Senior ML Engineer
Techsa · United States · posted today ago
Listing supplied by Himalayas. 101 Careers did not originate this post and applications are handled by the employer.
About this role
Category: Technology
Location:
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
Details
Location:
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.
- Telco or Banking is a must
- Uplift or causal modelling for incremental targeting.
- Feature store design.
- Working with commercial stakeholders on what a prediction is used for.
Details
Originally posted on Himalayas
