Senior Data Scientist II – Core & Agentic Automation

This is a fully remote position, open to applicants in Canada, +1 more country.

📋 Description

• Establish primary data science objectives across various teams and determine which inquiries and investments to pursue or reject.

• Assist in the implementation of the essential company KPI framework, encompassing its driver tree and sensitivity modeling.

• Direct the creation of reusable analytical frameworks and automation efforts across the organization.

• Design, construct, assess, and sustain agentic analytical workflows utilizing certified metrics to examine KPI fluctuations and produce evidence-based hypotheses.

• Implement monitoring systems, manage failures, and facilitate human review when necessary.

• Create reusable frameworks that include evaluation and evidence traceability.

• Establish definitions and targets for certified metrics and resolve conflicting definitions in collaboration with partners.

• Develop KPI trees and decompositions linking product and business drivers to MAU, retention, ARPU, revenue, and margin.

• Choose defensible models and quantify relationships between KPIs and North Star metrics.

• Validate leading indicators outside of sample data and differentiate between arithmetic identities, predictive associations, and causal effects.

• Construct scenarios and defensible targets for teams.

• Set measurement standards and justify identification assumptions.

• Select observational techniques and utilize diagnostics and robustness checks.

• Evaluate accuracy, adoption rates, reduced bespoke analysis, and decision-making timelines.

• Develop reproducible and tested analytical workflows in SQL and Python.

• Collaborate with Data Engineering on gold tables and data marts.

• Influence leadership strategy and team accountability, ensuring alignment between Finance and Product on targets and trade-offs.

• Work with Product, Finance, Commercial, and Engineering to foster adoption.

• Clearly communicate assumptions and uncertainties.

• Mentor scientists and establish reusable standards for KPI, measurement, and modeling across teams.

⛳️ Requirements

• Over 8 years of experience in data science, machine learning, or applied analytics.

• Proven ability to manage complex analytical challenges in product-oriented environments.

• Capability to articulate the prioritization of questions and investments and their implications for planning or investment decisions.

• Proficient in defining KPIs and hierarchies with multiple stakeholders.

• Skilled in structuring complex scenarios into manageable plans.

• Experience in establishing trusted metric definitions across Product, Finance, Commercial, and Engineering teams.

• A history of aligning Finance and Product on targets, trade-offs, and evidence-based adjustments.

• Proficient in selecting and validating statistical, predictive, and causal models, including regression, forecasting, sensitivity analysis, and uncertainty estimation.

• Ability to utilize models for target setting and scenario planning while distinguishing between prediction and causal impact.

• Experience in setting measurement standards and defending identification assumptions when randomization or causal evidence is limited.

• Knowledge of experimentation sufficient to support product data scientists.

• Strong skills in SQL, Python, statistics, causal inference, and forecasting.

• Proven track record in delivering reusable dashboards, diagnostic applications, or analytical frameworks with verified accuracy, adoption, and decision-making impact.

• Experience in collaborating on the productionization of robust SQL/Python assets.

• History of mentoring scientists and setting KPI, measurement, or modeling standards.

• Hands-on experience in automating systems with AI, including personally creating and deploying agentic automation used in analytical or business workflows.

• Ability to explain system contributions, data retrieval, tool usage, and task completion.

• Experience in assessing agentic systems for numerical and analytical accuracy, traceability, failure management, and adoption.

• Preferred experience in consumer product analytics connecting acquisition, activation, engagement, retention, and monetization.

• Preferred experience in owning KPI frameworks, causal measurement, or business planning across multiple teams.

• Experience in centralized or platform data science is preferred.

• Preferred experience with governed self-service analytics.

• Must have appropriate work authorization for the United States or Canada.

🏝️ Benefits

• Equity for full-time employees.

• 401(k) with dollar-for-dollar matching up to 4% of eligible pay.

• Comprehensive medical, dental, and vision plans, including coverage for pets.

• Up to ten thousand dollars per year in education reimbursement.

• Participation in Employee Resource Groups and Inclusion Council.

• Flexible paid time off (PTO).

• Nine paid holidays (holidays may vary for roles based in Canada).

• A week-long break at the end of the year.

• Fourteen weeks of fully paid parental leave.

• Six additional weeks of paid childbirth recovery leave for birthing parents.

• Flexible return-to-work schedule.

• One-time $2,000 Calvin Care Cash incentive for employees welcoming new family members.

• Flexible work environment.

• Hardware and software provided for remote work.