Jack Stehn

• EdTech Analytics Reliability: Specialize in the stability and scalability of the EdTech analytics layer, ensuring high-stakes dashboards and datasets are accurate, reliable, and actionable for school leadership teams.
• Enterprise Customer Delivery: Manage complex data pipelines, automate manual workflows, and deliver presentation-ready insights for enterprise accounts — translating raw data into decision-ready artifacts for non-technical stakeholders.
• Data Architecture Consulting: Co-designing the next-generation data architecture, proposing a transition plan to a Medallion (bronze/silver/gold) infrastructure on BigQuery to improve traceability, testability, costs, and downstream analytics velocity.
• Modern Stack Proposal: Proposed adopting a modern open-source stack — Terraform for IaC, dlt for ingestion, dbt for transformation, Dagster for orchestration, Cube.js for semantic layer — to replace ad-hoc workflows with a maintainable, version-controlled, observable platform.
• Workflow Automation: Automating previously manual customer delivery workflows to reduce turnaround time and free engineering capacity for higher-leverage analytics work.

• Strategic Leadership (Solo Data Lead): Owned the full data lifecycle (DS, DE, ML) as the sole data scientist. Partnered directly with C-suite and department heads to navigate a 'zero-to-one' environment, moving the org from manual spreadsheets to automated warehousing.
• Predictive ML & Risk Modeling: Developed and deployed explainable ML models (Logistic Regression, Deep Learning) to predict staff turnover. Engineered a 'Risk Tolerance' configuration allowing non-technical leadership to adjust precision/recall thresholds based on quarterly hiring capacity.
• Modern Data Stack Architecture: Architected and built a scalable platform on Google Cloud Platform (GCP). Orchestrated ELT pipelines using Dagster, dbt, and dlt to ingest data from disparate SIS (School Information Systems) and HR platforms.
• Engineering Maturity (ROI): Engineered a comprehensive People Team data pipeline, reducing manual consistency checks from months of collective annual work to seconds. Built a custom automated data quality framework to catch inconsistencies between systems.
• API Integration & Interoperability: Solved complex data interoperability challenges by designing integrations between Schoolmint and PowerSchool. Reverse-engineered undocumented APIs to create a unified data model for cross-functional analysis.
• Data Democratization: Updated the organization's data security policy and designed data literacy training modules, empowering school leaders to access real-time attendance and academic metrics without technical bottlenecks.
• Survey Design & Enrichment: Leveraged data to enrich staff satisfaction surveys with demographic, work location, role, grade level, and tenure data, enabling highly segmented and actionable insights.
• Stakeholder Management: Partnered with leadership on high-impact, data-driven solutions; presented findings to stakeholders including the Board of Directors.

• Business Impact: Contributed to product enhancements that achieved 33% faster ramp times, 16% higher revenue, and 15x ROI for customers.
• Production ML (Revenue): Developed and deployed production ML models for Propensity Scoring (deal closure probability) and Churn Modeling. Leveraged NLP on unstructured email metadata to identify sales signals.
• Pipeline Architecture (AWS): Led a critical overhaul of the AWS data infrastructure (S3, Athena, EMR). Implemented 'SQL Push-down' strategies and asynchronous DAGs, reducing data processing latency by 75% and scaling to multi-terabyte datasets.
• Scalable Data Modeling: Architected scalable Star Schema data models and optimized ETL/ELT processes, ensuring data readiness for LLM integration.
• Engineering Best Practices: Championed the adoption of CI/CD pipelines (GitHub Actions), unit testing (pytest), and Agile methodologies within the data science team.
• Causal Analysis: Performed deep causal inference studies to isolate specific sales behaviors that drive outcomes, influencing the product roadmap to focus on 'high-leverage' user actions.
• Technical Consulting: Acted as a technical consultant for enterprise customers, diagnosing complex data discrepancies and proposing architectural solutions for data integration.
• Cross-Functional Leadership: Collaborated seamlessly with Engineering, Product, and Support teams to spec out large-scale infrastructure restructuring.

• Leadership: Led data science components for mixed-methods studies on equity and public health (specifically violence against homeless youth). Managed a team of undergraduates.
• Unstructured Data: Analyzed diverse unstructured and non-traditional datasets (qualitative interviews, geospatial data, text corpora, hand-drawn maps) requiring the development of novel data processing approaches.
• Geospatial Analysis: Performed geospatial analysis (ArcGIS) to identify and visualize spatial violence patterns for non-technical stakeholders.
• Visualization: Created interactive dashboards (Tableau, Plotly) to communicate findings to stakeholders.
• Interdisciplinary Collaboration: Collaborated across disciplines (public health, psychology, sociology) ensuring ethical, robust research.

