Timeleft

Timeleft

Timeleft offers a unique dining experience where individuals can meet and connect with five strangers each week, using a personality-matching algorithm to facilitate engaging conversations and meaningful interactions over dinner.

Diversified Consumer Services
1-10
Founded 2020

Description

  • Build, validate, deploy, monitor, and retrain production ML models for personalization, pricing, churn, LTV, and propensity use cases.
  • Own the full model lifecycle from problem framing and feature engineering through evaluation and production deployment.
  • Design and launch personalized discounting models for real-time paywall decisions.
  • Partner with Product and Engineering to translate business problems into models and model outputs into API contracts.
  • Design A/B tests, holdouts, uplift models, and causal analyses to measure incremental revenue and retention impact.
  • Establish low-latency model serving patterns on GCP using technologies such as Vertex AI or Cloud Run.
  • Define feature pipelines across BigQuery/dbt and real-time systems such as Pub/Sub.
  • Implement monitoring for model drift, staleness, and prediction quality.
  • Develop reusable model-to-production processes and document serving handoffs for Engineering.
  • Write tested, versioned, reviewed, production-grade code and collaborate with Product, Engineering, and Lifecycle Marketing.

Requirements

  • 4–7 years of experience in a Data Scientist or ML Engineer role.
  • Experience personally taking at least one model from prototype to live production serving real users or traffic.
  • Strong Python skills with scikit-learn and XGBoost or LightGBM; PyTorch or TensorFlow is a plus.
  • Experience with a cloud ML platform, ideally GCP, including Vertex AI, BigQuery ML, Cloud Run, or Cloud Functions.
  • Strong SQL skills and experience with dbt and BigQuery.
  • Software engineering fundamentals including Git, code review, testing, and CI/CD.
  • Experience with causal inference, uplift modeling, or applied experimentation.
  • Quantitative background in computer science, statistics, engineering, or equivalent practical experience.
  • Experience with streaming or event pipelines such as Pub/Sub, Dataflow, or Kafka is preferred.
  • B2C, subscription, marketplace, pricing, discounting, personalization, feature stores, bandits, reinforcement learning, or startup experience is preferred; fluent English required.

Interested in this position?

Apply directly on the company website

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