About this role.
Instructure is hiring a Senior Decision Scientist to lead complex analytics initiatives that improve product adoption, customer engagement, and revenue growth. The role owns end-to-end work spanning opportunity identification, statistical modeling, causal inference, experimentation, deployment, and impact measurement. It partners closely with product, engineering, marketing, and executive stakeholders, requiring strong strategic communication and influence. Candidates need a quantitative graduate degree or equivalent expertise, 5–7 years of relevant experience, SQL, Python or R, and familiarity with modern data platforms and AI-enabled analytical workflows.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
5/5Pace & Pressure
4/5Autonomy Level
5/5Communication 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 Senior Decision Scientist role at Instructure. My background in statistical modeling, experimentation, causal inference, and product analytics aligns well with the opportunity to drive measurable adoption, engagement, and revenue outcomes.
I am experienced in translating ambiguous business questions into rigorous analytical frameworks, using SQL and Python or R to build, evaluate, and communicate actionable solutions. I would welcome the opportunity to partner with product, engineering, and business leaders while advancing high standards for experimentation and AI-enabled analysis.
Thank you for your consideration. I look forward to discussing how I can help Instructure turn data into clear, strategic decisions.
Sample interview questions
I begin by clarifying the decision, target population, primary success metric, guardrail metrics, expected effect size, and implementation constraints. I then define randomization, power and sample-size requirements, instrumentation checks, and an analysis plan before launch, and communicate the resulting business recommendation with uncertainty and trade-offs clearly stated.
I would first evaluate whether randomization, a natural experiment, difference-in-differences, matching, regression discontinuity, or an instrumental-variable approach is credible for the available data. I would document assumptions, test balance and robustness where possible, quantify uncertainty, and avoid presenting correlation as causal impact.
I would frame the problem around the decision to be made and the expected business value, then identify the minimal rigorous analysis needed to support it. I would use clear visualizations, describe the key driver and confidence level in plain language, and end with a recommendation, expected impact, risks, and next steps.
I would use SQL to validate source data, define cohorts and metrics, and build reproducible transformation logic; Python or R would support modeling, experiment analysis, and visualization. On a modern platform such as Snowflake or Databricks, I would version analytical code, establish data-quality checks, and partner with engineering to deploy and monitor the solution.
I would evaluate AI tools as accelerators for ideation, documentation, code assistance, exploratory analysis, and communication rather than as unverified sources of truth. I would protect sensitive data, validate outputs through peer review and reproducible tests, and measure whether the tooling improves quality, speed, or decision outcomes.
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:
As a Senior Decision Scientist, you will drive meaningful business outcomes by leading complex analytical initiatives and serving as a trusted partner to senior stakeholders.
What you will do:
Drive business impact through data-driven insights. Identify and pursue high-value opportunities to improve product adoption, customer engagement, and revenue growth using advanced analytical and statistical methods.
Lead experimentation and causal analysis. Design and execute rigorous A/B tests and causal inference studies to evaluate the impact of new product features, pricing strategies, and go-to-market initiatives. Champion experimental rigor across teams.
Influence product and business strategy. Partner with product managers, engineers, marketing leaders, and senior stakeholders to translate complex business questions into structured analytical frameworks and deliver strategic recommendations.
Deliver end-to-end analytical solutions. Own the full lifecycle of analytical initiatives — from identifying opportunities and building models to deploying solutions and measuring impact.
Default to AI-first thinking. Design analysis approaches, recommend solutions, and elevate standards and throughput across the team by leveraging AI tooling and modern analytical techniques.
Contribute to decision science best practices. Help define standards for experimentation, modeling, and insight delivery that raise the bar for analytical quality across the organization.
Mentor and elevate peers. Guide data scientists and analysts by demonstrating best practices in methodology, communication, and stakeholder engagement.
Communicate insights clearly and effectively. Translate complex analytical findings into actionable recommendations for both technical and non-technical audiences, including executive leadership.
What you will need to know/have:
Master’s degree or PhD in Statistics, Computer Science, Economics, Applied Mathematics, Operations Research, or a related quantitative field.
5–7 years of experience in data science, analytics, or a related discipline with a track record of delivering high-impact, cross-functional insights.
Expertise in statistical modeling, machine learning, causal inference, and experimental design (A/B testing).
Deep familiarity with AI tooling (Claude, ChatGPT, Glean) to improve throughput and analytical quality.
Strong proficiency in SQL for data extraction and manipulation.
Proficiency in Python or R for data analysis, modeling, and visualization.
Experience working with modern data platforms (e.g., Snowflake, Databricks, Fivetran).
Strong communication and storytelling skills with the ability to translate complex analyses into clear, strategic narratives.
Demonstrated ability to lead end-to-end data science initiatives from problem definition through deployment and measurement.
Experience in SaaS, product analytics, or EdTech environments is a plus.
Ability to operate independently while collaborating 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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