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# Senior Data Scientist

- **Company:** Kroll Inc
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Microsoft Azure, Software Quality, Continuous Integration, Data Validation, Github, Python (Programming Language), Machine Learning, Natural Language Processing, OpenAI, Tensorflow, Azure Data Lake, Software Deployment, Unstructured Data, Feature Engineering, Boosted Trees, Pytorch, LangChain, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Deep Learning, Llamaindex, Generative AI, Agentic-AI, Pandas, Data Lakes, AI Platforms, Pyspark, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Azure OpenAI API, Machine Learning Operations, Drift Detection, Evaluation of Large Language Models, Semantic Kernel, Software Version Control, Serverless Computing, Docker, Databricks
- **Published:** October 3, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pou82y464f

## About the Role

* 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

## Description

* 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