Lead Data Scientist

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

Day
30 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.

• 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 life cycles
• 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, Auto Gluon, 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

• 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 / Tensor Flow)
• Proven track record delivering end‑to‑end ML pipelines: ingestion → feature engineering → training → deployment → monitoring
• Hands‑on experience with cloud data warehouses (e.g. Big Query, 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)

• AutoML framework experience (e.g. Auto Gluon, 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
• Pass…