EXPOSED
The modal data scientist spends most of the week on work AI already does credibly: writing pandas/SQL, cleaning and joining tables, building baseline models, tuning hyperparameters, generating charts, and writing up findings in decks and notebooks. What resists is upstream and downstream — turning a vague business question into a measurable target, deciding whether an observed lift is causal, catching leakage and biased sampling before a model ships, and standing behind a recommendation that moves pricing, credit, or headcount. No license protects the role, and it is fully screen-based, so the shrinkage lands on the analysis-execution tier while the framing-and-accountability tier holds.
Mixed — a routine tier and a judgment tier. At 10, roughly half your week — feature engineering scripts, gradient-boosting baselines, cross-validation loops, matplotlib panels, notebook write-ups — is now competently drafted from a prompt, while defining the label for "churn" in a business with three contract types, spotting that your training set leaked post-outcome fields, or choosing a difference-in-differences design over a naive A/B split still needs you at the whiteboard; it sits below 14 because those framing hours are a minority of logged time, and above 6 because a model that ships without them fails in production.
Fully desk- and screen-based. A 2 reflects that everything from data pull to stakeholder deck happens on a laptop against cloud compute, with the only physical residue being whiteboard sessions and in-person readouts that videoconference replaces without loss.
No licence, no signature requirement. At 2 there is no board, no exam, no continuing-education requirement — a self-taught bootcamp graduate and a PhD hold the same title, and when a scoring model produces disparate impact under ECOA or the FCRA, the bank's compliance officers and counsel answer for it while your name appears nowhere on a filing.
Some relationship component. A 9 recognizes that the product manager who has watched you flag three bad metrics will accept your "that lift is seasonality" without a deck, and that institutional knowledge of which internal tables are trustworthy is genuinely personal — but the artifacts you ship are dashboards and models that keep working after you leave, so the relationship accelerates the work rather than being the work.
Exists to be accountable for ambiguous calls. 14 is earned by the calls with no correct procedure: whether a 0.7% AUC gain justifies a model that is unexplainable to a regulator, whether to tell leadership their favored experiment was underpowered from the start, whether known selection bias in the training population is tolerable for a pricing or credit-limit decision — you own these before anyone else can audit them, though it stops short of 17+ because a director or model-risk committee typically signs the deployment.
The Seattle Times reported that Amazon cut jobs within its artificial intelligence team.
Has AI actually changed your work?