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For Companies At a Glance
- Tasks: Design and deploy high-frequency demand forecasting models for pricing decisions.
- Company: Join a Series D tech scale-up revolutionising travel and hospitality with advanced machine learning.
- Benefits: Enjoy remote work, private healthcare, mental health support, nursery benefits, and generous PTO.
- Why this job: Be part of a world-class data science team driving global commercial strategies in a high-growth AI company.
- Qualifications: Strong background in statistics, machine learning, and proficiency in production-level Python code required.
- Other info: Opportunity to contribute to innovative ML libraries and frameworks in a modern MLOps setup.
The predicted salary is between 42000 - 84000 £ per year.
Job Description
Location: Remote-first (UK-based)Salary: £70,000
The Company
A Series D tech scale-up is reshaping the travel and hospitality industries through advanced machine learning and forecasting. Purpose-built for this space, their platform leverages deep learning to drive revenue optimisation, automation, and real-time analytics. With a growing client base of major global travel brands, they're hiring exceptional talent to push the boundaries of what's possible in commercial strategy.
The Role
As a Senior Data Scientist within the Forecasting Algorithms team, you'll design and deploy high-frequency demand forecasting models that directly support pricing decisions. You'll own the full development lifecycle-from modelling to deployment-while collaborating closely with science, product, and engineering stakeholders.
This is a hands-on role suited to someone who thrives on building, testing, and deploying complex statistical models in a production environment. You'll work in a modern MLOps setup, contributing to libraries and frameworks used across the company's data science organisation.
Key Responsibilities:
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Design and deploy time series forecasting models with a strong focus on accuracy and scalability
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Improve existing model performance and drive innovation through new methodologies
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Develop internal ML and forecasting libraries using Python
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Translate product goals into technical solutions in collaboration with leadership
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Implement validation frameworks, unit tests, and monitoring pipelines
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Stay current with advances in machine learning and apply them where appropriate
Tech Stack:
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Python (Pandas, Polars, SciPy, Scikit-learn, NumPy)
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Dagster, Argo, GCP
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Kibana, Elastic
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DevOps-first approach for model deployment
Skills & Experience:
Essential:
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Strong background in statistics, machine learning, and time series forecasting
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Experience with modern ML models (e.g. transformers, CNNs, attention mechanisms)
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Proficiency in writing production-level Python code
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Familiarity with CI/CD practices, testing, and model validation
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Confident working across teams and taking ownership of delivery
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Strong communication and problem-solving skills
Desirable:
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Experience in demand forecasting or operations research
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Familiarity with Bayesian methods, Monte Carlo simulations, or reinforcement learning
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Exposure to high-scale SaaS or B2C products
Why Apply?
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Build forecasting systems powering global commercial strategies
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Collaborate with a world-class data science team
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Up to £70,000 + equity in a high-growth AI company
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Excellent benefits: private healthcare, mental health support, nursery benefit (UK), and generous PTO
How to Apply
To apply, please send your CV to Daniel Abbasi via the Apply link on this page.
Contact Detail:
Harnham - Data & Analytics Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist
✨Tip Number 1
Familiarise yourself with the specific tech stack mentioned in the job description. Brush up on your Python skills, especially with libraries like Pandas and Scikit-learn, as well as tools like GCP and Dagster. Being able to discuss these technologies confidently during an interview will show that you're a great fit for the role.
✨Tip Number 2
Prepare to showcase your experience with time series forecasting models. Think of specific projects where you've designed or deployed such models, and be ready to explain your approach and the impact it had. This will demonstrate your hands-on experience and problem-solving skills.
✨Tip Number 3
Network with current or former employees of the company, if possible. They can provide insights into the company culture and the expectations for the Senior Data Scientist role. This insider knowledge can help you tailor your discussions during the interview.
✨Tip Number 4
Stay updated on the latest advancements in machine learning, particularly in areas relevant to demand forecasting. Being able to discuss recent trends or breakthroughs in the field will not only impress your interviewers but also show your passion for continuous learning.
We think you need these skills to ace Senior Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in statistics, machine learning, and time series forecasting. Emphasise any projects where you've designed or deployed forecasting models, as this aligns closely with the role.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data science and how your skills can contribute to the company's mission. Mention specific technologies from the job description, like Python and MLOps, to show you’re a great fit.
Showcase Your Technical Skills: If you have experience with modern ML models or CI/CD practices, make sure to include examples of your work. This could be through links to projects on GitHub or detailed descriptions of your contributions in previous roles.
Highlight Collaboration Experience: Since the role involves working closely with various teams, mention any past experiences where you collaborated with product or engineering teams. Highlight your communication skills and problem-solving abilities to demonstrate your teamwork capabilities.
How to prepare for a job interview at Harnham - Data & Analytics Recruitment
✨Showcase Your Technical Skills
As a Senior Data Scientist, you'll need to demonstrate your proficiency in Python and machine learning. Be prepared to discuss specific projects where you've designed and deployed forecasting models, and highlight your experience with libraries like Pandas and Scikit-learn.
✨Understand the Company's Tech Stack
Familiarise yourself with the tools and technologies mentioned in the job description, such as GCP and CI/CD practices. Showing that you have hands-on experience with these technologies will set you apart from other candidates.
✨Prepare for Problem-Solving Questions
Expect to face questions that assess your problem-solving abilities. Think of examples where you've tackled complex statistical challenges or improved model performance, and be ready to explain your thought process clearly.
✨Communicate Effectively
Strong communication skills are essential for this role. Practice explaining technical concepts in simple terms, as you'll need to collaborate with non-technical stakeholders. Being able to convey your ideas clearly can make a significant difference in the interview.
