Data Analyst

Fintellect Recruitment
London
1 year 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 Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.