Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Analyst

Fintellect Recruitment
London
10 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Job Title:Data Analyst – Commercial Strategy

Location:London, UK

Company:A fast-growing Consumer Credit Card Fintech


Our client is a rapidly expanding consumer fintech company specialising in innovative credit card solutions designed to empower individuals and drive financial inclusion. As a disruptor in the financial services industry, we leverage cutting-edge technology to deliver seamless, customer-centric products. With strong investment backing and a passion for growth, we are on an exciting journey to transform how people manage their finances.


We are looking for a talentedData Analystto join our dynamicCommercial Strategyteam. This is an exciting opportunity to play a key role in driving data-informed decision-making that will shape the future of our business.


Role Overview


As aData Analystwithin theCommercial Strategyteam, you will use your strong analytical skills to help inform the company’s strategic direction, optimise marketing, sales, and product performance, and unlock valuable insights to support key business initiatives. You will work closely with stakeholders across the commercial, product, credit risk and finance teams to analyse customer trends, performance data, and competitive benchmarks, ensuring that strategic decisions are data-driven and grounded in actionable insights.


Key Responsibilities of the Data Analyst


Data Analysis & Reporting:

  • Conduct in-depth analysis of commercial performance, customer behaviour, and market trends to support decision-making. Develop and maintain dashboards and reports that provide actionable insights on business performance, customer acquisition, retention, and product usage.

Customer Segmentation & Insights:

  • Analyse customer data to identify trends and segments that can drive targeted marketing, product optimisation, and sales strategies. Work closely with the marketing and product teams to develop customer segmentation models and personas.

Strategic Recommendations:

  • Present data-driven insights and actionable recommendations to senior leadership and cross-functional teams to inform strategic decisions on product offerings, pricing, marketing campaigns, and partnership opportunities.

Data Modeling & Forecasting:

  • Build and maintain financial and commercial models to forecast key metrics such as customer acquisition costs, lifetime value, retention rates, and product profitability.

Stakeholder Collaboration:

  • Work alongside marketing, product, finance, and operations teams to ensure alignment on key commercial objectives, tracking progress towards KPIs, and adapting strategies as needed.
  • Ad-hoc Analysis:
  • Provide ad-hoc data analysis and support for special projects, including competitor benchmarking, pricing analysis, and market research and assisting the credit risk teams.


Key Skills & Experience of the Data Analyst:


Analytical Expertise:

  • Proven experience as a Data Analyst, ideally in a fintech, consumer tech, or e-commerce environment. Strong proficiency in data analysis tools (e.g., SQL, Excel, Python, R) and business intelligence tools (e.g., Tableau, Power BI).

Commercial Acumen:

  • Strong understanding of commercial strategy, KPIs, and business performance metrics. Experience working with cross-functional teams to drive business outcomes.

Communication Skills:

  • Ability to present complex data and insights in a clear, concise, and actionable manner to both technical and non-technical stakeholders.

Problem-Solving:

  • Strong critical thinking and problem-solving skills with the ability to translate data into strategic insights and recommendations.

Experience in Consumer Credit or Financial Services:

  • Prior experience in the consumer credit card or broader fintech industry is a plus, though not required.

Educational Background:

  • A degree in a quantitative field such as Mathematics, Statistics, Economics, or Data Science, or equivalent practical experience.

Attention to Detail:

  • Strong attention to detail and ability to manage large datasets with accuracy and precision.


Why Join Us?


Rapid Growth:Join a fast-growing fintech company that’s reshaping the financial services landscape.

Innovative Culture:Be part of an innovative, forward-thinking team that’s passionate about using data to drive change.

Career Development:Opportunities for growth and advancement as the company expands.

Competitive Compensation:Competitive salary and benefits package, including health insurance, pension contributions, and flexible working options.


If you’re a data-driven individual with a passion for commercial strategy and are excited to work in a fast-paced, high-growth environment, we would love to hear from you!

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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.