I was interviewing for the Enclosures Team for both iPhone and Apple Watch, and the Battery Team.

A recruiter reached out, and I had a 30-minute phone screening. This was followed by two technical phone screens with two MQE managers, each lasting 30 minutes.

Next, I had to do a presentation on two different topics:

• A data analysis presentation. Prepare statistics thoroughly for this, including general concepts like SPC, process capability studies, Gage R&R, ANOVA, and normality tests.
• A presentation where I had to dimension a part and apply GD&T to it.

This was followed by a 10-panel interview comprising MQE managers, Design Engineering managers, and Directors. Expect this to be very technical.

Questions covered:

DOE and Statistical Methods:

• Difference between OFAT and fractional factorial DOE processes (situation-based).
• When to use each method.
• Explanation of a conducted DOE in detail, including the thought process.
• Which has more bias: repetition or replication?
• Example of response bias in a study.
• Example of a correlation study conducted.
• How to validate a DOE result (explained with ANOVA, regression model coefficients, R², R²(adj) difference, VIF).
• How to conduct Gage R&R.
• NDC in Gage R&R.
• Meaning of NDC = 1.
• If NDC < 5, is the measurement system bad?
• What does NDC < 5 mean for sample size?
• Cp/Cpk.
• When to use Cpm.
• Choosing a process for a deep dive based on Cp/Cpk values.
• When to use Pp/Ppk vs. Cp/Cpk.
• Can Cp be negative? Why not?

Process and Measurement:

• Experience with reliability testing.
• Experience with surface finish.
• How to measure and quantify surface roughness.
• Cosmetic characteristics experience with a part.
• When to use a t-test.
• Chi-square test questions.
• Questions on datum selection.
• Usage of positional/profile tolerances.
• What are LMC and MMC modifiers?
• Calculating boundary conditions for hole and shaft mating.
• Usage of I-MR charts.
• When to use X-bar and X-bar S charts.
• How to segregate data from a process to root cause common cause vs. assignable cause.

Projects:

• Asked about a machine availability improvement project.
• A project where cycle time for a machine was improved.
• A tolerance stack-up project to come up with spec limits for a process.
• A DOE project: explain the process, parameters taken, why, how parameter values were decided, every nitty-gritty detail.
• A vision project using a 3D profilometer: how the program was optimized, what defects were looked for.
• An X-ray algorithm improvement project: explanation of failure modes, issues with the algorithm, how specifications were changed, impact on yield, how the idea was communicated to the design team.
• Question on part traceability from a project.
• Experience with CMM/stylus probes.
• Experience with Helium leak testing.
• Questions on electrolyte filling process development for battery cells.

Data Engineering:

• Since I had experience with data engineering, a ton of questions were asked on how parameters were set up.
• How optional/non-optional were categorized.
• How constraints were added for NCs.
• Curiosity on how SQL scripts were made for process techs for root causing.
• Asked about experience with Tableau dashboards.