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

Nominate & Attend

Software Engineer II - Capacity Process Optimization

JPMorgan Chase & Co.
Bournemouth
1 year ago
Applications closed

Related Jobs

View all jobs

Software Engineer II - Data Engineer, Python, SQL - Associate

Software Engineer III - Data Engineer - Python, SQL - Senior Associate

Senior Data Engineer

Applied Scientist II (Machine Learning), ITA - Automated Performance Evaluation

Software Manager

Data Engineering Lead

You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you.

As a Software Engineer II at JPMorgan Chase within the Capacity Process Optimization team, you will have the opportunity to lead several initiatives, including automation projects using tools like Alteryx, Tableau, and Python. This role provides a platform to grow your skills and experience, and to make a significant impact across the Corporate & Investment Bank.

Job responsibilities

Identify stakeholder requirements, solutionise problems utilizing software development tools to deliver automation initiatives that positively impact the team and wider LOB. Conduct in depth data analysis and independent research when required to help illustrate potential risks and areas for improvement. Utilize the research effectively to collaborate effectively and deliver on measurable goals. Lead the management of a variety of key projects to completion. Designing / Contributing to, presenting and delivering on clear predefined goals which will assist in improving the team’s oversight of firmwide compliance. Supports the team’s effort in collaborating with business partners with a view to identify areas for process enhancement, demonstrating a readiness to spearhead improvement endeavours and be recognized as an SME by stakeholders Understand fully new initiatives being delivered by the team. Tailor and lead presentations and roadshows to senior stakeholders by modifying the delivery and focus dependent on LOB priorities.

Required qualifications, capabilities, and skills

Formal training or certification on Capacity Management practices concepts and expanding applied experience Knowledge of the Capacity Management practices and activities Understanding or willingness to learn coding languages such as Python Large amount of experience or certifications in low code automation tools such as Alteryx and Tableau Demonstrated innovative thinking and ability to lead in process improvement and automation, continually striving to add value seeking out improvement opportunities Excellent communication and interpersonal skills & strong organizational and time management skills Strong problem-solving skills, with the ability to address challenges and find solutions  Experience as a project manager with a track record of leading projects to completion Strong business and data analytical skills Experience across the whole Software Development Life Cycle & exposure to agile methodologies such as CI/CD, Application Resiliency, and Security Emerging knowledge of software applications and technical processes within a technical discipline (., cloud, artificial intelligence, machine learning, mobile,

Preferred qualifications, capabilities, and skills

Familiarity with modern front-end technologies Exposure to cloud technologies
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.

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.

The Ultimate Assessment-Centre Survival Guide for Machine Learning Jobs in the UK

Assessment centres for machine learning positions in the UK are designed to reflect the complexity and collaboration required in real-world ML projects. From psychometric assessments and live model-building tasks to group data science challenges and behavioural interviews, recruiters evaluate your statistical understanding, coding skills, communication and teamwork. Whether you specialise in deep learning, reinforcement learning or NLP, this guide offers a step-by-step approach to excel at every stage and secure your next ML role.