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Mid-level generalist ML role in metros with broad requirements increases applicant competition significantly.
Core ML and data engineering skills are transferable, though environmental domain knowledge provides added advantage.
Extensive mandatory technical stack, production deployment, and engineering expectations create stringent shortlisting filters.
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Design, build, and deploy advanced analytical and machine learning models for environmental data.
Develop and maintain robust, scalable data pipelines and infrastructure using modern big data and cloud technologies.
Collaborate with cross-functional teams, contribute to system architecture, and support production deployment and quality assurance of data science solutions.
Strong Python programming skills with advanced OOP and software engineering practices.
Experience in machine learning model development and evaluation.
Proficiency in relational databases, data modeling, and pipeline development with big data tools (e.g., Spark, Hadoop).
Bachelor’s or master’s degree in a quantitative field (Mathematics, Computer Science, Engineering, Economics, Data Science) or equivalent industry experience.
Experienced in both data science and data engineering, capable of end-to-end solution delivery including production deployment.
Skilled in cloud-native infrastructure, containerization (Docker, Kubernetes), CI/CD, and system architecture discussions, preferably with Azure experience.
Able to communicate complex analytics to diverse stakeholders and capable of mentoring or leading project delivery components.