2.1 MTC — the Massive Tech Company
2.1.1 Your team: one of many in MTC
2.1.2 The tech: advanced, but siloed across the company
2.1.3 The pros and cons of MTC
2.2 HandbagLOVE — the established retailer
2.2.1 Your team: a small group struggling to grow
2.2.2 Your tech: a legacy stack that’s starting to change
2.2.3 The pros and cons of HandbagLOVE
2.3 Seg-Metra — the early-stage startup
2.3.1 Your team—what team?
2.3.2 The tech: cutting edge technology that is taped-together
2.3.3 Pros and cons of Seg-Metra
2.4 Videory — the late-stage, successful tech start-up
2.4.1 The team: specialized but still room to move around
2.4.2 The tech: trying to not get bogged down by legacy code
2.4.3 The pros and cons of Videory
2.5 Global Aerospace Dynamics (GAD) — the massive government contractor
2.5.1 The team—a data scientist in a sea of engineers
2.5.2 The tech: old, hardened, and on security lockdown
2.5.3 The pros and cons of GAD
2.6 Putting it all together
2.7 Interview with Randy Au on different types of companies
2.7.1 What was your path to become a data scientist?
2.7.2 Are there big differences between large and small companies?
2.7.3 When you got to Google after working at start-ups, were you surprised by the differences?
2.7.4 Are there differences based on the industry of the company?
2.7.5 Should new data scientists be wary of startups since they’ll do tons of cleaning, pipeline creation, and unglamorous work?
2.7.6 What’s your final piece of advice for aspiring and junior data scientists?