Vice President, Data Management & Quantitative Analysis

BNY
Manchester
2 months ago
Applications closed

Related Jobs

View all jobs

Product Owner - Data Quality and Governance

Product Owner - Data Quality and Governance

Product Owner - Data Quality and Governance

Product Owner - Data Quality and Governance

Product Owner - Data Quality and Governance

Product Owner - Data Quality and Governance

Vice President, Data Management & Quantitative Analysis

Join to apply for the Vice President, Data Management & Quantitative Analysis role at BNY.


At BNY, our culture allows us to run our company better and enables employees’ growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world’s investible assets. Every day, our teams harness cutting‑edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide.


We’re seeking a future team member for the role of Vice President, Data Management & Quantitative Analysis to join our Data and Quantitative Analysis team. This role location is based in Manchester, UK.


Responsibilities

  • Lead the development and implementation of data management strategies by leveraging expertise in data analysis and quantitative methodologies.
  • Ensure data integrity and accuracy across all platforms by establishing rigorous data governance frameworks and protocols.
  • Collaborate with cross‑functional teams to translate complex data insights into actionable business strategies, enhancing decision‑making processes.
  • Drive continuous improvement initiatives in data management practices by staying abreast of industry trends and emerging technologies.
  • Mentor and guide junior team members, fostering a culture of learning and development within the data management team.
  • Champion data‑driven innovation by identifying opportunities for automation and efficiency enhancements in data processing and analysis.

Qualifications

  • Bachelor’s degree in Data Science, Statistics, Computer Science, or a related field. Advanced degree preferred.
  • Strong analytical and quantitative skills, with the ability to interpret complex datasets and deliver actionable insights.
  • Excellent communication skills, capable of conveying technical concepts to non‑technical stakeholders.
  • Proficient in data management tools and platforms, with a continuous improvement mindset.
  • Prior experience in risk and regulatory reporting, with a strong understanding of associated frameworks and compliance requirements, is highly desirable.

Benefits and Rewards

BNY offers highly competitive compensation, benefits, and wellbeing programs rooted in a strong culture of excellence and our pay‑for‑performance philosophy. We provide access to flexible global resources and tools for your life’s journey. Focus on your health, foster your personal resilience, and reach your financial goals as a valued member of our team, along with generous paid leaves, including paid volunteer time, that can support you and your family through moments that matter.


BNY is an Equal Employment Opportunity/Affirmative Action Employer - Underrepresented racial and ethnic groups/Females/Individuals with Disabilities/Protected Veterans.


#J-18808-Ljbffr

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.