Sr. Data Science Analyst

Company Overview
KLA is a global leader in diversified electronics for the semiconductor manufacturing ecosystem. Virtually every electronic device in the world is produced using our technologies. No laptop, smartphone, wearable device, voice-controlled gadget, flexible screen, VR device or smart car would have made it into your hands without us. KLA invents systems and solutions for the manufacturing of wafers and reticles, integrated circuits, packaging, printed circuit boards and flat panel displays. The innovative ideas and devices that are advancing humanity all begin with inspiration, research and development. KLA focuses more than average on innovation and we invest 15% of sales back into R&D. Our expert teams of physicists, engineers, data scientists and problem-solvers work together with the world's leading technology providers to accelerate the delivery of tomorrow's electronic devices. Life here is exciting and our teams thrive on tackling really hard problems. There is never a dull moment with us.

Job Description/Preferred Qualifications

• Responsibilities:
  
  Continuous improvement of spares demand forecast process: Our business supports a wide range of spare parts demand profiles across the fleet, from lumpy/sporadic to consistent/smooth, presenting a significant challenge in forecasting. Below is what you will be doing to drive improvements to our parts forecasting processes as follows:
  
  1. Supervise our planning system's forecast accuracy, partner with others in the organization to drive forecast improvements accordingly:
  
  • Diagnose forecast accuracy issues and demand anomalies, partner with our planning team on solutions to improve accuracy and/or mitigate risk
  • Identify common threads where forecasting accuracy needs improvement
  • Based on the findings, partner with the integration team to refine the system forecast algorithm as needed
  
  
  2. Build forecast models based on parts life characteristics (applies to parts that have varying failure rates according to the age of the part):
  
  • Perform analysis on part survival data (Kaplan-Meier survival curves and/or similar methods), identify customers/products/etc that behave differently as to be partitioned in forecast analysis
  • Build a regression fit to the survival data using statistical software packages to be fed into forecast simulations
  • Review with the part planner / engineering to settle on a forecast to be entered into the planning system
  
  
  3. Identify the main drivers in our spares delivery performance to be addressed: With over 10,000 transactions per month it can be challenging to understand the what is driving performance, what areas in the fleet to be concerned about, etc. In this role you will perform large scale experimentation building data driven models, using advanced analytics methods to extract value from our data;
  
  • Characterize our demand transaction data to find common threads in performance degradation
  • Build tool sets and processes crafted for the planning organization to perform self-service analysis around root cause of performance degradation
  • Work with the service and planning organization to investigate potential systemic issues that are weighing down performance
  • Build simulation and regression tools to partition performance issues into operational gaps vs abnormal field demand patterns
  
  4. Influence product teams through presentation of data based recommendations
  
  5. Work with IT to automate and release tool sets to support self-service analysis
  
  Minimum Qualifications
  
  • Bachelor's degree of 5 years OR Master's degree of 3 years
  • Strong analytical skills
  • Experience with SQL language
  • Experience with statistical software, scripting languages, and packages (e.g. Python, R, MATLAB, SAS)
  
  Preferred Qualifications:
  
  • Confirmed experience in conducting statistical analysis and building models with sophisticated scripting language such as Python, R, or other analytic tools
  • Considerable experience in solving business problems with sophisticated analytical solutions
  • Database design, SQL a plus
  • Supply chain experience / education a plus
  • Experience writing requirements documents, translating business needs into technical specs
  • Experience with Business Intelligence tools (Power BI, Tableau, Qlik, etc), experience with the Power BI / PowerPivot DAX language is a plus
  • Experience with Snowflake a plus
• Minimum Qualifications
  Minimum Qualifications
  
  Bachelor's degree with +5 years' work experience, OR Master's degree with +3 years' work experience

Minimum Qualifications

Master's Level Degree or work experience of 2 years , Bachelor's Level Degree or work experience of 3 years

COVID-19 Vaccination Requirement: Proof of full COVID-19 vaccination is required where permitted by law. KLA will consider reasonable accommodation as provided by applicable law. Please note that accommodation may not be possible where vaccination is required for an essential function of the position, including for international travel or customer site access.

The company offers a competitive and comprehensive benefits package including but not limited to the following: medical, dental, vision, life, and other voluntary benefits, 401(K) including company matching, employee stock purchase program (ESPP), student debt assistance, tuition reimbursement program, financial planning benefits, employee assistance program (EAP), paid time off and paid company holidays, family care and bonding leave.

KLA is proud to be an Equal Opportunity Employer. We do not discriminate on the basis of race, religion, color, national origin, sex, gender identity, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other status protected by applicable law. We will ensure that qualified individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us at [email protected] to request accommodation.