5 Shocking Truths January Reorgs Blindside Data Scientists (And Nobody Warns You)

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5 Brutal Truths Data Scientists Learn During January Reorgs:

1. The previous organizational chart should no longer be used for prediction. The modelling does not take into account all of the previous leaders' experiences. Solution: All models will have accompanying Assumptions Cards. Cards that lack adequate information about the model cannot be implemented.

2. Trust resets post reorganization. The new executives will not value the AUC from the previous quarter.
   Solution: Each metric being monitored in the business should be signed off on by the business and an ML expert.

3. Decisions made through models today are not (in most cases) valid anymore. The company's strategic direction has changed, and the model still reflects how the company used to do things.
   Solution: Post-organization, the Seller should run a formal Decision Fit Check to assess how the model responds to the company's new decisions. If the model does not fit, it should not be updated.

4. The choices made by the data scientists in the models will likely be perceived as politically motivated, given the biases that led to those decisions and the thresholds that were set. The context is with the previous leadership.
   Solution: For each model developed, the Data Scientist should log one explicit trade-off decision in plain English.

5. Ahead of the reality, a promise was made that productivity would improve through new work actions, but the reality has yet to hit. Not because the productivity is wrong, but because the ownership of the actions has changed.
   Solution: New actions must go through a Re-Alignment Review before they can be conducted.

💡Key Takeaway: 

Reorgs don’t break models. It’s what we do next that truly makes a difference. It's about how we adapt to change, how we decide to rebuild, and how we choose to move forward. The human element, our resilience, is what matters most after these kinds of shifts. They show us what was never put into words. It's often said that teams making it past January aren't just about killer metrics... it's more about how clearly they can explain what their models are really for.