Senior/Lead Data Scientist

Mention Me
London
1 month ago
Create job alert

About Mention Me

Mention Me amplifies the authentic human voice in a world of AI marketing noise to drive profitable brand growth. We help brands identify true promoters, activate authentic recommendations and UGC, and align teams around a single source of Voice of Customer insights so real brand love compounds into performance across channels.

The opportunity

Become one of the top contributors to bring the new product vision live. Work end to end across metric design, data and modeling, experimentation, and product integration. 

What you’ll do

• Build components of new products that turn real customer signals into timely actions and measurable outcomes allowing customers to amplify consumer voice for LLM visibility advantage. Partner across Product, Engineering, CS, and Commercial to make the human signal visible, actionable, and compounding in the product.

• Establish experimentation and measurement foundations: design how we test, learn, and prove impact; embed sound statistical practice; and turn results into simple, trusted narratives that guide product and commercial decisions.

• Productionize and scale thoughtfully: ship durable data and model workflows in collaboration with the engineering team, ensure quality and monitoring, and document decisions so the system is reliable, explainable, and easy to evolve.

What you’ll bring

• Track record, typically 4+ years, in applied data science for product or marketing in consumer or SaaS

• Continuous learning mindset: you stay current with generative AI advances, prototype with new models and frameworks, evaluate them critically, and translate useful innovations into practical product improvements. You share learnings and raise the bar for the team.

• Hands-on ML skills: feature engineering, propensity or uplift modeling, model evaluation, monitoring

• Strong Python and SQL with the ability to move from notebooks to production code

• Practical data engineering instincts: event schemas, batch jobs, orchestration, data quality guardrails

• Clear communication that translates complexity into actionable narratives for non-technical audiences

• Bias for action and ownership in ambiguous, fast-moving environments


Nice to have

• LLM applications for agentic solutions 

• Graph modeling 

• Personalization: propensity/uplift modeling, bandits, causal inference
Libraries: scikit-learn, XGBoost, LightGBM, CatBoost, CausalML, Keras, DoWhy

• Experience with dbt, Airflow, Looker or Metabase, AWS services such as S3 and Lambda

• Experience in designing and implementing model/metric endpoints as part of a platform ecosystem with clear contracts and SLOs (, FastAPI/Flask, OpenAPI), deploying via AWS Lambda/API Gateway or containers

How we work

• Hybrid with in-person collaboration at our Vauxhall HQ and flexibility for focused work

• Cross-functional by default with close partnership across Product, Engineering, CS, and Commercial

• Outcome-driven with small releases, fast validation, and scaling what works

Benefits

Here are some of our favourite perks and benefits, but we have so many more!

Hybrid working

 Private medical insurance with Vitality, including enhanced mental wellbeing support, dental and vision policies and a range of lifestyle benefits and great incentives

 Life insurance

 Two Celebration Days; additional time off for you to celebrate religious days, cultural events, birthdays, anniversaries, or any other significant day that’s important to you

 Enhanced parental leave

 25 days annual leave (plus public holidays), increasing over your time as a Mentioneer

 Regular social events, from chocolate-tasting and pottery-making to poker nights and picnics

 Up-to-date tech you’ll need (we love Macs)

Related Jobs

View all jobs

Lead/Senior Data Scientist - Ad Tech Locational Data

Lead/Senior Data Scientist - Ad Tech Locational Data

Data Science Lead / Manager

Lead Data Scientist - eCommerce

Lead Data Scientist - Marketing Science

Lead 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.