Senior Staff AI/MLE Scientist

Responsibilities

• Own the ML stack end-to-end across feature pipelines, model training, and deployment, with broad influence over the team's ML roadmap.
• Set the gold standard for production ML and enable the broader organization with tooling and infrastructure to ensure quality across the team — feature engineering hygiene, training reproducibility, deployment patterns, and post-launch monitoring.
• Train, deploy, and maintain batch models that power targeting, retention, and personalization, delivering tens of millions of dollars of business value.
• Evolve shared infrastructure (feature engineering, MLOps) that empower the entire organization: improve reliability, reduce time-to-feature for downstream modelers, and ensure features are consistent between training and scoring.
• Advise and mentor other data scientists on modeling best practices, code quality, and how to ship models that hold up in production.
• Embrace agentic modes of development to accelerate your work and the team’s work
• Partner with marketing, product, and analytics leadership to identify the highest-leverage modeling opportunities, scope them, and turn predictions into actions.
• Establish processes and systems to create scalable ML capabilities rather than one-off models — feature reuse, model templates, automated retraining, and monitoring.
• Anticipate future business challenges and design ML methodologies, architectures, and systems to address them.

Qualifications

• At least 7 years of experience building and deploying production machine learning systems, with significant time spent owning models end-to-end (data → features → training → deployment → monitoring).
• Demonstrated expertise in batch ML model development — including classification, propensity, and uplift modeling — with a track record of models that have driven measurable business impact in production.
• Strong software engineering fundamentals: experience contributing to and maintaining shared ML libraries, feature stores, or feature engineering frameworks (e.g., featlib, feat-layer, Feast, Tecton, or equivalent).
• Hands-on experience training and deploying models on modern ML platforms (Databricks, Spark MLlib, scikit-learn, XGBoost/LightGBM, PyTorch); familiarity with MLOps patterns (CI/CD for models, feature versioning, drift monitoring).
• A demonstrated ability to navigate ambiguity and deliver results that significantly impact the business.
• Excellent communication skills and the ability to work effectively with both technical and non-technical partners.
• Proficiency in Python, SQL, and PySpark.

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