Data Scientist

Job not on LinkedIn

🔥 0 minutes ago

🇪🇺 Europe – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Timeleft

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2020

👥 B2C

🏨 Hospitality

🏪 Marketplace

💰 Seed on 2021-11

B2C • Hospitality • Marketplace

Timeleft is a consumer-facing mobile-first platform and app that organizes weekly in-person social meetups—primarily dinners, drinks, and runs—matching strangers into small groups and handling all logistics (venue, group assignment, timing) so users just show up. The service emphasizes making friendships through curated, language- and personality-aware group assignments, operates in 200+ cities across 52 countries, and reports 3M+ members with hundreds of bookings per day. Timeleft targets adults (18+) seeking regular local social experiences without profiles, swiping, or planning.

📋 Description

• Build, validate, and ship production ML models for propensity, pricing/discount optimization, personalization, churn, and LTV • Own the full ML lifecycle: problem framing, feature engineering, training, evaluation, deployment, monitoring, and retraining • Design and ship a personalized discounting model for paywall decisions • Partner with Product on A/B testing and holdout experiment design to prove causal lift • Build measurement frameworks for pricing and discount decisions • Establish low-latency model serving on GCP using Vertex AI endpoints, Cloud Run, or equivalent • Define feature pipeline patterns across BigQuery/dbt and real-time systems such as Pub/Sub • Set up model monitoring for drift, staleness, and prediction quality • Collaborate with Product, Engineering, and Lifecycle Marketing to ship models as product features • Translate product problems into modeling problems and model outputs into API contracts • Document handoffs so Engineering can own the serving layer long-term • Design and interpret uplift/causal models and experiments measuring incremental revenue or retention impact • Establish a repeatable model-to-production playbook

🎯 Requirements

• Strong Python for data science and ML, including scikit-learn and XGBoost/LightGBM • Proven experience shipping models to production, including at least one model serving live traffic • Hands-on experience with a cloud ML platform, ideally GCP (Vertex AI, BigQuery ML, Cloud Run/Functions), or equivalent AWS/Azure experience • Solid SQL and experience working with a dbt/BigQuery warehouse • Software engineering fundamentals: git, code review, testing, and CI/CD • Causal inference, uplift modelling, or applied experimentation experience • 4–7 years in a data scientist or ML engineer role, with at least one model personally taken from prototype to live production • Quantitative background in computer science, statistics, engineering, or equivalent hands-on experience • Fluent English

🏖️ Benefits

• No benefits, perks, or compensation extras are specified in the posting