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About this role
Role overview A Staff Data Scientist will own the design and development of scalable machine learning systems powering personalization, recommendation, demand forecasting, and offer targeting across a hospitality platform. The role spans the full ML lifecycle and partners closely with engineering, product, and business stakeholders to shape ML-driven product strategy at scale.
Responsibilities - Own end-to-end ML initiatives from problem framing and data exploration through deployment and monitoring - Design and implement advanced statistical and ML models that improve product performance, operational efficiency, or customer insights - Collaborate with engineering, product, and business partners to define scope, success metrics, and integration approach - Guide architectural decisions and set standards for experimentation, validation, and productionization - Mentor data scientists through design reviews, feedback, and shared expertise - Identify new opportunities where data science can drive measurable business value and lead cross-functional efforts
Requirements - 7+ years of experience in data science with a track record of shipping production ML systems that drive measurable impact - Deep knowledge of statistical modeling, machine learning approaches (tree-based models, time series, deep learning), and evaluation methodology - Experience working with large-scale product data and translating ambiguous problems into scoped ML solutions - Background with distributed data processing, real-time inference, and MLOps frameworks - Prior experience mentoring other data scientists or serving as a technical lead - Proficiency in Python and SQL plus experience with frameworks such as scikit-learn, PyTorch, or TensorFlow - Strong grasp of software engineering practices including modular design, version control, testing, and CI/CD - Hands-on experience with cloud platforms (AWS preferred), including tools like SageMaker, Athena, Glue, DynamoDB, and Bedrock - Excellent communication skills and ability to influence both technical and non-technical stakeholders
Nice to have - Advanced degree in Computer Science, Statistics, or a related STEM field - Familiarity with MLOps tooling for monitoring, drift detection, retraining, and explainability - Experience fine-tuning LLMs and applying reinforcement learning from human feedback
Benefits and work setup - Hybrid work model that supports in-person collaboration while accommodating individual needs