Job Description

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.

Join us as an AI – Data Scientist at Barclays, where you will spearhead the evolution of our infrastructure and deployment pipelines, driving innovation and operational excellence. You will harness cutting-edge technology to build and manage robust, scalable and secure infrastructure, ensuring seamless delivery of our digital solutions.  

To be successful as an AI – Data Scientist, you should have experience with:

• AI & Machine Learning: Strong background in AI/ML concepts and architectures. Experience with deploying machine learning models or AI services in production. Familiarity with autonomous agents or generative AI technologies is a significant advantage (e.g. knowledge of LLMs, multi-agent systems, or AI orchestration frameworks).
• Cloud (AWS): Hands-on experience with AWS cloud services and infrastructure. Comfortable guiding the team in using AWS for AI workloads – for example, using AWS for model training, data storage (S3), computing (EC2, Lambda, or Kubernetes/EKS), and machine learning operations (SageMaker or similar), while ensuring cloud solutions are secure and cost-effective.
• Programming & System Design: Proficiency in modern programming languages and software design. Python or Java expertise is required – you should be capable of understanding and reviewing code in at least one of these languages, as our AI solutions might span Python (for data science and ML pipelines) and Java (for integration with enterprise systems). Knowledge of software engineering best practices (code reviews, testing, CI/CD) and microservice architecture will help in overseeing complex AI systems.
• Integration & Data: Experience integrating AI systems into existing platforms and workflows. Familiarity with data engineering pipelines, databases, and APIs in a financial services context (e.g. using data warehouses or streaming data for model input) is beneficial. A good grasp of security and compliance requirements (encryption, access control, audit logging) when handling sensitive financial data is expected.

• Team Management: Proven experience leading and mentoring engineering teams, ideally in AI or software innovation projects. You will coordinate tasks, set goals, and provide technical guidance to team members. Your leadership will foster a collaborative environment that encourages continuous learning, experimentation, and high performance.
• Cross-Functional Collaboration: Ability to work closely with cross-functional teams – including Product Management, Data Science, Compliance, and Business Units – to define project scopes and success criteria. You will translate business objectives into technical requirements for your team, and conversely articulate technical progress and challenges to non-technical stakeholders. Experience managing projects that involve multiple disciplines (engineering, AI research, UX, etc.) ensures cohesive delivery of AI solutions that meet user needs and regulatory standards.
• Innovation & Strategic Vision: A forward-thinking mindset to drive innovative AI solutions (autonomous agents, generative AI, etc.) from concept to production. You keep abreast of emerging AI trends and assess their potential business value. By providing strategic direction, you guide the team in prioritising opportunities that leverage AI for competitive advantage – for example, improving customer experience through intelligent automation or enhancing risk detection with AI agents – while balancing innovation with practical feasibility.
• Governance & Delivery: Ensure that projects are delivered in alignment with the bank’s governance processes. This includes managing project timelines, budgets, and resources, and rigorously applying quality controls. An understanding of model risk management and AI ethics in financial services is important; you will implement oversight for model validation, monitoring, and documentation (e.g. ensuring transparency of AI decision-making and maintaining up-to-date model documentation).

Some other highly valued skills may include:

• Stakeholder Management: Excellent communication and interpersonal skills for engaging with senior stakeholders, clients, and diverse team members. You can build trust and clearly communicate the value, progress, and risks of AI initiatives to executive leadership and advisory boards. Skilled in negotiating priorities and aligning the AI team’s work with broader business goals.
• Strategic Thinking: High-level planning and decision-making abilities. You can devise a technology roadmap for AI/ML in alignment with organisational strategy, and make prudent decisions about project investments and technical approaches. Your strategic mindset helps anticipate industry trends and regulatory changes, allowing the team to stay ahead in compliance and innovation.
• Adaptability & Problem-Solving: Flexibility to navigate the uncertainties of cutting-edge AI projects. In a fast-evolving domain, you’re adept at problem-solving, whether it’s resolving technical roadblocks or adjusting team focus when business needs shift. You maintain composure and provide guidance under pressure, adeptly handling challenges such as unexpected model behavior or changes in regulatory guidance.
• People Leadership & Integrity: A collaborative leadership style that encourages growth, inclusivity, and accountability. You mentor team members, provide feedback, and help develop their careers. Importantly, you lead by example in following ethical practices and upholding compliance – crucial for an AI team operating in a high-stakes, regulated environment. You champion a culture where quality, responsibility, and ethical considerations are central to every project.

You may be assessed on key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen, strategic thinking and digital and technology, as well as job-specific technical skills.

The role is based out of Pune.