Tech Stack

Description

You will own ML-powered features end-to-end, from design through deployment, partnering with product, design, and engineering to build scalable ML models that improve customer experiences and drive business growth. You'll work on real-world problems like revenue forecasting, churn analysis, and personalization, and be an integral part of a high-performing AI team.

Requirements

• 4+ years industry experience in applied ML with production models
• Full ML lifecycle expertise: EDA, feature engineering, model training, deployment, monitoring
• Experience with statistical/ML models for forecasting, churn, recommendation, NLP, anomaly detection
• Hands-on experience with managed ML services like Vertex AI or SageMaker for high-traffic apps
• Strong programming in Python, Scala, Java, or SQL, and ML frameworks like TensorFlow, PyTorch

Responsibilities

• Own ML features end-to-end: design, deployment, and monitoring
• Partner with product, design, and engineering to scope work and define success metrics
• Understand and troubleshoot deployment pipelines for ML services and data pipelines
• Use monitoring tools to triage alerts and incidents, participate in on-call rotation
• Serve as subject matter expert for owned features, including data contracts and SLAs