Senior Data Scientist - Net Revenue & Revenue Growth Management

Lorien
London
6 days ago
Create job alert

Senior Data Scientist - Net Revenue Management / Revenue Growth Management

Contract: 7‑Month Contract
IR35: Inside IR35
Location: Remote / Hybrid (UK)
Start: ASAP

We are looking for an experienced Senior Data Scientist with expertise in Net Revenue Management / Revenue Growth Management, ideally in the CPG or Retail sectors. The Data Scientist is required to lead advanced analytics initiatives across Commercial, Marketing, and Digital workstreams. This role offers the opportunity to guide a high-performing team, shape data science strategy, and deliver impactful, production-ready machine learning solutions. We are looking for expertise and prior experience with Net Revenue Management / Revenue Growth Management and advanced experience of the below key areas:

Causal inference Intervention analysis Counterfactual estimation Mathematical optimisation Scenario simulation

Key Responsibilities for the Senior Data Scientist - Net Revenue Management / Revenue Growth Management.

Data Science & Modelling

Develop, evolve, and enhance data science models across multiple business areas. Lead a team of data scientists across several workstreams using rule‑based methods, supervised and unsupervised learning, forecasting, and optimisation techniques. Work closely with product owners, data engineers, and ML engineers to deliver high-quality, scalable ML solutions. Prepare, clean, and engineer datasets for training and validating machine learning models. Define and implement performance metrics, including both ML performance and computing resource usage (CPU/memory). Support end‑to‑end deployment of machine learning models, including monitoring, retraining pipelines, and lifecycle management. Leverage third‑party and syndicated data to generate insights relating to trade promotions, pricing, distribution, and digital shelving-particularly in joint business planning environments. Design and execute statistical models and multi‑variate tests to measure the impact of business decisions and market variations.

Teamwork & Leadership

Foster a highly collaborative team culture built on openness, ownership, and accountability. Partner with key stakeholders and influence decision-making across the business. Contribute to building foundational AI assets across forecasting, optimisation, segmentation, attribution modelling, and experimentation. Conduct code reviews and ensure exceptionally high standards in code quality. Mentor and support junior data scientists and apprentices. Promote responsible AI practices and strong governance.

Qualifications & Skills for the Senior Data Scientist - Net Revenue Management / Revenue Growth Management

MS or PhD in Data Science, Computer Science, Applied Mathematics, Statistics, or a similar quantitative discipline is highly desirable. Industry experience ideally in CPG / Retail in developing machine learning models and building end‑to‑end ML pipelines. Experience of machine learning algorithms, statistical modelling, and probabilistic programming. Expert programming ability in Python, R, and SQL, with deep experience in time series modelling. Understanding of forecasting methods: ARIMA, ETS, Prophet, time series pattern detection, and ML‑based forecasting. Advanced experience in: Causal inference Intervention analysis Counterfactual estimation Mathematical optimisation Scenario simulation Solid grounding in Bayesian methods and probabilistic programming. Hands-on experience with scikit-learn, PyMC, TensorFlow, PyTorch, Databricks ML, Keras, and ML at scale. Experienced Data Scientist, including leadership experience and demonstrable impact-ideally within a Net Revenue Management / Revenue Growth Management environment. Critical thinking, exceptional attention to detail, and the ability to collaborate effectively in cross-functional teams.

Guidant, Carbon60, Lorien & SRG - The Impellam Group Portfolio are acting as an Employment Business in relation to this vacancy.

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist and Machine Learning Researcher

Senior Data Scientist

Senior Data Scientist

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.