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Soho House

Global private members' club and hospitality

Lead Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeLondon, UKNo salary listedIn-person

About the Job

Requirements

  • Significant hands-on data science experience, typically 5+ years, across problem types such as classification and propensity, recommendation, time-series forecasting, clustering and segmentation.
  • A track record of putting models into production and owning them after deployment, not just building prototypes.
  • Strong Python skills and familiarity with its data science ecosystem.
  • A deep grounding in statistics, experimentation and causal inference, including A/B testing, power analysis, variance reduction, difference-in-differences, synthetic control or similar methods.
  • Ability to turn a vague business question into a well-framed problem and explain results in plain English.
  • Ability to work as a senior hands-on individual contributor, set direction for others and review their work without line-managing them.

Responsibilities

  • Own the design, build and continuous improvement of the machine-learning portfolio, including member churn and segmentations, personalisation and recommendation engines such as events, and demand forecasting for labour and operational planning.
  • Frame business problems with stakeholders, choose the appropriate approach, which may not always be machine learning, and set clear success metrics tied to commercial outcomes.
  • Own the full model lifecycle: scoping, development, validation, deployment, monitoring, scheduled recalibration and retirement.
  • Set machine-learning operations standards for version control, model registry, documentation and governance.
  • Work with Data Engineering to put models into production on the stack (GCP, Snowflake and Airflow), with reliable scoring pipelines and outputs for downstream tools and teams.
  • Build monitoring and observability for production model accuracy against agreed thresholds and baselines, data drift and business impact, with clear alerts and review cycles.
  • Set advanced measurement standards across the wider analytics team, educate analysts and act as their expert on experimentation and causal inference, and find pragmatic, robust alternatives when ideal methods are not possible, such as when randomisation cannot be done.
  • Create reusable frameworks, templates and guidance so analysts can run sound tests and impact measurement independently.
  • Coach and upskill analysts, supporting the goal of building data literacy across the business.
  • Partner with Membership, Marketing/CRM, Digital, Operations and Finance to prioritise a value-based data science roadmap.
  • Explain complex methods and uncertainty clearly to non-technical audiences, including senior leadership.
  • Direct and quality-assure offshore data science resources when workload requires by scoping tasks, reviewing code and models, and maintaining consistent standards.

Desired Qualifications

  • Experience with a modern cloud data stack, ideally Snowflake (Snowpark, Cortex) and dbt.
  • Practical machine-learning operations experience, such as MLflow or similar, Git, CI/CD, containerisation, automated retraining and monitoring.
  • Experience with large language model and generative AI tools, both for development workflow and for data enrichment, such as classifying or extracting structure from unstructured text.
  • Experience in hospitality, membership, subscription, retail or consumer businesses.
  • Experience coaching analysts or building a measurement or experimentation culture.

About the Company

Soho House

Global private members' club and hospitality

Soho House is a global private membership platform that connects creatives and other professionals through exclusive Clubs called Houses, plus online access and city-only options. Members pay fees to access workspaces, social spaces, events, bedrooms, dining, and related services, with revenue also coming from in-house food/beverage, hotels, retail (Soho Home), and standalone venues. It differentiates itself by building a curated, community-driven ecosystem that combines hospitality, workspaces, and a lifestyle brand across many cities, rather than focusing on a single hotel or club. Its goal is to create a globally connected community where members can collaborate, socialize, and access a consistent, premium experience across locations.

Company size
5,001-10,000
Company stage
IPO
Headquarters
London, United Kingdom
Founded
1995

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