National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Project Manager with Digital Banking Operations and Artificial Intelligence AI

Nexus
London
5 months ago
Applications closed

Related Jobs

View all jobs

Senior Business & Data Analyst

Data Entry And Quality Control (Admin and Clerical)

Data Analyst Placement Programme

Data Analyst Placement Programme

Data Analyst Placement Programme

Data Analyst Placement Programme

Job Description

Project Manager with Digital Banking Operations and Artificial Intelligence AI

We are seeking a Project Manager with Digital Banking Operations and Artificial Intelligence (AI) Projects experience to join our Client a bank based in Central London.

This is a full-time role located in London, with flexibility for some remote work.

As an AI project manager, you be responsible for overseeing and managing the implementation of AI projects within our digital banking operations.

You will collaborate with cross-functional teams to define project goals, develop project plans, allocate resources, track progress, and ensure timely and successful delivery of projects.

Experience and Qualifications

Previous experience in project management, preferably within the banking or financial services industry Strong understanding of digital banking operations and Artificial Intelligence AI technologies Proven track record of successfully delivering complex projects on time and within budget Excellent communication and interpersonal skills, with the ability to effectively collaborate with cross-functional teams Strong problem-solving and decision-making abilities Knowledge of agile project management methodologies Experience with data analysis and reporting Ability to adapt to changing priorities and work well under pressure Project management certification (e.g., PMP) is a plus Bachelor's degree in a relevant field

Areas to Consider

1. Customer Service Enhancement

Chatbots and Virtual Assistants: Deploy AI-driven chatbots to handle routine inquiries, provide 24/7 support, and reduce wait times.Sentiment Analysis: Use AI to analyze customer feedback and sentiment from various channels to improve services.

2. Fraud Detection and Prevention

Real-Time Monitoring: Implement AI algorithms to detect and flag unusual transactions in real-time.Predictive Analytics: Use machine learning models to predict potential fraud based on historical data and behavioural patterns.

3. Loan Processing Automation

Credit Scoring: AI can evaluate creditworthiness more accurately by analyzing a wider range of data points.Document Verification: Automate the verification of documents submitted for loan applications, speeding up the approval process.

4. Personalized Banking Services

Customer Insights: Leverage AI to gain insights into customer behaviour and preferences, allowing for personalized product recommendations.Marketing Campaigns: Use AI to target customers with tailored marketing campaigns based on their transaction history and preferences.

5. Risk Management

Risk Assessment: AI can analyze market trends and economic indicators to provide early warnings about potential risks.Compliance Monitoring: Automate compliance checks and monitoring to ensure adherence to regulations and reduce the risk of non-compliance penalties.

6. Operational Efficiency

Process Automation: Use robotic process automation (RPA) to handle repetitive tasks such as data entry, account reconciliation, and report generation.Workflow Optimization: AI can optimize workflows by identifying bottlenecks and suggesting improvements.

Implementation Strategy

Assessment: Evaluate the current state of digital banking operations and identify areas where AI can add value.Pilot Projects: Start with pilot projects to test AI applications in a controlled environment.Scalability: Ensure that AI solutions are scalable and can handle increasing volumes of data and transactions.Employee Training: Train staff on AI tools and their applications to ensure seamless integration.Continuous Improvement: Regularly update AI models and algorithms based on new data and evolving business needs.

Challenges and Considerations

Data Quality: Ensure high-quality data for accurate AI predictions and analysis.Regulatory Compliance: Stay compliant with financial regulations while implementing AI solutions.Customer Trust: Maintain transparency in AI-driven decisions to build and maintain customer trust.Integration: Seamlessly integrate AI with existing banking systems and processes.

The main emphasis of this position to is harness the data from a variety of data tables at the bank and collate a Data Lake from which to extract a variety of AI reports to increase the banks customer strategy.

By strategically implementing AI in these areas, a Digital Banking Operations Manager can greatly improve the efficiency, security, and customer satisfaction in digital banking operations.

The position will be hybrid 3 days a week in the office.

The salary is negotiable depending on experience but probably in the range £80K - £120K plus benefits.

Do send your CV to us in Word format along with your salary and notice period.

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.