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Senior 12+ years requirement and niche GenAI/MLOps focus reduce applicant competition.
Senior ML/GenAI leadership demands deep specialized expertise, limiting easy cross-industry transferability.
Explicit 12+ years, advanced degree and mandatory MLOps/GenAI skills create strict shortlisting filters.
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Lead and manage a team of Data Scientists delivering enterprise-level GenAI and machine learning solutions across multiple business functions such as Finance, Sales, Marketing, and Supply Chain.
Develop, implement, and productize scalable GenAI/Agentic AI and advanced analytics models that can be replicated across markets and integrated into business processes for real-time decision making.
Stay current with cutting-edge GenAI, LLM fine-tuning, and ML techniques and guide their application to solve complex business problems, ensuring delivery of accurate, impactful analytic insights to senior stakeholders.
12+ years experience in Advanced Analytics or Machine Learning roles.
Advanced degree required: PhD, Masters, or B.Tech in Computer Science, Computer Engineering, or related field.
Proficiency in programming languages such as Python and SQL; hands-on experience with MLOps platforms like AWS Sagemaker, Dataiku, or DataRobot.
Working knowledge and implementation experience with GenAI, Agentic AI, LLM fine-tuning, and familiarity with at least one GenAI tool (e.g., ChatGPT, Copilot).
Senior-level experience leading analytics teams and deploying enterprise-scale ML and GenAI solutions with measurable business impact.
Technical expertise in machine learning fundamentals, big data technologies (Hadoop, Spark, NoSQL), and MLOps ensuring scalable and replicable analytics productization.
Ability to translate advanced analytical models into actionable business solutions and communicate insights clearly to diverse, cross-functional stakeholders.