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Mid-level ML role, broad skills, common 3–6 year band and metro hiring increase candidate density.
Core ML and analytics skills are transferable, though aviation operations experience is preferred.
Explicit 3+ years requirement plus mandatory ML, SQL and tooling skills make shortlisting strict.
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Apply advanced analytics, machine learning, AI, and statistical modeling to solve complex business problems and improve flight operations efficiency.
Develop and deploy scalable analytical products, dashboards, and automated decision-support solutions.
Collaborate with business stakeholders and technical teams to translate operational challenges into actionable analytical insights, including AI and Generative AI use cases like LLM-based solutions and text analytics.
Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Operations Research, Economics, or related quantitative field.
Minimum 3+ years of experience in modeling and machine learning.
Proficiency in R or Python and experience with database querying tools such as Teradata SQL and/or Microsoft TSQL.
Experience with predictive modeling techniques and analytical toolsets, including Power BI or similar visualization tools, and working knowledge of AI/Generative AI concepts (LLMs, prompt engineering, RAG).
Strong experience in large-scale data manipulation and predictive analytics within operational environments, preferably aviation or related sectors.
Capable of deploying AI/ML solutions and supporting operational decision-making with focus on innovation and best practices.
Experience with cloud platforms (AWS, Azure), Big Data ecosystems (Hadoop/Spark), and MLOps practices to support production analytics solutions.