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 thatpredict high-value conversions weeks in advance, and deliver these predictions to advertising platforms without compromising user privacy.

We're a two-founder London startup combining deep expertise in performance marketing and machine learning. As ourfirst 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 raretechnical 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 & Model Optimisation

Develop AutoML capabilities optimised for time-series behavioural data and subscription lifecycles
Implement sophisticated feature engineering for event-based data
Design multi-model systems handling various prediction horizons and conversion definitions
Optimise hyperparameter tuning using frameworks like Optuna, AutoGluon, or H2O

MLOps & Platform Engineering

Establish MLOps practices appropriate for a small team: experiment tracking, model registry, and monitoring
Collaborate with engineering on CI/CD pipelines, testing frameworks, and deployment automation
Implement data quality monitoring and model drift detection systems
Design for scalability: from a dozen customers today to 100+ within 12 months

Technical Leadership

Partner with the CTO on technical strategy and architecture decisions
Work directly with client technical teams to understand data nuances and maximise predictive value
Mentor junior data scientists through code review and pairing as the team grows
Co-create OKRs and a technical roadmap with the founding team

The ideal candidate must have...

5-8+ years building production ML systems with demonstrable business impact
Strong experience with time-series analysis and behavioural event modelling
Deep expertise in Python with high code quality standards
Experience with modern ML stack (e.g. pandas/polars, sklearn, xgboost, PyTorch/TensorFlow)
Proven track record delivering end-to-end ML pipelines: ingestion → feature engineering → training → deployment → monitoring
Hands-on experience with cloud data warehouses (e.g. BigQuery, Snowflake)
Track record of building automated, scalable systems from initial prototypes
Right to Work in the UK (we cannot sponsor visas)
Ability to work from Central London office 3 days/week (we believe in-person collaboration is crucial at this early stage)

You may be a great fit if you have any of the following...

AutoML framework experience (e.g. AutoGluon, TPOT, Optuna, H2O.ai)
MLOps tooling (e.g. MLflow, Weights & Biases, Evidently)
Hands-on experience with orchestration tools (e.g. Prefect, Airflow, Dagster)
Building robust API/ETL connectors with retry logic and incremental loading
Statistical depth beyond standard metrics: calibration, cost-sensitive learning, causal inference
Passionate about leveraging the latest LLM tooling for accelerated AI-enhanced delivery without compromising on quality

Domain knowledge bonus points (beneficial but not required)...

Marketing attribution and conversion modelling
Mobile app analytics and user lifecycle prediction
Ad-tech ecosystem and privacy regulations (ATT, GDPR)
Subscription business metrics and retention modelling

Our Interview Process

We respect your time and move quickly.

Application Review: We will look through your application (CV, screening questions, and code samples) to see if you meet the initial requirements for this role.
Initial Screen (20 mins with CTO): Short video call to assess mutual fit and technical background.
Practical Exercise (take home, up to 2 hours): We'll book you in for a 2-hour slot at any time. You will be given a real-world modelling challenge that mirrors our work at Day30. Use any tools you’d use on the job (including LLMs, Copilot, etc) - we care about approach and outcomes, not memorisation.
Technical Deep-Dive (60 mins, in-person): We will walk through your solution, discuss design and architecture decisions, consider alternative approaches, and work through live problem solving on solution extensions.
Founder Conversation (30 mins): Meet both founders for us to understand your motivations and career goals, and for you to ask questions. We want to know that you'll be a great fit for our team, but we also want to help you achieve your goals too.

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Compensation & Benefits

Base Salary: Up to £95,000 per annum (depending on experience)
Equity: Meaningful options as first technical hire
Holidays:25 days of annual paid leave, plus bank holidays.
Flexibility: 3 days/week in Central London office, remote otherwise
Equipment: Top-spec MacBook Pro and any tools you need
Learning Budget: Conferences, courses, and resources to stay current

What Makes This Role Unique

Founder-Adjacent Position: Work directly with two second-time founders, participating in strategic decisions beyond just ML. Your input will shape product direction and company culture.

Technical Challenges: Solve genuinely hard problems in behavioural prediction, working with millions of events to predict conversions weeks in advance. Balance statistical rigour with business pragmatism.

Immediate Impact: Your code ships to production quickly, directly affecting client performance. No layers of bureaucracy or months-long deployment cycles.

Growth Trajectory: As we scale, you'll have the opportunity to build and lead the data science function, defining best practices and mentoring the team.

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