The team you'll be part of

We are seeking an experienced Data Science/Machine Learning Engineer with a proven track record of developing and deploying ML solutions at scale. The ideal candidate combines deep statistical knowledge with strong engineering capabilities, bringing 10+ years of experience in building production-ready AI/ML systems. This role bridges the gap between cutting-edge machine learning research and practical business applications, requiring both technical excellence and business acumen.

 

Required Qualifications

10+ years of professional experience in data science and machine learning in production environments, with Proven track record of deploying ML models in production environments. Ph.D. in Computer Science, Statistics, Mathematics, or related field. Deep expertise in machine learning algorithms, statistical modelling, and optimization techniques. Strong programming skills in Python and proficiency in ML frameworks and distributed computing frameworks, and large-scale data processing. Expertise in ML infrastructure, including feature stores, model serving, and monitoring systems. Strong knowledge of ML testing, validation methods, and experimental design. Experience with MLOps practices and tools (ML pipelines, version control, containerization). Proficiency in Azure stack and their ML services. Deep understanding of data structures, algorithms, and software design principles.

Preferred Qualifications

Expertise in deep learning architecture design and optimization. Background in distributed systems and high-performance computing. Contributions to open-source ML projects or frameworks.

Responsibilities

Design and develop advanced machine learning solutions, from concept to production deployment, addressing complex business challenges. Build and optimize data pipelines, features, and ML infrastructure to support large-scale model training and inference. Lead the development of ML platforms and tools that enable efficient model development, deployment, and monitoring. Conduct sophisticated data analysis and create advanced statistical models for complex prediction and classification problems. Implement and maintain production ML systems with focus on scalability, reliability, and performance. Drive best practices for ML experimentation, version control, and reproducibility. Collaborate with domain experts to translate business requirements into technical solutions. Mentor data scientists and engineers on ML engineering best practices and system design. Establish frameworks for model performance monitoring, drift detection, and automated retraining. Research and implement new ML techniques to improve existing systems and solve novel problems.