Wealth Management Technology Audit - Vice President

ACCA Careers
2 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Engineering

Remote Travel Business Development / Sales Coach - World #1 Franchise

Remote Travel Business Development / Sales Coach - World #1 Franchise

Remote Travel Business Development / Sales Coach - World #1 Franchise

Remote Travel Business Development / Sales Coach - World #1 Franchise

Claims Data Analyst

External Title:Internal Auditor, Wealth Management Technology Audit – Vice President

We're seeking someone to join our team as a Vice President to provide audit coverage for application and system infrastructure supporting the Wealth Management Technology Audit.

In the Internal Audit division, we provide senior management an objective and independent assessment of the Firm's internal control environment for risk management and governance. This is a Vice President level position within the Technology Audit division.

Morgan Stanley is an industry leader in financial services, known for mobilising capital to help governments, corporations, institutions, and individuals around the world achieve their financial goals.

At Morgan Stanley Glasgow, we support the Firm’s global Operations, Technology, Finance, Corporate, and Institutional Securities divisions. The Glasgow office, known for its excellence in process, client service & leadership, has led us to win numerous innovation and people awards. Morgan Stanley has been rooted in the Glasgow community since 2000, steadily contributing to the development of a vibrant local financial services and fintech industry. Everyone is encouraged to chart their own meaningful career and achieve goals with the support of our best-in-class training and development opportunities.

Interested in joining a team that’s eager to create, innovate and make an impact on the world? Read on…

What you’ll do in the role:

  • Formulate and lead a wide range of assurance activities to assess risks within coverage area and the state of controls in place to mitigate them.
  • Proactively identify risk and emerging risk, and factor into risk assessment and assurance coverage.
  • Articulate actionable insights to management regarding criticality and impact of risks to the business.
  • Effectively partner with colleagues and stakeholders globally to drive effective working relationships.
  • Align projects and initiatives with department and coverage area priorities, and oversee team's execution of deliverables in accordance with audit methodology and quality standards.
  • Partner with Technology and Business Auditors to complete risk assessments, control environment assessments, audit scheduling, audit planning, test plan development and execution, audit issue documentation, and reports to senior management.

What you’ll bring to the role:

  • At least 6 years' relevant experience would generally be expected to find the skills required for this role.
  • Experience in auditing applications, interfaces, system infrastructure, data processing and technology general controls.
  • Knowledge of capital markets, banking products, or emerging technologies (e.g. fintech, machine learning, etc.) in Investment Management or Wealth Management is a plus.
  • Advanced knowledge of industry, global markets and regulations relevant to coverage area.
  • Strong understanding of audit principles, methodology, tools and processes (e.g., risk assessments, planning, testing, reporting and continuous monitoring).
  • Ability to articulate risk and impact clearly and succinctly to different audiences.
  • Effective change and project management techniques and ability to support teams in adapting new ways of working.
  • Ability to leverage and analyse data to inform focus and views on risk.
  • Ability to coach and mentor others and create an inclusive work environment for team.
  • Strong understanding of IT general controls.

What you can expect from Morgan Stanley:

We are committed to maintaining the first-class service and high standard of excellence that have defined Morgan Stanley for over 85 years. At our foundation are five core values — putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back — that guide our more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find trusted colleagues, committed mentors and a culture that values diverse perspectives, individual intellect and cross-collaboration. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry.

We're committed to bringing passion and customer focus to the business.

Certified Persons Regulatory Requirements:

If this role is deemed a Certified role and may require the role holder to hold mandatory regulatory qualifications or the minimum qualifications to meet internal company benchmarks.

Flexible work statement:

Interested in flexible working opportunities? Morgan Stanley empowers employees to have greater freedom of choice through flexible working arrangements. Speak to our recruitment team to find out more.

Morgan Stanley is an equal opportunities employer. We work to provide a supportive and inclusive environment where all individuals can maximize their full potential. Our skilled and creative workforce is comprised of individuals drawn from a broad cross section of the global communities in which we operate and who reflect a variety of backgrounds, talents, perspectives, and experiences. Our strong commitment to a culture of inclusion is evident through our constant focus on recruiting, developing, and advancing individuals based on their skills and talents.

For further information, and to apply, please visit our website via the “Apply” button below.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.