Data Scientist Architect

Requirements:
Strong Python and intermediate SQL skills.
Experience building, deploying, and evaluating ML/NLP/GenAI systems.
Experience with ML system design.
Very strong (including written) communication skills and ability to break down and articulate complex tasks into implementation strategies.
Software engineering best practices, including testing, OOP, etc.
Knowledge of cloud systems, especially Azure, preferred.
A more traditional ML perspective, some knowledge and understanding of mathematical optimisers, how to select the best ones for the purpose and set parameters.
Experience working closely with other data scientists, data engineers, software engineers, data managers and business partners.
Can build scalable, reusable, impactful data science products, usually containing statistical or machine learning algorithms, in collaboration with data engineers and software engineers.
Can carry out data analyses to yield actionable business insights.
Hands-on experience (typically 5+ years) designing, planning, prototyping, productionizing, maintaining and documenting reliable and scalable data science products in complex environments.
Applied knowledge of data science tools and approaches across all data lifecycle stages.
Thorough understanding of the underlying mathematical foundations of statistics and machine learning.
Development experience in one or more object-oriented programming languages (e. g. Python, Go, Java, C++).
Advanced SQL knowledge.
Knowledge of experimental design and analysis.
Customer-centric and pragmatic mindset.
Focus on value delivery and swift execution, while maintaining attention to detail.

Experience
5-9 yrs