Lead Data Scientist

Day30; City of London; Hybrid; GBP 80,000 - 95,000; Full time

Job summary

A growing tech startup in London is seeking a Data Scientist to develop ML capabilities and work closely with founders. You will implement end-to-end ML pipelines and lead technical efforts, providing immediate impact on business outcomes. Ideal candidates have extensive experience in ML systems, strong Python skills, and the ability to work in a collaborative environment. This role offers significant autonomy and the opportunity to shape the company's technical direction.

Benefits

Base salary up to £95,000

Equity options

25 days of paid leave

Learning budget for conferences and courses

Qualifications

• 5-8+ years building production ML systems with demonstrable business impact.
• Strong experience with time-series analysis and behavioral event modeling.
• Deep expertise in Python with high code quality standards.
• Proven track record delivering end-to-end ML pipelines.
• Hands-on experience with cloud data warehouses.

Responsibilities

• Design and implement end-to-end ML pipelines.
• Transform client-specific notebooks into modular pipelines.
• Develop AutoML capabilities for time-series data.
• Establish MLOps practices and collaborate with engineering.
• Mentor junior data scientists as the team grows.

Skills

Time-series analysis

Behavioral event modeling

Python

End-to-end ML pipelines

Cloud data warehouses

Tools

pandas/polars

sklearn

PyTorch

TensorFlow

Job description

Overview

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, and 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 are 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

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

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 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

Note: This description focuses on the role, responsibilities, and qualifications. Any boilerplate or extraneous postings have been omitted for clarity.