• Full Stack Development: Independently designed and developed a web application (Python, JavaScript, PHP, PostgreSQL) to guide student program selection.
• Data Acquisition: Built a database of 500+ university programs via web scraping (Beautiful Soup) and rigorous data cleaning.
• Stakeholder Consulting: Consulted with college stakeholders (district, counselors) to define user needs.
• UX/UI: Created an intuitive management dashboard for non-technical staff.

Selected for national fellowship applying leadership/management skills to advance educational equity via capacity-building projects and leadership development.

• Leadership Development: Applying data science & leadership skills to advance educational equity.
• Capacity Building: Building organizational capacity through strategic data projects at placement site.
• Cohort Engagement: Engaging in rigorous leadership development programming with diverse cohort.

Provide data-driven insights for policy-making and organizational growth through survey creation and analysis (qualitative & quantitative).

• Team Leadership: Led volunteer team providing data analysis for organizational strategy.
• Survey Analysis: Designed & analyzed surveys (qual/quant) informing policy & growth.
• Executive Presentation: Presented data-driven insights to chorus leadership.

• Event Production: Co-produced large-scale (~10k attendees) LGBTQ+ community street fair, overseeing all aspects from planning through execution.
• Logistics Management: Managed complex logistics, ~100 vendor relations, and multi-stage entertainment programming.
• Fundraising: Led fundraising efforts, securing over $90k in sponsorships & donations, contributing to event profitability.
• Stakeholder Coordination: Coordinated hundreds of diverse stakeholders including volunteers, performers, operations teams, city agencies, and non-profits.

Support members through mentorship and leadership within trans support initiatives, coordinating meetings and events.

• Peer Mentorship: Provided mentorship and peer support to chorus members.
• Community Leadership: Led coordination for trans member support group meetings and events.
• Inclusive Culture: Contributed to fostering an inclusive environment within the organization.

• Student Mentorship: Mentored incoming transfer students transitioning into UC Berkeley Data Science.
• Academic Guidance: Assisted students in developing data science skills & navigating coursework.
• Community Building: Fostered community and peer networking during remote learning (pandemic).

Supported transfer/career programs through data analysis, marketing, peer training, and event coordination.

• Data Analysis: Analyzed student transfer data (SQL, R, Excel) to inform program development.
• Marketing Leadership: Led marketing committee managing social media, web content, and outreach.
• Public Speaking: Presented transfer/career information via public speaking & workshops.
• Peer Training: Trained new student employees on department policies & procedures.
• Event Coordination: Organized large campus events coordinating multiple stakeholders.

• Pipeline Reliability: Own the stability and scalability of production data pipelines feeding high-stakes EdTech dashboards, ensuring datasets remain accurate and auditable for enterprise school district customers.
• Medallion Architecture Design: Co-designing a transition to a Medallion (bronze/silver/gold) data architecture on BigQuery, defining layer contracts, freshness SLAs, and lineage to improve traceability and downstream analytics velocity.
• Modern Stack Advocacy: Proposed adopting Terraform (IaC), dlt (ingestion), dbt (transformation), Dagster (orchestration), Cube.js (semantic layer) to replace ad-hoc workflows — enabling version control, automated testing, and unified observability across the platform.
• Workflow Automation: Replacing manual customer delivery workflows with automated, idempotent pipelines, reducing turnaround time and eliminating recurring sources of data drift between systems.
• Cross-System Integration: Manage complex data flows across disparate EdTech sources, designing for schema evolution, late-arriving data, and the operational realities of multi-tenant customer delivery.

• Modern Data Stack Architecture: Architected and built a scalable platform on GCP. Orchestrated ELT pipelines using Dagster, dbt, and dlt to ingest data from disparate SIS (School Information Systems) and HR platforms into BigQuery.
• Engineering Maturity (ROI): Engineered a comprehensive People Team data pipeline, reducing manual consistency checks from months of collective annual work to seconds. Built a custom automated data quality framework to catch inconsistencies between systems.
• API Integration & Interoperability: Solved complex data interoperability challenges by designing integrations between Schoolmint and PowerSchool. Reverse-engineered undocumented APIs to create a unified data model for cross-functional analysis.
• Solo Data Lead: Owned the full data lifecycle as the sole data professional, partnering directly with C-suite and department heads to move the org from manual spreadsheets to automated warehousing.
• Data Democratization: Updated the organization's data security policy and designed data literacy training modules, empowering school leaders to access real-time metrics without technical bottlenecks.

