Senior Analyst Product Control - Client modelling

FNZ
London
1 month ago
Applications closed

Related Jobs

View all jobs

Supply Chain Data Analyst CGT

Head of Underwriting Quality

Senior Data Scientist

Contract Product Data Analyst

Senior Analyst & Data Specialist

Senior Data Analyst

Location:Edinburgh or London

Working Pattern:Hybrid

Contract:Permanent

Role Overview:

We are seeking aSenior Analystto join our Product Control team, taking a pivotal role in overseeing modeling risk across FNZ Group. This position offers a unique opportunity to collaborate with cross-functional teams, enhance financial control frameworks, and provide actionable insights that drive strategic business decisions.

Key Responsibilities:

  • Senior analyst in a fully functioning Product Control team with responsibility for providing oversight of modelling risk across FNZ Group.
  • Partner with risk managers, and financial controllers to implement and maintain robust control frameworks.
  • Partner with modelling teams to enhance capability, accuracy and usability of models.
  • Monitor and reconcile financial product valuations, identifying and resolving discrepancies.
  • Validate pricing models for financial instruments and ensure alignment with accounting and regulatory standards.
  • Evaluate the financial performance of products and provide actionable insights to drive business strategy.
  • Oversee financial risk models, primarily business plan and customer profitability models as well as credit, market, operational, and liquidity risk models.
  • Conduct scenario analysis, stress testing, and sensitivity analysis to assess and predict financial risks including input into regulatory processes/reporting.
  • Provide regular reports and insights to senior leadership, highlighting emerging risks and their potential impact.
  • Model Risk Management (including statistical, advanced AI/ML based techniques) - Formulation of guidelines/policy, Laying down of Roadmap, Establishment of model risk governance including frameworks, validation, inventory management, attestations and optimization.
  • Program/Delivery Management – Create project plans and coordinate with key stakeholders.
  • Collaborate with stakeholders to integrate advanced analytics and machine learning into risk modeling processes.

Desired Qualifications and Skills:

Education:

  • Bachelor’s or master’s degree in finance, Economics, Mathematics, Engineering, or a related field.
  • Qualified accountant (and >2 years post qualified).

Experience:

  • 2+ years of experience in financial risk modeling, product control, or a similar role in banking, financial services, or fintech.
  • Strong understanding of financial instruments, derivatives, and risk management principles.

Technical Skills:

  • Advanced knowledge of Excel and experience with financial systems (e.g., Bloomberg, Reuters, or similar).
  • Familiarity with accounting principles (e.g., IFRS, GAAP) and regulatory requirements (e.g., Basel III, IFRS 9).
  • Statistical Techniques:Linear & Logistic Regression, Hypothesis Testing, Exploratory Data Analysis, Survival Analysis, Cluster Analysis, various Statistical Tests and Cross-Validation Techniques, ML Algorithms.
  • Proficiency in programming languages like Python, R, or MATLAB for quantitative modeling.

Soft Skills:

  • Strong analytical and problem-solving skills with attention to detail.
  • Ability to work collaboratively in a fast-paced and dynamic environment.

This is an exciting opportunity for an experienced and driven professional to play a key role in shaping the future of financial risk management and product control at FNZ. If you thrive in a dynamic environment, excel at solving complex challenges, and have a passion for driving strategic business outcomes, we encourage you to apply.

Application Deadline:31/01/2025

About FNZ:

FNZ is committed to opening up wealth so that everyone, everywhere can invest in their future on their terms. We know the foundation to do that already exists in the wealth management industry, but complexity holds firms back.

We created wealth’s growth platform to help. We provide a global, end-to-end wealth management platform that integrates modern technology with business and investment operations. All in a regulated financial institution.

We partner with over 650 financial institutions and 12,000 wealth managers, with US$1.5 trillion in assets under administration (AUA).

Together with our customers, we help over 20 million people from all wealth segments to invest in their future.

#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.

Jobs

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.

Jobs

Machine Learning Jobs in the Public Sector: Opportunities Across GDS, NHS, MOD, and More

Machine learning (ML) has rapidly moved from academic research labs to the heart of industrial and governmental operations. Its ability to uncover patterns, predict outcomes, and automate complex tasks has revolutionised industries ranging from finance to retail. Now, the public sector—encompassing government departments, healthcare systems, and defence agencies—has become an increasingly fertile ground for machine learning jobs. Why? Because government bodies oversee vast datasets, manage critical services for millions of citizens, and must operate efficiently under tight resource constraints. From using ML algorithms to improve patient outcomes in the NHS, to enhancing cybersecurity within the Ministry of Defence (MOD), there’s a growing demand for skilled ML professionals in UK public sector roles. If you’re passionate about harnessing data-driven insights to solve large-scale problems and contribute to societal well-being, machine learning jobs in the public sector offer an unparalleled blend of challenge and impact. In this article, we’ll explore the key reasons behind the public sector’s investment in ML, highlight the leading organisations, outline common job roles, and provide practical guidance on securing a machine learning position that helps shape the future of government services.

Jobs

Contract vs Permanent Machine Learning Jobs: Which Pays Better in 2025?

Machine learning (ML) has swiftly become one of the most transformative forces in the UK technology landscape. From conversational AI and autonomous vehicles to fraud detection and personalised recommendations, ML algorithms are reshaping how organisations operate and how consumers experience products and services. In response, job opportunities in machine learning—including roles in data science, MLOps, natural language processing (NLP), computer vision, and more—have risen dramatically. Yet, as the demand for ML expertise booms, professionals face a pivotal choice about how they want to work. Some choose day‑rate contracting, leveraging short-term projects for potentially higher immediate pay. Others embrace fixed-term contract (FTC) roles for mid-range stability, or permanent positions for comprehensive benefits and a well-defined career path. In this article, we will explore these different employment models, highlighting the pros and cons of each, offering sample take‑home pay scenarios, and providing insights into which path might pay better in 2025. Whether you’re a new graduate with a machine learning degree or an experienced practitioner pivoting into an ML-heavy role, understanding these options is key to making informed career decisions.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.