At a Glance
- Tasks: Lead the development of ML systems to predict app conversions and optimise ad spend.
- Company: Exciting London startup with a focus on performance marketing and machine learning.
- Benefits: Competitive salary, equity options, and the chance to shape the future of our tech.
- Other info: Work closely with founders and enjoy technical autonomy in a dynamic environment.
- Why this job: Be the first data scientist and make a real impact on business outcomes.
- Qualifications: Experience in machine learning and a passion for solving complex problems.
The predicted salary is between 43200 - 72000 £ per year.
Overview
The Role in 30 Seconds
First data scientist at a funded London startup (two founders with proven track record). Build ML systems that predict future mobile app conversions weeks in advance. Own the entire ML stack with direct business impact.
What We Do
Day30 helps subscription apps improve paid acquisition ROI by providing predictive signals to optimise ad spend. We connect directly to mobile measurement partners (MMPs) to analyse behavioural event data, build ML models that predict high-value conversions weeks in advance, and deliver these predictions to advertising platforms without compromising user privacy. We are a two-founder London startup combining deep expertise in performance marketing and machine learning.
As our first data scientist, you'll be founder-adjacent, working directly with our CEO and CTO to transform our current ML capabilities into a scalable, automated platform that will power hundreds of clients. This role offers rare technical autonomy: you'll work across the entire ML pipeline from data ingestion through production deployment, collaborate with the CTO and software engineers, and have direct input on all technical decisions. We're looking for someone who thrives on solving complex behavioural modelling problems and wants to see their work immediately impact real business outcomes.
What You'll Do
- Core ML Pipeline Development
- Design and implement end-to-end ML pipelines from data ingestion through model deployment and signal delivery
- Transform client-specific Jupyter notebooks into modular, config-driven pipelines using orchestration tools such as Prefect/Airflow
- Build robust API connectors handling schema evolution, incremental updates, and data quality validation
- Implement comprehensive machine learning model evaluation frameworks blending technical metrics (precision, recall, PRAUC, probability calibration) with business outcomes
- AutoML
Lead Data Scientist employer: Day30
At Day30, we pride ourselves on being an innovative and dynamic startup located in the heart of London, offering our first Lead Data Scientist the unique opportunity to shape the future of our machine learning capabilities. With a strong focus on employee growth, you will work closely with our experienced founders, enjoy technical autonomy, and directly influence impactful business outcomes while contributing to a collaborative and forward-thinking work culture. Join us to be part of a team that values creativity, problem-solving, and the drive to make a difference in the subscription app market.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Scientist
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. The more people you know, the better your chances of landing that Lead Data Scientist role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects and any relevant work you've done. This will give you an edge when chatting with founders or hiring managers about how you can impact their business.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your approach to solving complex problems. We want to see how you think and how you can contribute to our ML capabilities.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our startup journey.
We think you need these skills to ace Lead Data Scientist
Some tips for your application 🫡
Show Your Passion for Data Science:When writing your application, let your enthusiasm for data science shine through! Share specific examples of projects you've worked on and how they relate to the role. We want to see that you’re not just skilled, but also genuinely excited about the impact your work can have.
Tailor Your CV and Cover Letter:Make sure to customise your CV and cover letter for this position. Highlight relevant experience in building ML systems and working with data pipelines. We love seeing candidates who take the time to align their skills with what we’re looking for at Day30!
Be Clear and Concise:Keep your application clear and to the point. Use bullet points where possible to make it easy for us to read. We appreciate a well-structured application that gets straight to the heart of your qualifications and experiences.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re serious about joining our team at Day30!
How to prepare for a job interview at Day30
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts and techniques. Be ready to discuss your experience with building ML pipelines, especially in relation to data ingestion and model deployment. They’ll want to see that you can not only talk the talk but also walk the walk!
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of complex behavioural modelling problems you've tackled in the past. Think about how you approached these challenges and the impact your solutions had. This will demonstrate your ability to think critically and creatively, which is key for this role.
✨Understand Their Business
Familiarise yourself with Day30’s mission and how they help subscription apps improve their ROI. Knowing how your work as a data scientist can directly influence their business outcomes will show that you’re genuinely interested and invested in their success.
✨Be Ready for Technical Questions
Expect some deep dives into technical topics, especially around model evaluation metrics and API development. Brush up on precision, recall, and other relevant metrics, and be prepared to discuss how you would implement robust evaluation frameworks in real-world scenarios.
