The proven scorecard template to hire top startup data talent

Data talent both Individual Contributors (IC) and Managers are vital to the growth of your startup. Establishing your unique brand identity helps you stand out to potential customers and drives demand. So when you’re growing fast, a mis-hire in these roles can flatten your company's trajectory toward the moon. Don’t let that happen with Yobs' Data Interview Scorecard.

What's a Yobs hiring scorecard?

The Yobs Data Scorecard are built around competencies predictive of success in Data. Each kit includes a scorecard with (a) definitions of the competencies, (b) interview questions in the context of Data and if relevant managing a team, and (c) signals to look for in a candidate’s interview answers. 

Some competencies are more important than others when predicting performance, depending on the responsibilities of the job.

The Data Individual Contributor (IC) Scorecard focuses on the competencies that matter the most.


Pays attention to details


Hard working and orderly


Sets and achieves goals

Being Resilient

Overcomes obstacles


Creative problem solver

Download the Startup Data Scorecard

Relevant Job titles

The following job titles are especially relevant to the Data Individual Contributor Scorecard.

The Data Manager Scorecard focuses on the competencies that matter the most.

Builds Effective Teams

Builds teams of trust and cooperation

Drives Results

Consistently gets team to deliver

Being Resilient

Overcomes obstacles


Sets and achieves goals

Financial Acumen

Forecasts financial impact of decisions

Relevant Job titles to Data Manager Scorecard

The following job titles are especially relevant to the Data Manager Scorecard.

Scorecards include a balance of behaviorally- and scenario-based questions. On one hand, past behavior predicts future behavior and future performance. On the other hand, scenario-based questions are more flexible and future-facing to allow candidates to shoot their shot. Taken together, evidence supports a blend of multiple methods to optimize for predicting on-the-job success for Data Individual Contributors.

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