Ankita Puri

About Ankita Puri

STRUCTURED THINKING • CHEMICAL INTUITION • DATA-DRIVEN INSIGHT Ph.D. Chemist and Intel Process Engineer with a passion for turning complexity into clarity. I bring a balance of hands-on experimentation, computational modeling, and data-driven systems thinking. My approach combines curiosity and persistence with clear communication and mentorship, helping teams and projects move forward with confidence. I specialize in bridging experimentation, spectroscopy, and data systems to create clarity and lasting solutions. What I bring: • Foundation & Safety Mindset: Careful attention to reliable practices (SOPs, GMP, cleanroom protocols) to create environments where science can be trusted. • Effort & Persistence: Hands-on experimentation with determination (DOE, reproducibility studies, redox/surface reactivity, spectroscopy) to keep refining until results are clear and reliable. • Connection & Communication: Clear documentation, knowledge transfer, and teamwork that ensure ideas are understood and shared effectively across groups. • Clarity & Problem-Solving: Turning complexity into structured systems (Root Cause Analysis, Python/JMP automation, DFT modeling, process optimization) that make work faster, cleaner, and easier to build on. • Contribution & Growth: A focus on mentorship, collaboration, and shared success - supporting peers and building stronger teams for long-term impact. In both academia and industry, I’ve improved the reliability of experiments, built systems that turned raw data into clear insights, and uncovered root causes of complex problems. These experiences taught me that my strength lies in making science practical, reliable, and impactful. Excited to contribute in research, development, or process roles where science meets real-world needs. a••••••••@gmail.com

Experience

1. Process Development Engineer

   Intel

   Jun 2025 — Present · Hillsboro, OR, US

   Increased thin-film reliability by ~20% by planning and executing DOE-based PECVD experiments, identifying critical process variables, and optimising deposition conditions.• Cut troubleshooting time by 30%(~15 hours/week) by analyzing large process datasets with Python and JMP, translating raw tool outputs into clear statistical trends for faster root-cause identification.• Improved compliance and experimental reliability by developing and refining 10+ SOPs that met cleanroom safety standards and process specifications.

Education

• Southern Methodist University

  Doctor of Philosophy - PhD, Chemistry

• Maharani's Science College for Women - India

  Bachelor of Science - BS, Chemistry

  2010 — 2014

• The IIS University, Jaipur

  Master of Science - MS, Chemistry

  2014 — 2016

• University of Wisconsin-Madison

  Non-Degree Grad Courses, Chemistry

• Carnegie Mellon University

  Doctor of Philosophy - PhD, Chemistry