Interested in this Data Scientist role at Kroll Inc.?
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About This Role
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
Salary Context
This $60K-$150K range is in the lower quartile for Data Scientist roles in our dataset (median: $167K across 172 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Kroll Inc., this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($105K) sits 45% below the category median. Disclosed range: $60K to $150K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Kroll Inc. AI Hiring
Kroll Inc. has 1 open AI role right now. They're hiring across Data Scientist. Based in San Francisco, CA, US. Compensation range: $150K - $150K.
Location Context
AI roles in San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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