In which Jill tests “the sexiest job of the 21st century,” and finds herself alone. Oh, so alone.

It’s late fall in L.A. The Santa Ana winds have given way to crisp, sunny days and nights by the fire. Palm trees rustle with the suggestion of January’s arrival and the coming rains. It’s the last hour of light and I wait for my data scientist. And I wait.


My data scientist is running late. “I’m over halfway done documenting the semantic layer for the geospatial data,” he explains.

“But I’m already running the bath,” I protest.

“See you in a few hours,” he says hastily.

The ice in the Negroni will melt long before then, I want to tell him. The bath, the fondue, and I will all be cold. But it’s too late. He’s hung up.

Flash forward a few weeks. My data scientist and I are at a big data conference in San Francisco. I’m looking forward to a romantic dinner at La Folie, a Pisco Sour, and some classic rock. Maybe we’ll end up at that Tiki bar on Noriega Street. But my data scientist has other ideas.

“So do you really think R is still fringe?” he asks. I reapply my lipstick but he persists. “Do you think it can transcend the statistical community and stand on its own as a …”

I interrupt him. “When am I going to meet your parents?”

“It’s complicated,” he says perfunctorily, but at this point I’m not sure what he’s referring to.

We fly home. The data scientist works on the plane, muttering something about adaptive filters. It’s now twelve-hour days at the office and every conversation starts and ends with his job. The data scientist wants me to recommend a tool to capture business rules. He wants to run through optimal data validation techniques. He quizzes me on when unstructured data needs to be integrated with structured data. How to calculate the ROI of data federation. The merits of probabilistic versus deterministic matching. Whether I think data as a service has legs.

“I have legs. And they’re tanned. Here…take a look…”

But there’s no use. The data scientist is preoccupied. Preoccupied with finding, accessing, analyzing, validating, cleansing, integrating, provisioning, modeling, verifying, and explaining data to his management, colleagues, end-users, and friends. And to me. I’ve had enough.

I’ll give him an ultimatum tonight. It’s either me or the data, I’ll say. But he’s late again. His text message says only two words: “Stochastic processing.”

I pour Sambuca into a snifter and drop in two coffee beans. I watch them float in the glass. I fish one out and put it in my mouth. I bite down. Then I wait.