Senior Data Scientist
Kroll San Francisco, California, United States · $60K–$150K/yr
Business Consulting and Services · 5,001-10,000 employees
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About the role
The Senior Data Scientist will lead end-to-end machine learning initiatives and mentor junior team members while collaborating with stakeholders to deliver production-grade solutions. Responsibilities include designing and deploying models, managing ML pipelines on Azure, and ensuring robust model monitoring and testing practices.
What they look for
Requirements
Candidates must hold an advanced degree (MS or PhD) in a quantitative field and possess at least 5 years of applied machine learning experience. Proficiency in Python, Databricks, and the modern ML stack is required, along with practical experience in building LLM and GenAI applications.
Benefits
Full description
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.
Day-to-Day 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
Essential Traits:
- 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 Traits:
- 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
Your recruiter will be happy to walk you through your U.S.-specific benefits, which include:
- Healthcare Coverage: Comprehensive medical, dental, and vision plans.
- Time Off and Leave Policies: Generous paid time off (PTO), paid company holidays, generous parental and family leave.
- Protective Insurances: Life insurance, short- and long-term disability coverage, and accident protection.
- Compensation and Rewards: Competitive salary structures, performance-based incentives, and merit-based compensation reviews.
- Retirement Plans: 401(k) plans with company matching.
Please note that benefits may vary by region, department and role. We encourage you to speak with your recruiter to learn more about the specific benefits available for your position.
About Kroll
Join the global leader in risk and financial advisory solutions—Kroll. With a nearly century-long legacy, we blend trusted expertise with cutting-edge technology to navigate and redefine industry complexities. As a part of One Team, One Kroll, you'll contribute to a collaborative and empowering environment, propelling your career to new heights. Ready to build, protect, restore and maximize our clients’ value? Your journey begins with Kroll.
We are proud to be an equal opportunity employer and will consider all qualified applicants regardless of gender, gender identity, race, religion, color, nationality, ethnic origin, sexual orientation, marital status, veteran status, age or disability.
In order to be considered for a position, you must formally apply via careers.kroll.com.
The salary range for this role is $60,000 - $150,000 USD
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