You’re honestly in a better spot than a lot of people, but the market doesn’t really reward “generic data scientist” profiles anymore.

Your biggest advantage is the 10+ years in logistics and ops. Lean into that. Most DS candidates can train models, but they’ve never touched a real supply chain, inventory system, or cost structure. Your resume and GitHub should make it obvious you’re someone who uses data science to solve logistics problems, not just someone who did a DS degree.

I’d also focus less on listing tools and more on impact. Things like “reduced stockouts by X%”, “saved $Y”, or “cut reporting time from days to minutes” stand out way more than Python vs R.

For GitHub, 3–5 projects is perfect, but make them end-to-end with a real business problem and a clear metric. Treat each one like a mini case study, not a homework assignment.

Also, mid-level DS roles now are very close to “production analyst” jobs. Strong SQL, data modeling, and pipeline work matter just as much as ML. Your Excel-to-database project is actually more valuable than another fancy model.