Senior ML Engineer
# Senior ML Engineer **Techsa** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Machine-Learning-Engineer]` `[Senior-ML-Engineer]` `[Data-Scientist]` `[Senior-ML-Engineering]` `[Senior-AI-ML-Engineer]` `[Senior-Applied-ML-Engineer]` `[Senior-ML-Platform-Engineer]` `[Senior-AI-Engineer]` `[Senior-Staff-Machine-Learning-Engineer]` `[Senior-AI-ML-Developer]` --- ### About the 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 - 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. Details Originally posted on Himalayas
- Machine-Learning-Engineer
- Senior-ML-Engineer
- Data-Scientist
- Senior-ML-Engineering
- Senior-AI-ML-Engineer
- Senior-Applied-ML-Engineer
- Senior-ML-Platform-Engineer
- Senior-AI-Engineer
- Senior-Staff-Machine-Learning-Engineer
- Senior-AI-ML-Developer
Наблюдалась 2026-10-12, впервые 2026-10-12, источник — Himalayas (JSON API).