What I actually Do as a Data Scientist.

It’s always interesting to peek into a data scientist’s calendar.

Is it really just wall-to-wall modeling and number crunching?

Spoiler: not even close.

In reality, about 70% of my day ( and not only mine ) is spent in discussions with cross-functional partners, aligning on priorities, clarifying metrics, and making sure the work we ship actually moves the needle.

When I share my schedule here, I’ll skip most of the “Discuss X” or “Discuss Y” items (trust me, there are many) and focus on the more distinctive, high-impact activities. Think causal inference puzzles, data sleuthing, experiment design… and a little table tennis.

Here’s what my week actually looks like - from Monday morning coffee to Friday afternoon wrap-up.

MONDAY — Strategy + Sizing

8:00 AM — Breakfast + Inbox Scan
Over coffee and avocado toast, review invites draft for the in-person advertiser event. Strategy isn’t just “who to invite” — it’s balancing reach, potential revenue uplift, and relationship-building.

9:00 AM — Invitation Strategy Deep Dive
Segment advertisers by spend potential, engagement, and likelihood to convert post-event. Build a quick simulation to see which maximizes ROI without overfilling the venue.

10:30 AM — Opportunity Sizing
Run vertical-level revenue models for multiple ad categories. Compare projections to historical campaign lift to validate the numbers.

12:00 PM — Lunch with Colleagues
Swap notes on last quarter’s campaign results and — more importantly — where our next offsite will be.

1:00 PM — Data Engineering Huddle
Explain to DE why we need a new metric in the vertical datatable. Clarify definitions to avoid “two teams, two different metrics” syndrome.

3:00 PM — PM Discussion on Uplift Results
Share last quarter’s experiment outcomes. Brainstorm how to fold the uplift data into next quarter’s planning, adjusting spend and targeting based on measured impact.

4:30 PM — Campaign Analysis Across Regions
Evaluate marketing Threads campaigns worldwide. Identify which creative angles work best per market — and which just drain budget.

TUESDAY — Data & Causal Inference Lab

8:00 AM — Coffee + Prep
List open questions for the day: “Why does PSM + regression adjustment disagree with DiD?” tops the list.

9:00 AM — Causal Method Comparison
Run side-by-side results for PSM+RegAdj vs. DiD. Diagnose differences: sample imbalance? Parallel trends violated? Different covariate coverage?