Senior Data Analyst

HSBC Global Services Limited
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Some careers shine brighter than others. 

 

If you’re looking for a career that will help you stand out, join HSBC and fulfil your potential. Whether you want a career that could take you to the top, or simply take you in an exciting new direction, HSBC offers opportunities, support and rewards that will take you further. 

 

Wholesale Data & Analytics is creating a world class “data-driven” organization that leads our competitors and inspires our employees. We are building a revolutionary data analytics ecosystem to generate business insights and provide great customer experience from well-managed and trusted data assets.

 

Our global team is partnering with IT to deliver an ecosystem of curated, enriched, and protected sets of data – created from global, raw, structured, and unstructured sources. Our Wholesale Big Data Lake is the largest aggregation of data ever within HSBC. We have over 300 sources and a rapidly growing book of work. We are utilising the latest technologies to solve business problems and deliver value and truly unique insights.

 

We are looking for Data Analysts that proactively drive a number of business outcomes through the use our extensive global Wholesale data estate, leveraging an array of innovative technologies and practices to deliver a best-in-class set of data solutions.

This will involve a number of disciplines; data asset development, business engagement, root cause analysis of complex commercial challenges, data modelling, requirements management, solution testing and technical deployments.

 

In this role you will:

 

  • Engage with stakeholders of various levels across business lines to understand business requirements, identify solutions and contribute to decision making
  • Lead on the definition and development of logical data assets to support Wholesale business use-cases, largely related to the lending journey
  • Generate insights and analytics to support business activities, financial projects, product planning, sales activities, performance measurement, regulatory requests and decision making
  • Work closely with the CDO communities across Wholesale, Risk, Finance and FCR to ensure the unified data model is fit for purpose for the business data demands
  • Form a crucial part of a dynamic, global delivery team including colleagues across multiple functions and geographies with the unified aim of creating innovative and strategic data solutions
  • Inform and implement measured data governance structures and practices to ensure that our products and deliveries are robust and compliant
  • Co-ordinate across multiple delivery teams, ensuring that all activities are timely and contribute to strategic business outcomes
  • Guide and develop colleagues to drive performance standards and best practices

 

To be successful in this role you should have:

 

  • Strong hands-on data and analytics background, with experience in coding languages such as SAS, Python, and SQL essentially
  • An ability to extract, analyse and manage large data sets within big data environments
  • Experience interpreting and creating artefacts such as Data Dictionary, Data Architecture and Data flow diagrams
  • An ability to work independently with limited oversight of day-to-day activities
  • A high degree of attention to detail and problem-solving abilities
  • Experience in planning and deploying both business and IT initiatives
  • Leadership of focused delivery teams across multiple functions adhering to Agile disciplines
  • Experience working within a frontline Wholesale business function (CMB/GBM) desirable but not essential
  • Experience working with platforms such as Hadoop or Google Cloud desirable but not essential

 

This is a hybrid role based in Birmingham or London.

 

Being open to different points of view is important for our business and the communities we serve. At HSBC, we’re dedicated to creating diverse and inclusive workplaces. Our recruitment processes are accessible to everyone - no matter their gender, ethnicity, disability, religion, sexual orientation, or age.

 

We take pride in being a Disability Confident Leader and will offer an interview to people with disabilities, long term conditions or neurodivergent candidates who meet the minimum criteria for the role.

If you’d like to apply for one of our roles and need adjustments made, please get in touch with our Recruitment Helpdesk:

 

Email:

Telephone: "

 

 

 

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.

Job-Hunting During Economic Uncertainty: Machine Learning Edition

Machine learning (ML) has firmly established itself as a crucial part of modern technology, powering everything from personalised recommendations and fraud detection to advanced robotics and predictive maintenance. Both start-ups and multinational corporations depend on machine learning engineers and data experts to gain a competitive edge via data-driven insights and automation. However, even this high-demand sector can experience a downturn when broader economic forces—such as global recessions, wavering investor confidence, or unforeseen financial events—lead to more selective hiring, stricter budgets, and lengthier recruitment cycles. For ML professionals, the result can be fewer available positions, more rivals applying for each role, or narrower project scopes. Nevertheless, the paradox is that organisations still require skilled ML practitioners to optimise operations, explore new revenue channels, and cope with fast-changing market conditions. This guide aims to help you adjust your job-hunting tactics to these challenges, so you can still secure a fulfilling position despite uncertain economic headwinds. We will cover: How market volatility influences machine learning recruitment and your subsequent steps. Effective strategies to distinguish yourself when the field becomes more discerning. Ways to showcase your technical and interpersonal skills with tangible business impact. Methods for maintaining morale and momentum throughout potentially protracted hiring processes. How www.machinelearningjobs.co.uk can direct you towards the right opportunities in machine learning. By sharpening your professional profile, aligning your abilities with in-demand areas, and engaging with a focused ML community, you can position yourself for success—even in challenging financial conditions.

How to Achieve Work-Life Balance in Machine Learning Jobs: Realistic Strategies and Mental Health Tips

Machine Learning (ML) has become a cornerstone of modern innovation, powering everything from personalised recommendation engines and chatbots to autonomous vehicles and advanced data analytics. With numerous industries integrating ML into their core operations, the demand for skilled professionals—such as ML engineers, research scientists, and data strategists—continues to surge. High salaries, cutting-edge projects, and rapid professional growth attract talent in droves, creating a vibrant yet intensely competitive sector. But the dynamism of this field can cut both ways. Along with fulfilling opportunities comes the pressure of tight deadlines, complex problem-solving, continuous learning curves, and high-stakes project deliverables. It’s a setting where many professionals ask themselves, “Is true work-life balance even possible?” When new algorithms emerge daily and stakeholder expectations soar, the line between healthy dedication and perpetual overwork can become alarmingly thin. This comprehensive guide aims to shed light on how to achieve a healthy work-life balance in Machine Learning roles. We’ll discuss the distinctive pressures ML professionals face, realistic approaches to managing workloads, strategies for safeguarding mental health, and how boundary-setting can be the difference between sustained career growth and burnout. Whether you’re just getting started or have been at the forefront of ML for years, these insights will empower you to excel without sacrificing your well-being.

Transitioning from Academia to the Machine Learning Industry: How PhDs and Researchers Can Thrive in Commercial ML Settings

Machine learning (ML) has rapidly evolved from an academic discipline into a cornerstone of commercial innovation. From personalising online content to accelerating drug discovery, machine learning technologies permeate nearly every sector, creating exciting career avenues for talented researchers. If you’re a PhD or academic scientist thinking about leaping into this dynamic field, you’re not alone. Companies are eager to recruit professionals with a strong foundation in algorithms, statistical methods, and domain-specific knowledge to build the intelligent products of tomorrow. This article explores the essential steps academics can take to transition into industry roles in machine learning. We’ll discuss the differences between academic and commercial research, the skill sets most in demand, and how to optimise your CV and interview strategy. You’ll also find tips on networking, developing a commercial mindset, and navigating common challenges as you pivot your career from the halls of academia to the ML-driven tech sector.