VP - Data Analytics & AI lead

The VP — Data Analytics & AI Lead is a senior leadership role within the  Compliance division, responsible for defining and executing the data analytics and artificial intelligence strategy across all risk domains. The role holder will build and lead a high-performing team that delivers Data analytics, machine learning models, and AI-driven solutions to strengthen risk identification, measurement, monitoring, and reporting capabilities.

This individual will act as the bridge between Compliance subject-matter experts and Technology teams, translating complex regulatory and business requirements into scalable, production-grade analytical solutions. The role demands a unique combination of deep technical expertise, strong business acumen in financial services risk management, and the ability to influence senior stakeholders across the organisation.

Key Responsibilities

Strategic Leadership & Vision
• Define and own the multi-year data analytics and AI roadmap for Compliance, aligned with the firm's enterprise data strategy and regulatory commitments.
• Identify and prioritise high-impact use cases for  Data Analytics ,AI/ML across credit risk, market risk, operational risk, financial crime, and compliance surveillance.
• Serve as the senior subject-matter expert on Data Analytics , AI/ML applications in Compliance management, advising the  Barclays Compliance functions on emerging capabilities, risks, and investment priorities.
• Champion a culture of data-driven decision-making across Compliance, driving adoption of advanced analytics among compliance professionals.

Advanced Analytics & AI Delivery
• Lead the design, development, and deployment of machine learning models, NLP solutions, and generative AI applications for risk detection, early warning systems, regulatory reporting, and compliance monitoring.
• Deliver predictive analytics capabilities including, anomaly detection for financial crime, stress testing automation, and real-time surveillance dashboards.
• Architect end-to-end ML pipelines from data ingestion and feature engineering through model training, validation, deployment, and monitoring.
• Drive the adoption of large language models (LLMs) and generative AI for regulatory document analysis, policy gap detection, and automated compliance assessments.

Model Risk & AI Governance
• Establish and enforce robust AI governance frameworks including model risk management, explainability standards, bias detection and mitigation, and responsible AI practices.
• Partner with Model Risk Management (MRM) to ensure all analytics models meet internal validation standards and regulatory expectations (e.g., SS1/23, SR 11-7, TRIM).
• Maintain comprehensive model inventories, documentation, and performance monitoring dashboards.
• Lead regulatory exam preparedness for AI/ML-related enquiries from the PRA, FCA, and other supervisory bodies.

Data Strategy & Infrastructure
• Collaborate with Chief Data Office, Data Engineering, and Cloud Platform teams to ensure  Compliance has access to high-quality, governed, and timely data.
• Define data quality requirements and risk data aggregation standards in alignment with BCBS 239 principles.
• Drive migration of legacy analytics to cloud-native platforms (AWS/Azure/GCP), ensuring scalability, security, and cost efficiency.
• Oversee the development and maintenance of enterprise BI dashboards and reporting solutions using tools such as Power BI, Tableau, and QlikSense.

Stakeholder Engagement & Communication
• Build strong partnerships with senior stakeholders across Compliance,  Technology, and Front Office to align analytics priorities with business needs.


Team Leadership & Talent Development
• Recruit, develop, and retain a diverse, high-performing team of data scientists, ML engineers, analytics engineers, and quantitative analysts.
• Establish clear career pathways, technical competency frameworks, and performance objectives for the analytics team.
• Foster a culture of innovation, continuous learning, and technical excellence, including participation in hackathons, research publications, and patent applications.

Technology & AI Solution Architecture:

 Lead the design, delivery, and evolution of the Compliance data analytics  Platform, encompassing modern data architectures, advanced analytics, machine learning, and generative AI solutions. The role requires expertise in scalable data platforms, AI/ML model development and operationalization, MLOps, and AI governance, with a proven ability to deliver risk, regulatory, and financial crime solutions through intelligent applications. The successful candidate will ensure all AI capabilities are deployed within robust governance frameworks aligned to regulatory and model risk management standards.

Essential Qualifications & Experience

Education
• Master's degree  in a quantitative discipline — Computer Science, Data Science, Statistics, Mathematics, Physics, Engineering, or a related field.
• Relevant professional certifications are advantageous (e.g., FRM, PRM, CFA, AWS/Azure ML certifications).

