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Tier-1 brand, remote/hybrid, mid-level ML role with broad required skillset increases competition.
Core ML and MLOps skills transfer across industries, but SaaS telemetry and product analytics domain knowledge raise specificity.
Explicit 6+ years requirement plus mandatory MLOps, ML frameworks, and observability tools makes filters strict.
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Lead end-to-end ML model development and deployment for predictive analytics including PQL scoring, churn prediction, and expansion modeling in a multi-product SaaS environment.
Build and maintain MLOps pipelines with dataset versioning, feature drift monitoring, automated retraining, and model registry management ensuring model reliability and performance.
Own model observability by creating dashboards and alerts for performance issues and lead incident response; collaborate cross-functionally to turn data insights into business actions.
6+ years experience in applied data science or product analytics (or equivalent practical experience).
Proficient in advanced SQL and Python (including Pandas, Scikit-learn, Statsmodels, PyTorch or TensorFlow).
Experience building, deploying, and monitoring ML models in production with focus on reliability and performance.
Familiarity with MLOps platforms (e.g., MLflow, SageMaker, Vertex AI, Kubeflow) and data quality/observability tools (e.g., Great Expectations, Soda, Monte Carlo).
Experienced data scientist capable of designing and operating scalable, production-grade ML systems with strong ownership of model lifecycle and observability.
Skilled at working with telemetry and event-level data to model user behavior and product engagement in a SaaS context.
Comfortable collaborating across product, sales, and engineering stakeholders to embed analytics into business decision-making with expertise in experimentation design and causal inference.