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Tier-1 brand plus remote hiring and a mid-level ML generalist profile increases candidate competition.
Role demands specialized ML, MLOps, telemetry, and experimentation skills, limiting cross-industry transferability.
Explicit 6+ years requirement plus mandatory ML, MLOps, and tech-stack requirements creates strict filters.
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Lead end-to-end development and deployment of ML models for PQL scoring, churn prediction, and expansion modeling within a multi-product SaaS environment.
Build and maintain MLOps pipelines including dataset versioning, feature drift monitoring, automated retraining, and observability dashboards with incident response ownership.
Collaborate cross-functionally with Product, Sales, and Engineering teams to deliver a production-grade intelligence engine that supports strategic decision-making.
6+ years of experience in applied data science or product analytics (or equivalent practical experience).
Proficiency in advanced SQL and Python (including Pandas, Scikit-learn, Statsmodels, PyTorch or TensorFlow).
Experience building, deploying, and monitoring ML models in production with a focus on reliability and accuracy.
Experience with MLOps platforms (e.g., MLflow, SageMaker, Vertex AI, Kubeflow) and data quality/observability tools (e.g., Great Expectations, Soda, Monte Carlo).
Strong background in statistics, causal inference, and experimentation design applied to business problems.
Experienced in working with telemetry and event-level data to model user behavior and product engagement.
Comfortable operating in a hybrid work environment collaborating across multiple teams to drive complex data product development and deployment.