Analytics Manager

C5i

Leeds

10 months ago

Applications closed

Related Jobs

View all jobs

Data Analyst in Luton)

Data Analyst - 6 Month FTC in Luton)

Data Science Manager - Advanced Analytics & AI

Data Science Manager: Lead Analytics & Innovation

Data Analyst

Data Engineer, Payments Analytics & Growth

About C5i

C5i is a pure-play AI & Analytics provider that combines the power of human perspective with AI technology to deliver trustworthy intelligence. The company drives value through a comprehensive solution set, integrating multifunctional teams that have technical and business domain expertise with a robust suite of products, solutions, and accelerators tailored for various horizontal and industry-specific use cases. At the core, C5i’s focus is to deliver business impact at speed and scale by driving adoption of AI-assisted decision-making.

C5i caters to some of the world’s largest enterprises, including many Fortune 500 companies. The company’s clients span Technology, Media, and Telecom (TMT), Pharma & Lifesciences, CPG, Retail, Banking, and other sectors. C5i has been recognized by leading industry analysts like Gartner and Forrester for its Analytics and AI capabilities and proprietary AI-based platforms.

Global offices

United States | Canada | United Kingdom | United Arab of Emirates | India

Job Summary

We are seeking a highly skilled Analytics Manager with 8–10 years of experience in analytics, specifically in Revenue Management and Pricing analytics. The ideal candidate will have excellent leadership skills, be highly organized, and possess exceptional problem-solving abilities.

In this role as a Analytics Manager, you will be bridging the gap between business and analytics teams. You will collaborate with Business Product Owners, Analytics Teams, and Cross-Functional Partners to define key business problems, success criteria, drive product lifecycle and transform data-driven insights into strategic actions. You will be responsible for ensuring that analytical solutions align with business needs in Pricing Strategy, Trade Promotions, Revenue Optimization, and Market Insights.

Job Responsibilities

Business Understanding & Translation:Act as a liaison between business teams and data analytics teams to ensure alignment on Revenue Management objectives aligned with business vision, priorities and goals.Partner with business product owner to deliver Revenue Management D&A capabilities to drive & embed top quartile Promotion & Trade terms
Project Management: Lead NRM, SRM, and Pricing-related projects,defining scope, objectives, and deliverables in collaboration with stakeholders. Spearhead the D&A delivery of a comprehensive product strategy for the domain
Pricing & Revenue Analytics: Identify pricing, promotion, and assortment opportunities to optimize revenue streams. Drive insights on Price Elasticity, Promotional Effectiveness, Mix Optimization, and Trade Investment and recommend optimization strategies for ROI improvement
Stakeholder Collaboration: Work closely with Sales, Marketing, Finance, and Data Science Teamsto align on revenue growth strategies. Collaborate with cross-functional teams to design and implement analytics solutions that scale and are aligned with product objectives set out in the roadmap
Data Interpretation: Translate complex analytical outputs (e.g., Market Mix Models (MMM), Price Elasticity Models, and ROI of Promotions) into business-friendly insights
Performance Monitoring: Track KPIs related topricing strategies, promotional impact, and revenue growthto ensure continuous improvement
Presentation & Reporting: Create clear, impactful reports and presentations for senior leadership to influence pricing and promotion decisions
Training & Enablement: Educate stakeholders on how to interpret and leverage analytics to drive revenue growth

Minimum Requirements

Bachelor’s/Master’s degree in Business Administration, Economics, Finance, Data Science, or a related field
8+ years of experience in a Business Translator/Consultant, Pricing, NRM, or SRM role within the CPG or Retail industry
CPG and Retailer knowledge, including knowledge of, and experience using key external third-party data sources including, first-party data, and Associates surveys
Proven Track Record of Success in Revenue Growth, Trade Spend Optimization, and Customer Planning, Pricing, Promotion Optimization, Price Pack, etc
Proven track record to get things done in a matrixed organization – including the ability to influence without authority
Experience in managing relationships with external data vendors and analytics service providers
Strong analytical skills with expertise in Data Interpretation, Visualization, and Reporting
Excellent communication and storytelling skills – ability to convert analytical findings into clear business recommendations
Experience with analytical tools such as Excel, Power BI, Tableau, or SQL is a plus
Leadership qualities to guide teams, foster collaboration, and drive business impact.

C5iis proud to be an equal opportunity employer. We are committed to equal employment opportunity regardless of race, color, religion, sex, sexual orientation, age, marital status, disability, gender identity, etc. If you have a disability or special need that requires accommodation, please keep us informed about the same at the hiring stages for us to factor necessary accommodations.

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.