Christopher Bauer

Background

About Christopher Bauer

Summary and Highlights:•Site SME for validations and project documentation relating to single use consumable medical devices.•Sector lead with GE Research (GRC) and principal member of task force doubling yield and decreasing cost per unit by 50% with >100% increase in chamber life.•Increased capability sector wide (GRC) and drafted a sector upgrade plan.•Started continuous improvement meetings and led sector conversion to GHS at IBM.•Operations lead for experiments with safety risks that required coordination across nine groups of stakeholders with zero adverse incidents.•Decreased startup time by 70% for MOCVD process, increasing revenue by $2-$4MM per year.•Participant in, or designer of, training and QMS improvement programs in all posts.•Use of SQL and Access to query data to analyze in JMP, resulting in 3% yield improvement by improved control of a major defect mode.•Lean Six Sigma Green Belt, passing scores on actuarial exams 1/P and 2/FM•Proficient in Access, JMP and Minitab, learning R, Python and Jupyter Notebooks. • I am an NPI development engineer for single use consumable medical devices and Lean Six Sigma Black Belt with demonstrated results in yield improvement, cycle time reduction and continuous improvement projects and have a passion for statistical analysis and modeling. I have direct experience in multiple analytical techniques, including AFM, SEM, XRD (powder and film), magnetometry, FTIR, PL/EL, TXRF, particle measurements and have a background knowledge of chromatography, NMR and electrophoresis. I am experienced in multiple semiconductor processes spanning deposition (MOCVD of materials, PVD of metals and PECVD), etch (both RIE and wet etch with 100mm, 150mm and 300mm) and characterization (AFM, XRD, TXRF, SEM, XRR) of silicon and silicon carbide technologies. I have led sectors and projects involving multiple stakeholders and vendors and love to fix inefficiencies in operations. Currently, I am learning python with an aim toward proficiency in machine learning and statistical modelling.