The Role
Own production machine learning solutions for predictive customer scores, including churn, propensity, lifetime value, spend intent, and response models. Responsibilities include requirements, architecture, implementation, deployment, retraining, monitoring, drift detection, and continuous improvement. Collaborate with engineering, product, data, design, and business stakeholders to build scalable, reliable capabilities within a data platform. The role requires deep experience with tabular customer modeling and production MLOps in telco or banking.
Summary Generated by Built In
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.
Skills Required
- 5+ years of relevant professional experience
- Applied machine learning experience with production models, not only research or proof-of-concept work
- Strong hands-on experience with tabular predictive modeling and customer data
- Strong hands-on experience with feature engineering, gradient boosting, model evaluation, and model calibration
- Strong hands-on experience with Python and Spark
- Strong hands-on MLOps experience
- Experience building churn or propensity models in telecommunications, banking, or retail
- Experience building training, deployment, and retraining pipelines in a self-managed environment
- Experience with model registries, versioning, retraining, monitoring, and drift detection
- Telco or banking domain experience
- Experience with uplift or causal modeling for incremental targeting
- Feature store design experience
- Experience working with commercial stakeholders on prediction use cases
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The Company
What We Do
Techsa is a Cairo-based software services and development company that builds technology solutions tailored to client needs. Its work combines custom and mobile-app development, IT consulting, and software innovation with IoT/data-management platforms and analytics, including AI/ML-driven analysis. It serves sectors such as water and wastewater, logistics, roads, and property maintenance, and also describes itself as a partner for recruiting, onboarding, and managing software teams.
