Remote Engineering Manager - Identification Accuracy & ML

Jobgether; South Africa; On-site; ZAR 2,428,000 - 3,399,000; Full time

Benefits offered by this job

Fully remote

Global team

High autonomy

Job summary

Jobgether is seeking an Engineering Manager for Identification Accuracy based in a fully remote, globally distributed team. You will lead a multidisciplinary group of ML engineers, data scientists, and analytics engineers, shaping the roadmap and translating business needs into technical initiatives.

You will drive production ML systems, manage data pipelines, feature engineering, and model deployment, while mentoring leaders and fostering strong teams in a high-velocity fraud-detection

Qualifications

• 5+ years in software/ML/data science leadership, incl. team management.
• Experience delivering production ML systems with data pipelines and model deployment.
• Proven ability to grow high-performing multidisciplinary teams.
• Strong understanding of ML systems, data platforms, and MLOps tooling.
• Experience with large-scale behavioral/event data is a plus.

Responsibilities

• Lead a multidisciplinary Identification Accuracy team of ML engineers, data scientists, analysts and analytics engineers.
• Own and drive the team's technical roadmap and collaboration with stakeholders.
• Improve model quality and production ML system reliability at massive scale.
• Mentor engineers and foster a culture of psychological safety and high performance.
• Translate business needs into technical priorities and product improvements.

Skills

Leadership

Machine Learning

Data Science

MLOps

Team Building

Communication

Tools

DBT

MLflow

Feature Stores

CI/CD

Experiment Tracking

Job description

Jobgether is seeking an Engineering Manager for Identification Accuracy based in a fully remote, globally distributed team. You will lead a multidisciplinary group of ML engineers, data scientists, and analytics engineers, shaping the roadmap and translating business needs into technical initiatives.

You will drive production ML systems, manage data pipelines, feature engineering, and model deployment, while mentoring leaders and fostering strong teams in a high-velocity fraud-detection