Back
Tier-1 brand, remote and popular mid-level ML role attracts many qualified applicants.
Specialized ML, MLOps, and telemetry requirements increase domain bias but skills remain transferable across industries.
Explicit 6+ years and mandatory ML, MLOps, and production deployment skills enforce strict filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead development and deployment of end-to-end machine learning models (PQL scoring, churn prediction, expansion modeling) with defined latency and accuracy SLAs in a multi-product SaaS environment.
Build and maintain MLOps pipelines including dataset versioning, feature drift monitoring, automated retraining, and model registry management, ensuring reliable production deployments.
Own model observability with dashboards and alerts for performance degradation, lead incident response, and collaborate cross-functionally to translate telemetry insights into business actions for Sales, Product, and Leadership.
6+ years of experience in applied data science or product analytics.
Strong proficiency in SQL and Python with libraries such as Pandas, Scikit-learn, Statsmodels, PyTorch or TensorFlow.
Experience building, deploying, and monitoring ML models in production with focus on reliability and performance.
Experience with MLOps platforms (e.g., MLflow, SageMaker, Vertex AI, Kubeflow) and data observability tools (e.g., Great Expectations, Soda, Monte Carlo).
Comfortable working in a multi-product SaaS environment with complex telemetry and event-level data to model user behaviour and product engagement.
Skilled at end-to-end ML lifecycle ownership including model development, deployment, monitoring, and incident management under SLA constraints.
Experienced in statistics, causal inference, experimentation design, and leveraging generative AI or LLM techniques to automate insight generation from telemetry data.