Machine Learning Specialist (Research & Engineering)

1. Research & Experimental Innovation

Advanced Research: Conduct deep-dive research into state-of-the-art (SOTA) architectures and foundational models to solve complex business problems like credit scoring, fraud detection and personalization.

1. Data Strategy & Engineering

Pipeline Design: Lead the design of experimentation datasets and production data pipelines, focusing on feature engineering and data augmentation.

1. Production Engineering & MLOps

Deployment & Orchestration: Architect and manage the end-to-end deployment of models using containers (Docker, Kubernetes) and CI/CD pipelines.

1. Squad Collaboration & Agile Delivery

Active Squad Collaboration: Work as a core member of a cross-functional squad, aligning daily with Data Engineers, Backend Developers, and Product Owners to ensure seamless product integration.

1. Documentation & Knowledge Leadership

Technical Documentation: Author and maintain the full technical stack documentation, ranging from scientific research findings and experimental logs to system architecture diagrams and deployment guides.

Qualifications:

Education: Undergraduate degree in a quantitative field (e.g. Computer Science, Statistics, Information Technology or Physics or Mathematics). A Graduate degree (Master's or PhD) is highly preferred for the research component.

Experience: 3+ years in a functionally similar role (Data Science , ML Research, or ML Engineering.

Technical Proficiency: Expert-level Python and SQL.

Strong experience with ML frameworks (e.g. Pytorch, TensorFlow, JAX).

Hands-on experience with Git, CI/CD and MLOps tools.

Mindset: A strong bias toward model explainability and security.