About this role.
Instructure is hiring a full-time Decision Scientist to strengthen data-driven product and business decision-making in an EdTech environment. The role conducts analyses, A/B tests, and causal studies related to product adoption, customer engagement, pricing, go-to-market activity, and revenue growth. It requires 2–4 years of analytics or data science experience, strong SQL and Python or R skills, statistical modeling knowledge, BI experience, and familiarity with platforms such as Snowflake, Databricks, and Fivetran. The scientist will own analytical projects from problem framing through insight delivery and outcome tracking while partnering closely with product, engineering, and marketing teams. Clear storytelling for technical and non-technical audiences and effective work in a fast-paced cross-functional setting are central to success.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
4/5Pace & Pressure
4/5Autonomy Level
4/5Communication Load
5/5Salary analysis
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Core skills
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Cover letter sample
Dear Hiring Team,
I am excited to apply for the Decision Scientist role at Instructure. My background in SQL, Python, experimentation, and statistical modeling enables me to turn complex product and business questions into clear, actionable recommendations.
I would bring a structured, end-to-end approach to analytical work, from defining success metrics and evaluating causal impact to communicating findings with product, engineering, and marketing partners. I am particularly drawn to Instructure’s mission and the opportunity to use data and AI-enabled tools to improve learning experiences and business outcomes.
Thank you for your consideration. I would welcome the opportunity to discuss how my analytical and cross-functional collaboration skills can contribute to Instructure’s growing decision science capability.
Sample interview questions
I would start by clarifying the decision, target population, primary success metric, guardrail metrics, and expected effect size. I would randomize at the appropriate unit, conduct a power analysis, validate instrumentation before launch, and analyze results using intention-to-treat principles while checking for sample-ratio mismatch and heterogeneous effects.
I would first assess whether randomization is feasible. If not, I would define a credible comparison group and use methods such as difference-in-differences, matching, regression discontinuity, or synthetic control depending on the intervention and data-generating process; I would then test assumptions and communicate uncertainty clearly.
I would translate the request into a measurable business question, define the relevant entities and time windows, inspect data quality, and build a reproducible SQL dataset. In Python or R, I would conduct exploratory analysis, model or test the hypothesis, validate robustness, and present recommendations alongside limitations and expected impact.
I would lead with the decision and recommendation, then show only the few metrics or visuals needed to establish confidence. I would avoid unnecessary statistical jargon, explain practical effect size and trade-offs, and provide a concise appendix or follow-up material for stakeholders who need methodological detail.
I would use SQL to create trusted, well-documented source tables and Python or R for analysis, experimentation workflows, and visualization. I would leverage platforms such as Snowflake or Databricks for scalable data access, BI tools for stakeholder-facing monitoring, and AI tools for productivity while validating all outputs, logic, and sensitive-data handling.
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 Decision Scientist at Instructure will contribute to how data informs strategic decisions across the organization. This is a foundational role within a growing decision science capability, offering the opportunity to apply analytical rigor to meaningful business problems in an EdTech environment.
Impact of the Role
As a Decision Scientist, you will support data-driven decision-making across Instructure by delivering high-quality analyses and partnering with cross-functional teams.
In this role, you will:
Support business impact through data-driven insights. Conduct analyses that uncover opportunities to improve product adoption, customer engagement, and revenue growth using rigorous analytical methods.
Execute experimentation and causal analysis. Support the design and execution of A/B tests and causal inference studies to evaluate the impact of new product features, pricing strategies, and go-to-market initiatives.
Contribute to product and business strategy. Partner with product managers, engineers, and marketing teams to translate business questions into structured analytical frameworks and deliver clear, actionable findings.
Deliver analytical solutions end-to-end. Own the lifecycle of analytical projects — from scoping problems and building models to presenting insights and tracking outcomes.
Apply AI-first thinking. Leverage AI tooling (e.g., Claude, ChatGPT, Glean) to accelerate analysis, improve throughput, and elevate the quality of deliverables.
Communicate insights clearly. Translate analytical findings into recommendations that both technical and non-technical stakeholders can understand and act on.
What You Will Need
Master’s degree in Statistics, Computer Science, Economics, Applied Mathematics, Operations Research, or a related quantitative field (PhD is a plus).
2–4 years of experience in data science, analytics, or a related discipline with demonstrated ability to deliver actionable insights.
Strong understanding of statistical modeling, machine learning fundamentals, and experimental design (A/B testing).
Familiarity with AI tooling (Claude, ChatGPT, Glean) to improve productivity.
Strong proficiency in SQL for data extraction and manipulation.
Proficiency in Python or R for data analysis, modeling, and visualization.
Proficiency in a Business intelligence tool (Tableau, PowerBI… etc.)
Experience working with modern data platforms (e.g., Snowflake, Databricks, Fivetran).
Strong communication and storytelling skills with the ability to present complex analyses clearly.
Experience in SaaS, product analytics, or EdTech environments is a plus.
Ability to work effectively in a fast-paced, cross-functional environment.
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.
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