OverviewThe Role in 30 SecondsFirst 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 DoDay30 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 DoCore ML Pipeline DevelopmentDesign and implement end-to-end ML pipelines from data ingestion through model deployment and signal deliveryTransform client-specific Jupyter notebooks into modular, config-driven pipelines using orchestration tools such as Prefect/AirflowBuild robust API connectors handling schema evolution, incremental updates, and data quality validationImplement comprehensive machine learning model evaluation frameworks blending technical metrics (precision, recall, PRAUC, probability calibration) with business outcomesAutoML & Model OptimisationDevelop AutoML capabilities optimised for time-series behavioural data and subscription lifecyclesImplement sophisticated feature engineering for event-based dataDesign multi-model systems handling various prediction horizons and conversion definitionsOptimise hyperparameter tuning using frameworks like Optuna, AutoGluon, or H2OMLOps & Platform EngineeringEstablish MLOps practices appropriate for a small team: experiment tracking, model registry, and monitoringCollaborate with engineering on CI/CD pipelines, testing frameworks, and deployment automationImplement data quality monitoring and model drift detection systemsDesign for scalability: from a dozen customers today to 100+ within 12 monthsTechnical LeadershipPartner with the CTO on technical strategy and architecture decisionsWork directly with client technical teams to understand data nuances and maximise predictive valueMentor junior data scientists through code review and pairing as the team growsCo-create OKRs and a technical roadmap with the founding teamRequirementsThe ideal candidate must have...5-8+ years building production ML systems with demonstrable business impactStrong experience with time-series analysis and behavioural event modellingDeep expertise in Python with high code quality standardsExperience 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 → monitoringHands-on experience with cloud data warehouses (e.g. BigQuery, Snowflake)Track record of building automated, scalable systems from initial prototypesRight 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 loadingStatistical depth beyond standard metrics: calibration, cost-sensitive learning, causal inferencePassionate about leveraging the latest LLM tooling for accelerated AI-enhanced delivery without compromising on qualityDomain knowledge bonus points (beneficial but not required)...Marketing attribution and conversion modellingMobile app analytics and user lifecycle predictionAd-tech ecosystem and privacy regulations (ATT, GDPR)Subscription business metrics and retention modellingOur Interview ProcessWe 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.BenefitsCompensation & BenefitsBase Salary: Up to £95,000 per annum (depending on experience)Equity: Meaningful options as first technical hireHolidays: 25 days of annual paid leave, plus bank holidays.Flexibility: 3 days/week in Central London office, remote otherwiseEquipment: Top-spec MacBook Pro and any tools you needLearning Budget: Conferences, courses, and resources to stay currentWhat Makes This Role UniqueFounder-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. #J-18808-Ljbffr