Application Guide

How to Apply for Director, Decision Science

at Workiva

🏢 About Workiva

Workiva is a unique SaaS company focused on integrated ESG reporting, helping organizations achieve transparent climate impact measurement and compliance. They operate at the intersection of technology, data, and sustainability, making them particularly appealing for professionals who want their work to have meaningful environmental and social impact beyond typical business metrics.

About This Role

As Director of Decision Science at Workiva, you'll define and own the multi-year Data Science and BI strategy while managing a team that builds predictive models and AI-powered insights. This role is impactful because you'll directly enable ESG reporting transparency and compliance through data products that support go-to-market, customer success, and product strategy in a B2B SaaS environment.

💡 A Day in the Life

A typical day involves strategic meetings with business unit leaders to identify high-impact data product opportunities, mentoring your team of Data Scientists and BI Analysts on current projects, reviewing model deployment progress, and refining the multi-year roadmap to ensure alignment with Workiva's ESG reporting priorities and business outcomes.

🚀 Application Tools

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Interview Prep

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🎯 Who Workiva Is Looking For

  • Has 8+ years in Data Science/Machine Learning with 3+ years leading technical teams in SaaS/B2B environments
  • Holds an advanced degree (MS/PhD) in Computer Science, Statistics, or related quantitative field with hands-on production ML deployment experience
  • Demonstrates experience aligning data science initiatives with enterprise priorities and quantifiable business outcomes
  • Has successfully managed end-to-end predictive model lifecycles from ideation through scalable deployment and optimization

📝 Tips for Applying to Workiva

1

Quantify your experience with specific metrics around ML model deployment success rates and business impact in previous SaaS/B2B roles

2

Highlight any ESG, sustainability, or compliance-related data projects in your background, even if tangential

3

Demonstrate your understanding of Workiva's specific business model by mentioning how data products could enhance their integrated ESG reporting platform

4

Showcase experience with agile delivery processes in data science contexts, not just software development

5

Include specific examples of mentoring data scientists and BI analysts to build high-performing teams

✉️ What to Emphasize in Your Cover Letter

['Your experience defining multi-year data science strategies aligned with enterprise priorities in SaaS environments', "Specific examples of deploying ML models in production that drove business outcomes relevant to Workiva's ESG focus", 'Your approach to building and mentoring high-performing data science teams that balance innovation with operational excellence', 'How your background enables you to act as primary analytic partner to business unit leaders for data product development']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Workiva's specific ESG reporting products and how data science could enhance their climate impact measurement capabilities
  • → The company's recent financial reports and investor presentations to understand their strategic priorities
  • → Competitors in the ESG reporting space and how data/AI differentiates Workiva's offerings
  • → Workiva's company culture and values, particularly around innovation and sustainability

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 How would you approach defining a multi-year Data Science and BI strategy for Workiva's ESG reporting platform?
2 Describe your experience with end-to-end predictive model lifecycle management in production SaaS environments
3 How have you previously aligned data science initiatives with go-to-market or customer success strategies in B2B companies?
4 What metrics would you use to measure the success of your team's data products at Workiva?
5 How would you establish a culture of innovation while ensuring operational excellence in a data science team?
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⚠️ Common Mistakes to Avoid

  • Focusing only on technical ML expertise without demonstrating business partnership experience with non-technical leaders
  • Presenting generic data science experience without tailoring examples to SaaS/B2B or ESG/compliance contexts
  • Failing to show quantifiable outcomes from previous data science leadership roles, especially around team management and business impact

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Workiva!

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