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Strong brand, mid-level generalist ML/data role, and metro location create high candidate competition.
Technical ML and data skills transfer, but maritime/commodity domain knowledge raises fit sensitivity to medium.
Explicit 3-6 year requirement plus mandatory Python, PySpark, ML, and cloud/DevOps skills increases strictness.
Design, develop, and maintain scalable data-processing and analytical workflows using Python, PySpark, SQL, and R.
Build and integrate AI-assisted workflows employing generative AI techniques with clear governance and monitoring to improve data discovery, transformation, and operationalization.
Contribute to cloud infrastructure and DevOps activities including AWS, Docker, Terraform, CI/CD, production support, and collaborate cross-functionally to deliver reliable data and AI products in the maritime commodities domain.
3-6 years of relevant experience including at least 3 years hands-on with Python and Spark/PySpark.
Strong skills in Python data tooling, software engineering fundamentals, SQL, and experience with AI/ML or generative AI solutions.
Working knowledge of cloud platforms preferably AWS, familiarity with Docker, and experience in data engineering or closely related roles.
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, AI, ML, or related quantitative disciplines from a reputed institution.
Experienced in designing and operating production-grade data and AI pipelines at scale, with strong focus on quality, observability, and security in cloud environments.
Skilled in building AI workflows that combine generative AI, prompt engineering, and responsible AI governance integrated into business processes.
Comfortable working in a multidisciplinary team, translating complex business questions into analytical approaches within the maritime commodities or related economic data domains.