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About This Role
Description
Join a team where innovation meets impact. As a Senior Data Scientist, Generative AI \& Agentic Systems, you will help drive the bank's AI transformation by designing, developing, and deploying Large Language Model (LLM) solutions, Retrieval\-Augmented Generation (RAG) systems, AI agents, and intelligent automation capabilities. You will work across business, technology, risk, and compliance teams to deliver responsible, scalable, and production\-ready GenAI solutions that improve customer experiences, enhance operational efficiency, and create measurable business value.
This role is ideal for an experienced data scientist with strong software engineering and machine learning skills, deep expertise in NLP and Generative AI, and experience developing AI solutions within highly regulated environments.
Key Responsibilities
- Design, develop, and deploy production\-grade Generative AI solutions using LLMs, RAG frameworks, AI agents, and workflow orchestration platforms.
- Build intelligent document processing capabilities for information extraction, summarization, classification, question answering, and conversational AI applications.
- Develop agentic workflows capable of autonomous reasoning, task execution, tool utilization, and multi\-step decision support.
- Design and implement retrieval pipelines, vector search architectures, embedding strategies, and knowledge\-grounded AI systems.
- Evaluate and improve LLM performance through prompt engineering, model benchmarking, hallucination reduction, and faithfulness testing.
- Build scalable AI solutions using modern frameworks and infrastructure including vLLM, LangChain, LangGraph, MLflow, Databricks, Snowflake, and cloud\-native platforms.
- Perform exploratory data analysis, feature engineering, and statistical analysis to support machine learning and GenAI model development.
- Develop model monitoring, evaluation, and observability frameworks to measure quality, reliability, fairness, and operational performance.
- Collaborate closely with Model Risk Management (MRM), Compliance, Audit, Legal, and Information Security teams to ensure responsible AI deployment.
- Create technical documentation, model development artifacts, validation packages, and executive\-level presentations.
- Partner with product managers, engineers, data architects, and business stakeholders to identify and prioritize GenAI opportunities.
- Stay current with advances in Generative AI, agentic systems, multimodal AI, foundation models, and emerging industry best practices.
Qualifications
Required
- Ph.D. or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, or a related quantitative field.
- 7\+ years of experience in data science, machine learning, predictive analytics, or artificial intelligence.
- 4\+ years of hands\-on experience developing NLP and Generative AI solutions.
- Strong proficiency in Python and modern software development practices.
- Experience developing and deploying LLM\-based applications using commercial or open\-source models.
- Experience with Retrieval\-Augmented Generation (RAG), vector databases, embeddings, and semantic search.
- Experience with prompt engineering, prompt evaluation, and LLM performance optimization.
- Strong understanding of machine learning algorithms, deep learning, statistical modeling, and model explainability techniques.
- Experience working with structured and unstructured data at enterprise scale.
- Experience collaborating with cross\-functional stakeholders and communicating technical concepts to non\-technical audiences.
- Strong knowledge of model governance, validation processes, and documentation standards.
Preferred
- Experience designing and deploying AI agents and multi\-agent systems.
- Experience with agent orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, CrewAI, Autogen, or similar technologies.
- Experience serving open\-source LLMs using vLLM, Hugging Face, or equivalent inference frameworks.
- Experience with RAG evaluation frameworks such as RAGAS or other LLM evaluation methodologies.
- Experience with model monitoring, MLOps, and production AI deployment.
- Experience with cloud AI platforms such as AWS Bedrock, Azure AI, Databricks, Snowflake Cortex.
- Experience building document intelligence solutions involving PDFs, OCR, document extraction, knowledge extraction from images, and workflow automation.
- Experience within banking, financial services, fintech, insurance, or other regulated industries.
- Experience supporting Model Risk Management (MRM), model validation, audit reviews, or regulatory examinations.
- Familiarity with MCP (Model Context Protocol), tool calling frameworks, and AI workflow automation platforms.
Technical Skills
Generative AI \& LLMs
- GPT, Claude, Llama and other foundation models
- Retrieval\-Augmented Generation (RAG)
- AI Agents and Multi\-Agent Systems
- Prompt Engineering and Prompt Optimization
- Fine\-Tuning and Model Adaptation
- LLM Evaluation and Guardrails
- Knowledge Retrieval and Vector Search
Programming \& Frameworks
- Python
- SQL
- PyTorch
- TensorFlow
- Scikit\-Learn
- LangChain
- LangGraph
- Hugging Face
Data Platforms \& MLOps
- Experience with cloud\-based data, AI, and ML platforms (AWS, SageMaker, Databricks, Snowflake, etc.)
- Experience with distributed data processing frameworks (Spark / PySpark/Snowpark Snowflake)
- Experience with ML lifecycle, orchestration, and deployment tools (MLflow, Airflow, CI/CD)
- Experience with AI\-assisted development and model monitoring solutions
NLP \& Analytics
- Text Classification
- Information Extraction
- Summarization
- Topic Modeling
- Question Answering
- Sentiment Analysis
- Explainable AI
Preferred Candidate Profile
The ideal candidate needs to demonstrate success building production\-scale GenAI solutions such as RAG platforms, conversational AI systems, document intelligence solutions, AI agents, and automated decision\-support systems. They possess strong technical depth, understand governance requirements in regulated industries, and can bridge the gap between cutting\-edge AI capabilities and practical business outcomes. This individual is comfortable operating from concept through production deployment while maintaining a strong focus on quality, compliance, explainability, and measurable impact.
Hours \& Work Schedule
- Hours per Week: 40
- Work Schedule: Monday \- Friday
- Hybrid: 4 days per week on\-site, 1 day remote
*Some job boards have started using jobseeker\-reported data to estimate salary ranges for roles. If you apply and qualify for this role, a recruiter will discuss accurate pay guidance.*
Equal Employment Opportunity
Citizens, its parent, subsidiaries, and related companies (Citizens) provide equal employment and advancement opportunities to all colleagues and applicants for employment without regard to age, ancestry, color, citizenship, physical or mental disability, perceived disability or history or record of a disability, ethnicity, gender, gender identity or expression, genetic information, genetic characteristic, marital or domestic partner status, victim of domestic violence, family status/parenthood, medical condition, military or veteran status, national origin, pregnancy/childbirth/lactation, colleague’s or a dependent’s reproductive health decision making, race, religion, sex, sexual orientation, or any other category protected by federal, state and/or local laws. At Citizens, we are committed to fostering an inclusive culture that enables all colleagues to bring their best selves to work every day and everyone is expected to be treated with respect and professionalism. Employment decisions are based solely on merit, qualifications, performance and capability.
Background Check
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Any offer of employment is conditioned upon the candidate successfully passing a background check, which may include initial credit, motor vehicle record, public record, prior employment verification, and criminal background checks. Results of the background check are individually reviewed based upon legal requirements imposed by our regulators and with consideration of the nature and gravity of the background history and the job offered. Any offer of employment will include further information.
Benefits
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We offer competitive pay, comprehensive medical, dental and vision coverage, retirement benefits, maternity/paternity leave, flexible work arrangements, education reimbursement, wellness programs and more.
Awards We've Received
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Dave Thomas Foundation’s Best Adoption\-Friendly Workplace
Disability:IN World's Top Disability Inclusive Business
Glassdoor
Best Place to Work in Consulting, Finance \& Insurance
Human Rights Campaign Corporate Equality Index 100 Award
Newsweek America's Most Charitable Company
The Banker's
US Bank of the Year
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Citizens, 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Citizens AI Hiring
Citizens has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Pittsburgh, PA, US, Columbus, OH, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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