Prioritize competing engineering requests

Scenario

You are a Data Scientist/Analytics partner supporting multiple engineering teams. Two (or more) teams simultaneously ask you to prioritize their analytics/experimentation requests, and the requests conflict in timeline and scope.

Question

1. How do you decide which request to prioritize?
2. What information do you gather (from PM/Eng/Stakeholders) before committing?
3. How do you communicate tradeoffs, timelines, and a final decision back to the teams?
4. What do you do if a senior leader pressures you to override your prioritization?

Solution

What a strong answer demonstrates

• You prioritize by business impact, urgency, risk, and effort, not by who asks loudest.
• You create a transparent intake + prioritization framework and socialize it.
• You manage stakeholders proactively and de-risk delivery.

Step-by-step approach

1) Clarify each request (don’t accept vague asks)

For each team, quickly standardize:

• Goal: what decision will this analysis/experiment enable?
• Success metric: what metric moves, by how much, by when?
• Time sensitivity: hard deadlines (launch, exec review), or “nice to have”.
• Effort & dependencies: data availability, instrumentation needs, eng work, approvals.
• Risk: potential revenue/user harm, legal/privacy risk, correctness risk.

2) Use a scoring rubric (lightweight, repeatable)

Example rubric (score 1–5):

• Impact (revenue, retention, advertiser outcomes)
• Confidence/clarity (is the problem well-defined?)
• Urgency (launch gating vs exploratory)
• Effort (lower effort = higher score)
• Risk reduction (does it prevent major harm?)

You can turn this into a simple priority score like:

$\text{Priority} = (Impact + Urgency + RiskReduction + Clarity) - Effort$

3) Offer options, not just “yes/no”

When two requests compete, propose:

• Option A: Do request 1 fully now; request 2 next sprint.
• Option B: Do a thin-slice for request 2 (minimum viable analysis) now + deeper follow-up.
• Option C: Parallelize by shifting work: ask for eng instrumentation first, while you analyze existing data.

4) Make tradeoffs explicit (and document)

Communicate:

• What you will deliver (scope)
• By when
• What you are not doing (de-scoped items)
• Risks/assumptions

A short written doc (1 pager) prevents re-litigation later.

5) Handle escalation / senior pressure

• Ask for the decision criteria: “If we override, which goal are we optimizing—revenue this quarter, launch readiness, or platform risk?”
• Provide the opportunity cost: “If we do X, Y slips by 2 weeks; expected impact is …”
• If leadership insists, align, execute, and record the decision + rationale.

Common pitfalls

• Agreeing before clarifying success metrics.
• Prioritizing by relationship rather than impact.
• Not de-risking via instrumentation checks and data quality.
• Not setting expectations on iteration vs one-shot perfection.