Marketing and Sales Analyst

Love Finance

Birmingham

1 year ago

Applications closed

Related Jobs

View all jobs

Data Analyst Apprentice

Staff Data Analyst Engineering · Norwich · Fully Remote

Hybrid Junior Data Analyst: Clean Data to Drive Growth

Junior Data Analyst

Data Analyst

Data Analysts ITSM and Azure Data Platform

Love Finance: Senior Marketing & Sales Analyst– Permanent – Hybrid – Birmingham City Centre - £50,000 - £70,000 per annum

Love Finance is a leading online finance broker and lender dedicated to empowering UK businesses. Since 2016, Love Finance have been instrumental in assisting thousands of companies in accessing funding via cutting-edge, technology. Ranked in theTop 15 fastest growing finance companies, withGreat Places to Work certificationand a4.9-star rating on Trust Pilot, we consistently surpass our targets and are looking to secure a Senior Data and Sales Analyst to continue our growth trajectory.

About You as Senior Data and Sales Analyst:

We are looking for a self-starting Senior Data Analyst to join the Love Finance team, acting as the key data contact for all senior leaders across the business. For this role you will need to work self sufficiently and in a predominantly stand-alone capacity, taking control of data projects, prioritisation of data projects and transfer of useful data knowledge to the Love Finance team.

Role: Senior Marketing & Sales Analyst

Type: Permanent

Working Pattern: Hybrid

Location: Birmingham City Centre

Salary: £50,000 to £70,000 per annum

The Role of the Marketing & Sales Analyst:

Essential Systems Experience:Must have knowledge of BI Platforms/BI Tools, Thoughtspot or Tableu

Essential Experience:Candidates must have at least 5 years’ experience within a similar role and must have previously worked on analysis for Sales and Marketing departments predominantly

Act as the go to point of contact for all data projects across Love Finance, partnering with Sales, Marketing, Finance, Recruitment, HR and Operations on data insights
Collaborate with the Head of Marketing, Sales and Operations Director and Data Engineer to ensure accurate and timely tracking of data and leads through to Sales Agents
Track the data and format in easy-to-understand manner for wider business use and efficiency
Analyse data to understand market opportunities and business growth overviews
Be the Thoughtspot expert user ensuring completion of dashboards for easy data access
Utilise innovative ways to widen the scope of data capture
Use data collected to assess, marketing campaign performance, marketing engagement, sales trends, KPI data improvement etc.
Researching and evaluating current economic conditions that may affect the organisation’s ability to sell its products or services in the marketplace
Preparing sales forecasts and collecting and analysing data to evaluate current sales goals
Compiling, analysing, and reporting sales data
Monitoring and analysing competitive activity, customer, and market trends
Providing actionable insights to guide the sales and marketing teams
Research sales trends and evaluate sales performance for forecasting and business planning purposes
Collects, cleanse, and analyse data to answer questions and inform decision-making across the business
Utilise tools such as statistical analysis, data visualization, and machine learning
Ensures data quality and accuracy
Identifies trends, patterns, and insights to help the organisation make informed decisions
Train and support the Junior Data Analyst

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Feb 3, 2026

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Jan 29, 2026

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Jan 24, 2026

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.