• Pipeline Architecture (AWS): Led a critical overhaul of the AWS data infrastructure (S3, Athena, EMR). Implemented 'SQL Push-down' strategies and asynchronous DAGs, reducing data processing latency by 75% and scaling to multi-terabyte datasets.
• Scalable Data Modeling: Architected scalable Star Schema data models and optimized ETL/ELT processes, ensuring data readiness for ML and LLM integration.
• Engineering Best Practices: Championed the adoption of CI/CD pipelines (GitHub Actions), unit testing (pytest), and Agile methodologies within the data science team.
• Business Impact: Contributed to infrastructure enhancements that achieved 33% faster ramp times, 16% higher revenue, and 15x ROI for customers.
• Cross-Functional Leadership: Collaborated with Engineering, Product, and Support teams to spec out large-scale infrastructure restructuring.

Selected for national fellowship applying leadership/management skills to advance educational equity via capacity-building projects.

• Capacity Building: Building organizational capacity through strategic data engineering projects at placement site.

• Analytics Layer Stewardship: Own the correctness and interpretability of the EdTech analytics layer, ensuring metrics, segmentation, and dashboards remain trustworthy for school leadership decision-making.
• Stakeholder-Ready Insights: Deliver presentation-ready analyses for enterprise accounts, translating raw operational data into segmented, decision-driving narratives for non-technical school leaders.
• Customer-Facing Analysis: Diagnose data quality and metric discrepancies for enterprise customers, applying statistical reasoning and domain knowledge to surface root causes rather than just symptoms.
• Analytics Architecture Input: Consulted using data engineering experience on the company's data architecture roadmap, planning for a Medallion structure on BigQuery to give analytics workflows reproducible, well-tested foundations.
• Workflow Automation: Automating recurring analytics workflows so the team can spend more time on high-leverage exploratory analysis and less on manual report production.

• Predictive ML & Risk Modeling: Developed and deployed explainable ML models (Logistic Regression, Deep Learning) to predict staff turnover. Engineered a 'Risk Tolerance' configuration allowing non-technical leadership to adjust precision/recall thresholds based on quarterly hiring capacity.
• Strategic Leadership (Solo Data Lead): Owned the full data lifecycle (DS, DE, ML) as the sole data scientist. Partnered directly with C-suite and department heads.
• Survey Design & Enrichment: Leveraged data to enrich staff satisfaction surveys with demographic, work location, role, grade level, and tenure data, enabling highly segmented and actionable insights.
• Data Democratization: Designed data literacy training modules, empowering school leaders to access real-time attendance and academic metrics without technical bottlenecks.
• Stakeholder Management: Partnered with leadership on high-impact, data-driven solutions; presented findings to stakeholders including the Board of Directors.

• Production ML (Revenue): Developed and deployed production ML models for Propensity Scoring (deal closure probability) and Churn Modeling. Leveraged NLP on unstructured email metadata to identify sales signals.
• Business Impact: Contributed to product enhancements that achieved 33% faster ramp times, 16% higher revenue, and 15x ROI for customers.
• Causal Analysis: Performed deep causal inference studies to isolate specific sales behaviors that drive outcomes, influencing the product roadmap to focus on 'high-leverage' user actions.
• Scalable Data Modeling: Architected scalable Star Schema data models and optimized ETL/ELT processes, ensuring data readiness for LLM integration.
• Technical Consulting: Acted as a technical consultant for enterprise customers, diagnosing complex data discrepancies and proposing architectural solutions.

• Leadership: Led data science components for mixed-methods studies on equity and public health (specifically violence against homeless youth). Managed a team of undergraduates.
• Unstructured Data: Analyzed diverse unstructured and non-traditional datasets (qualitative interviews, geospatial data, text corpora, hand-drawn maps) requiring the development of novel data processing approaches.
• Geospatial Analysis: Performed geospatial analysis (ArcGIS) to identify and visualize spatial violence patterns for non-technical stakeholders.
• Visualization: Created interactive dashboards (Tableau, Plotly) to communicate findings to stakeholders.
• Interdisciplinary Collaboration: Collaborated across disciplines (public health, psychology, sociology) ensuring ethical, robust research.

Selected for national fellowship applying data science & leadership skills to advance educational equity.

• Leadership Development: Applying data science & leadership skills to advance educational equity.

Provide data-driven insights for policy-making and organizational growth through survey creation and analysis.

• Team Leadership: Led volunteer team providing data analysis for organizational strategy.
• Survey Analysis: Designed & analyzed surveys (qual/quant) informing policy & growth.

• Production Data Reliability: Own the stability and scalability of production analytics infrastructure on BigQuery — the same data foundation layer that underpins ML feature pipelines and model training datasets.
• Modern Data/ML Stack Advocacy: Proposed adopting dlt, dbt, and Dagster to bring CI/CD, testing, and orchestration discipline to data workflows — directly transferable to MLOps practice (feature pipelines, model retraining DAGs, evaluation jobs).
• Architecture Design: Co-designing a Medallion (bronze/silver/gold) architecture on BigQuery to establish the lineage, testability, and reproducibility prerequisites for trustworthy downstream ML and analytics workloads.
• Automation & Observability: Replacing manual workflows with automated, observable pipelines — applying the same engineering rigor required for reliable model serving and continuous training systems.

