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Mid-senior ML role with popular title but niche Databricks and agentic automation requirements.
Strong ML/AI skills transferable, but Databricks and energy domain mildly increase specialization.
Multiple hard filters: 6–10 years, Databricks mandatory, and applied agentic automation experience.
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Own end-to-end data science products: from problem framing through production deployment and monitoring on AWS/Databricks.
Design and deploy applied agentic automation systems that automate manual analytical or operational workflows with measurable impact.
Provide hands-on technical guidance and mentorship to data scientists and engineers, influencing architecture, modeling, and production best practices without formal people management.
6–10 years of hands-on Data Science/ML experience with production deployment and maintenance of ML solutions (not just prototyping).
Strong, demonstrable hands-on experience with Databricks is mandatory.
At least 1 year of applied agentic automation development deployed or piloted in business context (e.g., LLM agents, agentic workflows).
Proficiency in Python, SQL, Apache Spark (PySpark), AWS cloud-native platforms, and at least one ML framework (TensorFlow, PyTorch, or scikit-learn).
Experienced individual contributor focused on building scalable production ML and automation solutions end-to-end with strong software engineering discipline.
Comfortable working closely with cross-functional business and engineering teams to translate complex problems into technical solutions.
Skilled at raising technical standards through mentorship and hands-on code and design reviews while remaining actively coding and deploying models.