Senior ML Engineer
Posted 8
views
Summary
Remote Senior ML Engineer at Techsa who owns the predictive customer scores shipping with the product (churn, propensity, lifetime value, spend intent, response) end to end, from training and deployment to retraining and monitoring. Core stack: Python, Spark, tabular predictive modeling, gradient boosting, and MLOps practice.
This is a remote position.
We are looking for a Senior ML Engineer to join our team and take ownership of key areas within our technology and data platform. Owns the predictive customer scores that ship with the product: churn, propensity, lifetime value, spend intent, and response scoring, from training through to monitoring.
Key Responsibilities
• Own and deliver solutions within the scope of the role, from requirements and technical/design decisions through implementation and continuous improvement.
• Work closely with engineering, product, data, design, and business stakeholders to translate requirements into practical, scalable solutions.
• Apply strong engineering and/or domain expertise to build reliable, maintainable, and production-ready capabilities.
• Contribute to architecture, standards, documentation, quality, and technical decision-making appropriate to the role.
• Identify performance, scalability, data quality, usability, reliability, or operational risks and address them proactively.
• Collaborate across teams to ensure solutions integrate effectively with existing systems and platform components.
Requirements
• Experience: 5+ years of relevant professional experience.
• Strong hands-on experience with: Applied machine learning, tabular predictive modelling, feature engineering, gradient boosting, model evaluation and calibration, Python, Spark, MLOps.
• Applied machine learning with models running in production, not research or proof of concept.
• Deep hands on with tabular predictive modelling on customer data.
• Has built churn or propensity models in telco, banking, or retail.
• Training, deployment, and retraining pipelines in a self managed environment.
• MLOps practice: model registry, versioning, retraining, monitoring, and drift detection.
• Comfortable working inside a data platform rather than a notebook.
Domain Requirement:
• Telco or Banking is a must
Preferred Qualifications
• Uplift or causal modelling for incremental targeting.
• Feature store design.
• Working with commercial stakeholders on what a prediction is used for.