Experience
• Progressive experience in data analytics, data science, or AI/ML, with at least 5 years in financial services — preferably within Risk, Compliance, or regulatory functions.
• Leadership experience managing multi-disciplinary analytics teams (6+ individuals) in a regulated environment.
• Proven track record of delivering production-grade ML models and AI solutions that have driven measurable business impact in risk management or compliance.
• Deep understanding of banking risk frameworks including credit risk (IRB models, IFRS 9, ECL), market risk (VaR, FRTB), operational risk (scenario analysis, loss data), and financial crime (AML, sanctions, fraud detection).
• Hands-on experience with regulatory requirements such as BCBS 239, Basel III/IV, MiFID II, GDPR, and UK regulatory expectations (PRA SS1/23, FCA guidance on AI/ML).
• Demonstrated experience managing the full model lifecycle — from development through independent validation, regulatory approval, and ongoing monitoring.

Technical Skills
• Expert proficiency in Python, R, or Scala for statistical modelling and ML development.
• Strong SQL skills and experience with big data technologies (Spark, Hadoop, Databricks).
• Hands-on experience with ML frameworks — TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM.
• Experience with NLP and generative AI technologies — LLMs, RAG architectures, prompt engineering, fine-tuning.
• Proficiency with cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI) and MLOps tooling (MLflow, Kubeflow, Airflow).
• Experience with BI and visualisation tools — Tableau, Power BI, QlikSense.
• Familiarity with data governance tools and metadata management platforms.

Desirable Skills & Experience

• Experience implementing Responsible AI frameworks including fairness testing, explainability (SHAP, LIME), and bias mitigation at enterprise scale.
• Knowledge of graph analytics and network analysis for financial crime detection.
• Experience with real-time streaming analytics (Kafka, Flink) for surveillance and monitoring use cases.
• Prior involvement in regulatory examinations, internal audit reviews, or supervisory engagement related to AI/ML models.
• Published research or patents in applied machine learning, risk analytics, or related fields.
• Experience leading cloud migration programmes for legacy risk analytics platforms.
• Familiarity with emerging regulations on AI (EU AI Act, UK AI regulatory framework) and their implications for financial services.
• ACAMS certification or AML/financial crime analytics expertise.
• Experience with Agile/SAFe delivery methodologies in large-scale technology programmes.

You will be assessed on key critical skills for the role, such as Model Risk & AI Governance, Analytical ability.

This role is based our of Pune.

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.

Vice President Expectations

• To contribute or set strategy, drive requirements and make recommendations for change. Plan resources, budgets, and policies; manage and maintain policies/ processes; deliver continuous improvements and escalate breaches of policies/procedures..
• If managing a team, they define jobs and responsibilities, planning for the department’s future needs and operations, counselling employees on performance and contributing to employee pay decisions/changes. They may also lead a number of specialists to influence the operations of a department, in alignment with strategic as well as tactical priorities, while balancing short and long term goals and ensuring that budgets and schedules meet corporate requirements..
• 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 be a subject matter expert within own discipline and will guide technical direction. They will lead collaborative, multi-year assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will train, guide and coach less experienced specialists and provide information affecting long term profits, organisational risks and strategic decisions..
• Advise key stakeholders, including functional leadership teams and senior management on functional and cross functional areas of impact and alignment.
• Manage and mitigate risks through assessment, in support of the control and governance agenda.
• Demonstrate leadership and accountability for managing risk and strengthening controls in relation to the work your team does.
• Demonstrate comprehensive understanding of the organisation functions to contribute to achieving the goals of the business.
• Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategies.
• Create solutions based on sophisticated analytical thought comparing and selecting complex alternatives. In-depth analysis with interpretative thinking will be required to define problems and develop innovative solutions.
• Adopt and include the outcomes of extensive research in problem solving processes.
• Seek out, build and maintain trusting relationships and partnerships with internal and external stakeholders in order to accomplish key business objectives, using influencing and negotiating skills 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.