Data Scientist

Are you passionate about using data to solve complex business challenges and influence strategic decisions We are seeking a Data Scientist to advance KLAs service cost analytics and forecasting capabilities.

In this role you will develop predictive models and analytical frameworks that help the organization better understand service costs reliability risk and long-term financial impact. Working with Service Engineering Finance and Operations stakeholders you will transform data into actionable insights that improve forecasting accuracy support business decision-making and drive operational effectiveness.

While the primary focus of this role is advanced analytics and modeling you will also leverage modern AI-assisted development tools to rapidly prototype lightweight solutions that enable business teams to operationalize analytical insights.

• Develop predictive models and statistical methodologies to improve service cost forecasting and business planning.
• Analyze part reliability failure behavior service consumption patterns and installed base trends to identify key service cost drivers.
• Apply statistical machine learning reliability and actuarial techniques to quantify uncertainty risk exposure and long-tail service events.
• Design scalable analytical frameworks that enable rapid assessment of reliability and cost impacts across products and technologies.
• Evaluate model performance and continuously improve forecasting accuracy through validation experimentation and refinement.
• Define analytical approaches for solving business problems where historical data may be incomplete sparse or highly variable.
  

• Partner closely with Service Finance Operations and Engineering teams to understand business processes planning workflows and decision-making needs.
• Translate business challenges into analytical solutions and ensure model outputs align with operational objectives.
• Work with stakeholders to identify how predictive insights can be incorporated into forecasting planning cost management and operational decision-making processes.
• Present findings assumptions recommendations and business impacts to both technical and non-technical audiences.
  

• Build lightweight prototypes analytical tools and proof-of-concept solutions using AI-assisted development techniques to accelerate insight generation and stakeholder adoption.
• Develop simple decision-support tools that make analytical models accessible and actionable for business users.
• Identify opportunities to improve data availability business processes and analytical infrastructure to support future modeling capabilities.

• Masters or PhD in Data Science Statistics Mathematics Computer Science Engineering Actuarial Science Economics or a related field.
• Experience with statistical modeling machine learning predictive analytics or forecasting.
• Strong programming skills in Python and SQL.
• Experience working with large and complex datasets to solve business problems.
• Familiarity with reliability engineering survival analysis warranty forecasting actuarial modeling or risk analytics is highly desirable.
• Experience rapidly prototyping analytical solutions using modern AI-assisted development tools.
• Strong communication and stakeholder management skills with the ability to connect analytical outputs to business outcomes.
  

This role directly influences how KLA forecasts service costs evaluates business risk and supports critical operational decisions. Success is measured by the ability to develop accurate scalable analytical models and ensure those insights are effectively adopted within business processes to improve decision quality and operational performance.