Senior Data Scientist

CloudDevs
London
1 week ago
Create job alert

Let’s fix hospitality, for good.


Hospitality is tough – margins are thin, waste is high, and teams are stretched. But it doesn’t have to be this hard. That’s why we built Nory.


Our CEO, Conor, knows the pain first-hand. After founding and scaling Mad Egg in Ireland, he got fed up with juggling “market-leading” systems, clunky spreadsheets, and endless printouts. So he set out to build the tool he wished he’d had from day one.


Nory is an all-knowing restaurant management system. It blends real-time data with AI predictive analytics, giving operators control of their margins. From food prep to forecasting, it’s operational intelligence that helps restaurants run with consistency, certainty, and profit. The result? Thriving restaurants, better jobs, less waste, healthier margins.


And we’re just getting started. Fresh off a Series B led by Kinnevik, we’ve grown to 70+ people across Ireland, the UK, and Spain – and demand is scaling faster than we ever imagined.


We’re looking for a Senior Data Scientist to work with Applied Machine Learning and help us build models that actually work — not just in a notebook, but in the messy, real world of live operations. This isn’t a research or pure experimentation role. It’s about building practical ML systems that drive results — and making sure they keep working long after they’re deployed.


What you’ll be doing:

This role is for someone who thrives on ownership, moves fast with rigour, and cares about meaningful business impact. You’ll design, build, and maintain machine learning systems that optimise how our customers operate day‑to‑day — from forecasting demand to improving labour planning, reducing waste, and more.



  • Own ML end-to-end: Take problems from initial framing all the way through to production deployment, monitoring, and iteration. You’ll lead with autonomy and pace.
  • Apply statistical and ML rigour: Choose the right tool for the job, and explain why. Your approach is grounded in fundamentals, not just patterns.
  • Keep systems alive: Build with monitoring and retraining in mind. Your models will keep learning and delivering value in production.
  • Work hand‑in‑hand with product teams: Collaborate with engineers, PMs, and designers to embed ML into our product in ways that drive commercial outcomes.
  • Focus on business impact: Success isn’t just a great validation score — it’s improved margins, better efficiency, and clearer decisions for our customers.
  • Contribute to our culture: Help us raise the bar by sharing learnings, giving feedback, and shaping how we grow the ML craft at Nory.

What you’ll bring:

  • Proven end-to-end ownership: You’ve shipped ML systems in production, more than once. You know what it takes to get from idea to impact — fast, and without a big team around you.
  • Strong classical ML foundation: You’re comfortable with forecasting, regression, classification, drift detection, causal reasoning, and feature engineering — and can back up your decisions with clarity.
  • Statistical & experimental thinking: You apply rigour in how you design solutions and test what’s working — always with an eye on practical outcomes.
  • Hands‑on technical skills: Strong Python, confident with ML libraries like scikit‑learn, pandas, and LightGBM. You’ve worked with cloud infrastructure (e.g. GCP) and modern data tools (e.g. dbt, Snowflake).
  • Cross‑collaboration: You thrive in highly collaborative environments, communicate clearly, and offer help where needed to improve team outcomes.
  • Clear communicator: You explain complex ideas simply, listen well, and bring others along with you.
  • Startup‑ready mindset: You’re proactive, resourceful, and thrive in ambiguity. You bias towards action and know when to optimise for speed vs. polish.

Nice‑to‑have:

  • Experience working in lean data teams with high ownership cultures
  • Background in B2B SaaS or operational domains (e.g. logistics, supply chain, workforce planning)
  • Exposure to LLMs or modern NLP approaches (not a core part of this role, but useful context)

What you’ll get in return:

  • Competitive salary range
  • Meaningful equity, at N is an owner!
  • 35 days of paid leave per year (including bank holidays)
  • Comprehensive private health insurance via Irish Life (Ireland) and Axa (UK)
  • Enhanced parental leave and baby loss support
  • Learning & development culture – €1000 personal annual budget + quarterly book budget
  • €250 home office workspace budget
  • Regular team offsites & socials
  • Offices in either London (LABS House, 15-19 Bloomsbury Way) or Dublin (Dogpatch Labs, The Chq Building, Custom House Quay, North Wall)
  • And much more

How we work

Our vision is to build a better future for the restaurant industry.


One where operators are in control, margins are stronger, and frontline teams can build careers they’re proud of. To get there, we move fast, stay focused, and hold ourselves to a high bar. Our values guide how we work, grow, and win – together.


These are the values we live by:


We serve up impact with a side of profit: We prioritise work that delivers real financial results for our restaurant partners.


We prioritise speed of service: We move fast, unblock quickly, and deliver with urgency.


We act like owners: We own problems, raise the bar, and build better every day.


We win as a crew: We grow stronger through feedback, collaboration, and shared wins.


We hire humans.

At Nory, we believe that diverse teams build better products. We welcome applicants from all backgrounds, identities, and walks of life. We do not discriminate based on gender, ethnicity, sexual orientation, religion, family status, age, disability, or race. What matters to us is how you think, how you work, and what you bring to the table. Please let us know if you require any adjustments so you can bring your best self to the interview process.


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.