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About This Role
Date live: 07/14/2026
Business Area: See job description
Area of Expertise: Data \& Analytics
Contract: Permanent
Reference Code: JR\-0000122927
What will you be doing?
Barclays Bank Delaware seeks Data Scientist, Analytics and Modelling in New York, New York (multiple positions available):
- Deliver strategic data initiatives within a large multinational bank, including cloud migration, data platform modernization, and enterprise\-wide data transformation programs.
- Define and execute product maps for data assets that support critical financial, regulatory, operational, and analytical use cases.
- Design curated, reusable, and governed data products on Partnerships Card portfolios.
- Use Agile methodology to deliver high impact\-data solutions by serving as the primary bridge between business stakeholders and engineering teams gathering requirements, translating complex business requirements and technical limitations into actionable product features, and delivering scalable data solutions.
- Leverage data strategy expertise to unlock business value across key domains, including Card Partnerships, Loans, Consumer Banking, and Credit Data. Identify strategic opportunities to use data as an asset, influence roadmap decisions, and translate analytical insights into actionable outcomes that drive customer experience, revenue growth, and operational efficiency.
- Shape the design of data products using Partnerships Card expertise
- Architect and implement cloud\-native data architecture knowledge to support the migration of legacy, on\-premises data storage systems to AWS, using cloud tools (e.g., S3, Glue, and Redshift).
- Embed data governance, lineage, metadata, and quality standards into every stage of the delivery lifecycle.
- Drive alignment with the bank’s enterprise data strategy by shaping initiatives around digital transformation, data monetization and modernization ensuring they integrate seamlessly with the broader business architecture and comply with evolving regulatory frameworks.
- Develop advanced data validation and quality assurance processes across TSYS data systems using SQL, Python, and PySpark.
- Ensure data integrity, consistency, and reliability across multiple downstream banking platforms by proactively identifying anomalies, resolving data quality issues, and maintaining trust in critical datasets.
- Design and implement Python\-based data pipelines and utilities to process large\-scale credit datasets including data ingestion, transformation, validation, and reconciliation using frameworks including Pandas, PySpark, and AWS Lambda.
- Develop reusable code modules that support automated data operations and maintain end\-to\-end data integrity across critical systems
- Build and maintain metadata catalogs, data dictionaries, and data lineage documentation to support enterprise\-wide transparency and data governance initiatives
- Facilitate and manage Agile processes to ensure clear prioritization, timely delivery and traceability of data features across cross\-functional teams. Champion Agile product management methodologies using tools such as Jira and Confluence to manage product backlogs, user stories, and delivery milestones.
- Enable knowledge transfer by creating documentation, reference materials, and training resources to support business stakeholders in effectively using delivered data solutions
- Must be within commutable distance of New York, NY with flexibility to travel to Wilmington, DE. Occasional domestic travel to work from company’s DE office required.
Salary / Rate Minimum/yr: $169,541 per year
Salary / Rate Maximum/yr: $180,000 per year
The minimum and maximum salary/rate information above include only base salary or base hourly rate. It does not include any other type of compensation or benefits that may be available*.*
This position is eligible for incentives pursuant to Barclays Employee Referral Program.
Purpose of the role
To use innovative data analytics and machine learning techniques to extract valuable insights from the bank's data reserves, leveraging these insights to inform strategic decision\-making, improve operational efficiency, and drive innovation across the organisation.
Accountabilities
- Identification, collection, extraction of data from various sources, including internal and external sources.
- Performing data cleaning, wrangling, and transformation to ensure its quality and suitability for analysis.
- Development and maintenance of efficient data pipelines for automated data acquisition and processing.
- Design and conduct of statistical and machine learning models to analyse patterns, trends, and relationships in the data.
- Development and implementation of predictive models to forecast future outcomes and identify potential risks and opportunities.
- Collaborate with business stakeholders to seek out opportunities to add value from data through Data Science.
Assistant Vice President Expectations
- To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions.
- Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes
- If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.
- OR for an individual contributor, they will lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will identify new directions for assignments and/ or projects, identifying a combination of cross functional methodologies or practices to meet required outcomes.
- Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues.
- Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda.
- Take ownership for managing risk and strengthening controls in relation to the work done.
- Perform work that is closely related to that of other areas, which requires understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub\-function.
- Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategy.
- Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, etc).to solve problems creatively and effectively.
- Communicate complex information. 'Complex' information could include sensitive information or information that is difficult to communicate because of its content or its audience.
- Influence or convince stakeholders to achieve outcomes.
All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.
Salary Context
This $169K-$180K range is above the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Barclays, 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 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($174K) sits 9% below the category median. Disclosed range: $169K to $180K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Barclays AI Hiring
Barclays has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Whippany, NJ, US, New York, NY, US. Compensation range: $175K - $230K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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