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.
