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The AI Specialist will partner closely with Risk subject matter experts, Technology teams, and firmwide AI governance stakeholders to help define, develop, and execute the Risk department’s AI strategy. This role will support the buildout of the Risk AI roadmap, operating model, and implementation plan, with a focus on identifying high\-value AI opportunities, translating business needs into scalable AI solutions, and ensuring responsible AI adoption across the Risk function.
The ideal candidate will bring strong AI expertise, an understanding of risk management processes, and the ability to work across business, technology, governance, and senior management stakeholders.
Key Responsibilities
*AI Strategy and Roadmap Support*
- Support the development and execution of the Risk department’s AI strategy, roadmap, operating model, and implementation plan.
- Partner with Risk leadership and subject matter experts to identify AI opportunities across risk management processes.
- Help evaluate, prioritize, and assess the feasibility, value, and adoption potential of AI use cases within Risk.
Contribute to the articulation of Risk’s AI strategy, progress, opportunities, and risks for senior management audiences.
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*AI Use Case Development and Execution*
- Work with Risk SMEs to define and execute prioritized AI use cases.
- Translate business requirements into scalable, practical AI solutions.
- Advise on appropriate AI models, tools, technologies, and solution approaches based on business needs, data availability, control requirements, and enterprise standards.
- Support the full AI use case lifecycle, including ideation, requirements gathering, solution design, testing, deployment, enhancement, and ongoing monitoring.
- Partnership with Technology Teams
- Collaborate closely with relevant Technology teams to support AI solution development, integration, testing, and enterprise deployment.
- Help ensure AI solutions are scalable, maintainable, secure, and aligned with enterprise technology architecture and standards.
Serve as a bridge between Risk stakeholders and Technology teams to ensure clear communication of requirements, constraints, priorities, and delivery timelines.
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*AI Governance, Controls, and Risk Management*
- Work with firmwide AI governance teams to ensure appropriate control processes are established for Risk AI usage.
- Support governance processes related to AI use case approval, data controls, model or code review, guardrails, testing, documentation, access management, change management, and ongoing monitoring.
- Ensure AI solutions are implemented in accordance with applicable internal policies, risk standards, and regulatory expectations.
Help identify, document, and escalate potential risks related to AI adoption, including data quality, privacy, model performance, bias, operational risk, and control gaps.
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*AI Adoption, Training, and Change Management*
- Promote AI adoption across the Risk department by educating stakeholders on AI capabilities, limitations, and relevant use cases.
- Monitor and communicate developments in AI technology, industry trends, and emerging best practices relevant to risk management.
- Facilitate AI training, education sessions, and knowledge\-sharing forums for Risk users.
Support change management efforts to embed AI tools and processes into day\-to\-day Risk workflows.
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Required Qualifications
- Experience working with AI, machine learning, generative AI, data science, analytics, or related technologies.
- Strong understanding of AI solution design, model evaluation, data requirements, testing, deployment, and ongoing monitoring.
- Experience translating business requirements into technology\-enabled solutions.
- Strong stakeholder management skills, with the ability to partner effectively across business, technology, governance, and control functions.
- Excellent communication skills, including the ability to explain technical concepts to non\-technical audiences and present effectively to senior stakeholders.
- Strong analytical, problem\-solving, and project execution skills.
Ability to operate in a highly controlled, regulated, and fast\-paced environment.
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Primary Location Full Time Salary Range of $225,000 \- $250,000\.
Salary Context
This $225K-$250K range is above the 75th percentile for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Jefferies LLC, 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 in Demand for This Role
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 $198,000 based on 808 positions with disclosed compensation. This role's midpoint ($237K) sits 20% above the category median. Disclosed range: $225K to $250K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Jefferies LLC AI Hiring
Jefferies LLC has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $250K - $250K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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