Lead Data Scientist in London

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’re 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.

• 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.

• 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.

• 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.

Requirements

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 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.

Benefits

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.