Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Director, Decision Science
- Remote from
- USA
- Salary
- USD 180k–200k / yr
- Department
- Data Science & Analytics
- Employment
- Full Time
- Experience
- Director
- Published
- Apply before
- 5 Dec 2026
- Listing views
- 27
- Application actions
- 3
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About this role.
At Instructure, we believe in the power of people to grow and succeed throughout their lives. Our goal is to amplify that power by creating intuitive products that simplify learning and personal development, facilitate meaningful relationships, and inspire people to go further in their education and careers.
We do this by giving smart, creative, passionate people opportunities to create awesome. And that’s where you come in:
The role.
We’re hiring a Director of Decision Science to build and lead the team that turns Instructure’s data into better decisions. You’ll own the decision science function: the people, the roadmap, and the standards. Your team will run experiments, build causal and predictive models, and answer the questions that shape our product, pricing, go-to-market, and operating plans.
This is a people-leadership role. You’ll hire, coach, and grow a team of decision scientists, and you’ll set the bar for analytical rigor across the company. You’ll still be close enough to the work to review a model, challenge a test design, or dig into a dataset yourself. But your main job is making your team’s work count, and making sure leaders act on it.
You’ll sit at the table with product, engineering, marketing, finance, and the executive team. When a decision is expensive, ambiguous, or high-stakes, you’re the person who tells us what the data supports and what it doesn’t.
What you’ll do.
Build and lead the decision science team. Hire, onboard, coach, and develop a group of scientists and analysts, and own their career growth.
Set the function’s strategy and roadmap. Decide which problems the team takes on, which it declines, and how success gets measured.
Partner with product, engineering, marketing, finance, and executive leadership to turn fuzzy business questions into clear analytical problems, then deliver answers people can act on.
Direct the development of predictive, prescriptive, and causal models that inform product decisions, customer retention, pricing, and operating efficiency.
Raise the bar on how insights reach decision-makers, from dashboards and metric definitions to written recommendations and executive readouts.
Present findings and recommendations to senior leaders. Say clearly what we know, what we don’t, and what you’d do about it.
Grow data literacy across Instructure so teams ask sharper questions and read results correctly on their own.
Work with data engineering and platform teams to make sure the team has the data, tooling, and infrastructure it needs.
Hold the line on ethical, privacy-conscious data use, including student data. Follow applicable regulations and our own standards, and make sure your team does too.
What you’ll bring.
10 or more years of experience in data science, analytics, or decision science, including at least three years managing and developing a team.
A track record of analytical work that changed real decisions and produced measurable business results.
Deep expertise in experimental design, causal inference, statistical modeling, and machine learning, and the judgment to know which one a problem calls for.
Strong SQL skills, plus fluency in Python or R.
Experience with modern data platforms and large-scale data tools, such as Snowflake, Databricks, or Spark.
The ability to explain a complicated analysis to an executive in plain language, and to defend the method to a peer who knows the math.
A record of hiring well, developing people, and building a team culture others want to join.
Comfort setting priorities in a fast-moving environment where the questions change and the data is imperfect.
A master’s or PhD in statistics, economics, computer science, operations research, applied mathematics, or a related quantitative field, or equivalent practical experience.
Nice to have.
Experience in education technology or another B2B SaaS business.
Familiarity with subscription and product-led growth metrics, such as retention, expansion, and adoption.
Experience building an analytics or decision science function from an early stage.
Get in on all the awesome at Instructure!
We offer competitive, meaningful benefits in every country where we operate. While they vary by location, here’s a general idea of what you can expect:
Competitive compensation, plus all full-time employees participate in our ownership program – because everyone should have a stake in our success.
Flexible work culture. Our remote, hybrid and in-office collaboration spaces vary by role, team and location.
Generous time off, including local holidays and our annual “Dim the Lights” period in late December, when teams are encouraged to step back and recharge based on departmental needs.
Comprehensive wellness programs and mental health support
Learning and development resources, including professional development tools and tuition reimbursement, to support your growth
The technology and tools you need to do your best work
Motivosity employee recognition program
A culture rooted in inclusivity, support, and meaningful connection
We believe in hiring great people and treating them right. The more diverse we are, the better our ideas and outcomes.
Instructure is an Equal Opportunity Employer. We comply with applicable employment and anti-discrimination laws in every country where we operate.
All employees must pass a background check as part of the hiring process. To help protect our teams and systems, we’ve implemented identity verification measures. Candidates may be asked to verify their legal name, current physical location, and provide a valid contact number and residential address, in accordance with local data privacy laws.
Any attempt to misrepresent personal or professional information will result in disqualification.
This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.
Role DNA
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Sample interview questions
I would begin with the decision to be made, the intervention's controllability, the outcome horizon, and the cost of being wrong. When randomization is feasible and the goal is to estimate causal impact, I would prioritize a well-powered experiment; when it is not feasible, I would use an observational design with explicit causal assumptions and sensitivity checks. Predictive models are appropriate when the primary goal is forecasting or prioritization rather than estimating the effect of an intervention.
I would inventory high-value decisions across product, pricing, retention, go-to-market, and operations, then rank them by expected business impact, decision urgency, data readiness, and analytical effort. I would establish a balanced portfolio of quick decisions, foundational measurement work, and longer-term strategic initiatives. The roadmap would include clear owners, success metrics, assumptions, and a regular process for reprioritizing as business conditions change.
I lead with the decision, recommendation, expected impact, and uncertainty rather than methodology. I use plain-language visuals and define the practical trade-offs, while maintaining an accessible technical appendix for stakeholders who need to examine assumptions or methods. I also align with decision-makers early on the question and success criteria so the final analysis directly addresses their choices.
I would establish documented access controls, data minimization standards, purpose limitations, retention practices, and review processes for sensitive analyses. The team would use reproducible workflows, clearly document assumptions and model limitations, and involve legal, security, and privacy partners early when work affects student data. I would also train the organization to distinguish technically possible analyses from analyses that are appropriate and compliant.
I would define clear role expectations and career paths, pair regular coaching with challenging business ownership, and create review practices that improve technical rigor without slowing learning. Team members would receive opportunities to present directly to stakeholders and develop both analytical and influence skills. I would also protect focus time, recognize high-quality work, and hire for complementary technical and domain strengths.
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