Data Scientist, Analytics and Modelling

Barclays•New York, NY

3d•$169,541 - $180,000•Hybrid

About The Position

The purpose of the role is 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. 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.

Requirements

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.

Nice To Haves

Experience with 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).
Experience embedding data governance, lineage, metadata, and quality standards into every stage of the delivery lifecycle.
Experience driving 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.
Experience developing advanced data validation and quality assurance processes across TSYS data systems using SQL, Python, and PySpark.
Experience ensuring data integrity, consistency, and reliability across multiple downstream banking platforms by proactively identifying anomalies, resolving data quality issues, and maintaining trust in critical datasets.
Experience designing and implementing 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.
Experience developing reusable code modules that support automated data operations and maintain end-to-end data integrity across critical systems.
Experience building and maintaining metadata catalogs, data dictionaries, and data lineage documentation to support enterprise-wide transparency and data governance initiatives.
Experience facilitating and managing Agile processes to ensure clear prioritization, timely delivery and traceability of data features across cross-functional teams.
Experience championing Agile product management methodologies using tools such as Jira and Confluence to manage product backlogs, user stories, and delivery milestones.
Experience enabling knowledge transfer by creating documentation, reference materials, and training resources to support business stakeholders in effectively using delivered data solutions.

Responsibilities

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.
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.
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.