Senior Data Science and Machine Learning Engineer

at  Nokia

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.

• 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.