitor, and maintain models in production on Azure (Azure AI
Foundry, Azure OpenAI, Azure Functions, AKS), including CI/CD, model
versioning, and drift detection

* Validate model inputs, outputs, and business impact; establish robust testing
and monitoring practices

* Partner with engineering, product, and business stakeholders to scope
problems and translate ML capabilities into measurable outcomes

* Communicate technical concepts, tradeoffs, and results to non-technical
audiences, including senior leadership and clients

* Mentor junior data scientists and contribute to team standards around code
quality, experimentation, and responsible AI

Requirements

* Advanced degree (MS or PhD) in computer science, statistics, mathematics,
analytics, or a related quantitative field

* 5+ years of applied machine learning experience, including delivering models
to production

* Strong Python skills and experience with the modern ML stack (scikit-learn,
PyTorch or TensorFlow, pandas, Hugging Face Transformers)

* 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

* Practical experience building LLM/GenAI applications — prompt engineering,
RAG, fine-tuning, embeddings, vector databases, and evaluation

* Solid grounding in the full ML lifecycle: data validation, feature
engineering, model design, experimentation, deployment, and monitoring

* Experience with structured and unstructured data, including text, documents,
and semi-structured sources

* Strong statistical foundation and ability to reason about uncertainty, bias,
and model risk

* Excellent technical and business communication skills

Preferred

* Experience in financial services, risk, compliance, or regulatory domains

* Familiarity with MLOps tooling (MLflow, Docker, Kubernetes, Azure DevOps or
GitHub Actions)

* Hands-on experience with agentic AI frameworks (LangChain, LlamaIndex,
Semantic Kernel), LLM evaluation tooling, and production deployment of GenAI
applications

* Knowledge of responsible AI practices, including fairness, explainability,
and data privacy

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