Data Scientist - Machine Learning

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DescriptionJob overview and responsibilities

The Data Scientist designs, develops, and implements scalable AI and Generative AI solutions that improve customer experience, business efficiency, and decision-making. The role builds production-grade applications and workflows using Python, SQL, large language models, Retrieval-Augmented Generation, and agentic AI frameworks. It applies advanced analysis, prompt engineering, evaluation, and observability practices to deliver reliable and useful AI capabilities. The role partners with cross-functional teams to translate ambiguous business needs into practical solutions and clearly communicate findings and recommendations.

This position is offered on local terms and conditions. Expatriate assignments and sponsorship for employment visas, even on a time-limited visa status, will not be awarded.

QualificationsWhat’s needed to succeed (Minimum Qualifications):

Master’s or Ph.D. degree. Data Science, Statistics, Engineering, Computer Science, Operations Research, or a related STEM field. 4–7+ years of industry experience in AI engineering, data science, machine learning, or applied advanced analytics, including production model deployment. Advanced proficiency in Python and SQL, with software engineering best practices including object-oriented programming, Git, and CI/CD; experience architecting data pipelines and working with large datasets in relational or distributed databases such as Snowflake, BigQuery, SQL Server, or Teradata; deep expertise in Generative AI, including prompt engineering, LLM fine-tuning, embeddings, RAG, vector databases, and Agentic AI frameworks; hands-on MLOps or LLMOps experience covering model monitoring, latency management, drift mitigation, and system reliability; Fluent in written and spoken English.

What will help you propel from the pack (Preferred Qualifications):

Advanced coursework or specialization in machine learning, artificial intelligence, applied statistics, or cloud computing. Experience deploying and scaling enterprise Generative AI applications, particularly multi-agent frameworks or enterprise-scale Retrieval-Augmented Generation. Relevant cloud, machine learning, or MLOps certification.