We are seeking a talented, customer-focused applied scientist to join our JCI Measurement and Optimization Science Team (JCI MOST), with a charter to build scalable systems that automatically detect pricing defects, implement intelligent corrections, measure intervention impacts, and deliver data-driven pricing strategies to leadership.

This role requires an individual with exceptional machine learning, LLM, and Causal Inference expertise, strong system architecture capabilities, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit to drive measurable improvements in pricing quality and competitiveness.

We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and thrives in a fast-paced, data-driven environment.

Key job responsibilities

• Key Job Responsibilities
• Build scalable defect detection systems that automatically identify pricing anomalies, competitive gaps, and quality issues across millions of products using ML and LLM models and real-time monitoring
• Deploy automated defect remediation with intelligent pricing recommendations, and validation frameworks that reduce manual intervention requirements
• Measure impact and drive strategy by establishing robust measurement frameworks, designing large-scale experiments, building attribution models, and developing executive dashboards that translate findings into actionable insights for leadership
• Lead cross-functional collaboration by partnering with product, engineering, and science teams to deploy solutions at scale while communicating complex technical concepts clearly to executive audiences
• Stay at the forefront of innovation by applying state-of-the-art techniques in ML, deep learning, LLM, and causal inference to pricing quality challenges while fostering rapid experimentation and continuous learning

Basic Qualifications

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, Mx Net, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.
• Experience with neural deep learning methods and machine learning
• Knowledge of advanced causal modeling techniques, both in experimental and observational settings

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