Sr. Applied Scientist, JCI Measurement and Optimization Science Team

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


- 3 years of building machine learning models for business application experience
- PhD or Masters degree and 6 years of applied research experience
- Experience programming in Java C Python or related language

- Experience with modeling tools such as R scikit-learn Spark MLLib MxNet 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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