Sr Data Scientist Product Performance

Intuitive•Sunnyvale, CA

About The Position

Primary Function of Position: The Sr Data Scientist Product Performance leverages deep expertise in data science, machine learning, statistical modeling and analytics to enhance product telemetry and service technology across Intuitive’s portfolio. The role is responsible for building robust data pipelines, developing advanced ML/AI models, and collaborating with cross-functional teams to advance proactive and predictive service initiatives. The position also ensures compliance with regulatory standards and continuously drives innovation and data-driven improvements in product performance and customer experience.

Requirements

Bachelor’s degree (minimum) with 5+ years of experience in applied data science; an advanced degree is preferred.
Proficiency with machine learning and statistical modeling frameworks, with hands-on experience applying models for classification, survival analysis, anomaly detection, and time-series forecasting with human-in-the-loop feedback cycles.
Expertise in LLM techniques: prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning, transfer learning, and other relevant strategies for NLP-driven solutions to analyze unstructured text data.
Strong skills in distribution analysis, statistical modeling, failure analysis, and predictive maintenance analytics.
Fluent in Python, with strong SQL skills, especially in the Snowflake platform.
Hands-on experience with dashboarding and data visualization tools (e.g., Tableau, Apache Superset, Matplotlib).
Strong interpersonal skills with ability to work cross-functionally in ambiguous, time-sensitive and high-impact environments; adept at synthesizing technical and business needs for global service and engineering teams.
Skilled in clear communication and presenting technical concepts to both technical and executive audiences.
Results-oriented with excellent analytical and troubleshooting skills, capable of independently leading initiatives and managing competing priorities in a high-tech setting.
Demonstrated track record of data-driven decisions and critical thinking, asking questions to get to the root cause of an issue and identifying patterns in the face of ambiguity and unclear ownership
Sense of urgency to solve customer and service issues and disseminate learning
Organized with excellent project management skills
Must be proficient in the use of Microsoft Office suite of applications
Ability to travel 25%

Nice To Haves

Experience in reliability statistics, survival analysis, and time-to-event modeling is a plus.

Responsibilities

Design, deploy, and maintain both supervised and unsupervised machine learning models to support proactive diagnostics, anomaly detection, classification, and time-series forecasting (e.g., part failure, inventory management).
Leverage LLMs, NLP techniques, and regular expressions to extract, clean, and structure data from semi-structured text.
Fine-tune domain-specific LLMs for tasks like entity extraction and complaint record classification, ensuring high-quality data for analysis.
Apply time-series models (statistical, ML and sequence models) for survival analysis and predictive modeling of component and system lifecycles.
Develop workflow automation tools that incorporate human-in-the-loop feedback cycles to improve performance and reliability. These tools will effectively blend automation with expert review, resulting in more robust and adaptable solutions.
Expand data ingestion pipelines to integrate diverse sources such as system log data, CRM, customer complaint and field service data to automate and streamline troubleshooting and failure analysis reporting.
Develop and manage dashboards, visualizations, and analytics to communicate key insights and model results to a range of audiences, from technical staff to senior leadership.
Lead cross-functional investigations and stakeholder feedback loops for continuous model improvement, engaging with Services Product Management, engineering, and clinical teams.
Collaborate with other engineering teams to uphold data governance and accelerate the integration of advanced analytics solutions into products and workflows.
Conduct additional analysis/activities across the product/service lifecycle, as needed, to support product telemetry and service tech evolution and advancement