• Predictive ML & Risk Modeling: Developed and deployed explainable ML models (Logistic Regression, Deep Learning) to predict staff turnover. Engineered a 'Risk Tolerance' configuration allowing non-technical leadership to adjust precision/recall thresholds based on quarterly hiring capacity.
• Modern Data Stack Architecture: Architected and built a scalable ML platform on GCP. Orchestrated ELT pipelines using Dagster, dbt, and dlt to ensure clean, reliable feature data for model training.
• Engineering Maturity (ROI): Built a custom automated data quality framework to catch inconsistencies between systems, reducing manual consistency checks from months of annual work to seconds.
• Strategic Leadership (Solo Data Lead): Owned the full data lifecycle (DS, DE, ML) as the sole data professional, partnering directly with C-suite to identify high-leverage ML applications.

• Production ML (Revenue): Developed and deployed production ML models for Propensity Scoring (deal closure probability) and Churn Modeling. Leveraged NLP on unstructured email metadata to identify sales signals.
• Business Impact: Contributed to ML-driven product enhancements that achieved 33% faster ramp times, 16% higher revenue, and 15x ROI for customers.
• Pipeline Architecture (AWS): Led a critical overhaul of the AWS data infrastructure (S3, Athena, EMR). Implemented 'SQL Push-down' strategies and asynchronous DAGs, reducing data processing latency by 75% and scaling to multi-terabyte datasets.
• Scalable Data Modeling: Architected scalable Star Schema data models and optimized ETL/ELT processes, ensuring data readiness for LLM integration.
• Engineering Best Practices: Championed the adoption of CI/CD pipelines (GitHub Actions), unit testing (pytest), and Agile methodologies within the data science team.
• Causal Analysis: Performed deep causal inference studies to isolate specific sales behaviors that drive outcomes, influencing the product roadmap.

• Leadership: Led data science components for mixed-methods studies on equity and public health. Managed a team of undergraduates.
• Unstructured Data: Analyzed diverse unstructured and non-traditional datasets (qualitative interviews, geospatial data, text corpora) requiring novel data processing approaches.

Selected for national fellowship applying ML & leadership skills to advance educational equity.

• Leadership Development: Applying ML engineering & leadership skills to advance educational equity.

• System Reliability: Own the stability and scalability of production data systems serving enterprise customers, treating analytics infrastructure as a software product with uptime, correctness, and performance requirements.
• Architecture Consulting: Co-designing the next-generation system architecture, proposing a Medallion (bronze/silver/gold) layered design on BigQuery with clear interface contracts and separation of concerns between ingestion, transformation, and serving.
• Modern Toolchain Advocacy: Proposed migrating to a version-controlled, test-driven stack (Terraform, dlt, dbt, Dagster, Cube.js) to replace ad-hoc scripts — bringing CI/CD, code review, and observability practices to a domain that often lacks them.
• Workflow Automation: Designed and built automated workflows that replaced manual, error-prone delivery processes — reducing turnaround time and eliminating recurring operational toil.
• Cross-System Integration: Engineer integrations across disparate enterprise systems, accounting for schema evolution, failure modes, and the realities of multi-tenant production environments.

• Platform Architecture: Architected and built a scalable data platform on GCP, orchestrating automated pipelines using Dagster, dbt, and dlt with comprehensive test coverage.
• API Integration & Interoperability: Solved complex system interoperability challenges by designing integrations between Schoolmint and PowerSchool. Reverse-engineered undocumented APIs to create a unified data model.
• Engineering Maturity (ROI): Built a custom automated data quality framework, reducing manual consistency checks from months of annual work to seconds.
• Data Democratization: Updated the organization's data security policy and designed training modules, empowering non-technical staff to access real-time metrics.

• Pipeline Architecture (AWS): Led a critical overhaul of the AWS data infrastructure (S3, Athena, EMR). Implemented 'SQL Push-down' strategies and asynchronous DAGs, reducing data processing latency by 75% and scaling to multi-terabyte datasets.
• Engineering Best Practices: Championed the adoption of CI/CD pipelines (GitHub Actions), unit testing (pytest), and Agile methodologies within the data science team.
• Scalable Data Modeling: Architected scalable Star Schema data models and optimized ETL/ELT processes, ensuring data readiness for LLM integration.
• Business Impact: Contributed to product enhancements that achieved 33% faster ramp times, 16% higher revenue, and 15x ROI for customers.
• Cross-Functional Leadership: Collaborated with Engineering, Product, and Support teams to spec out large-scale infrastructure restructuring.
• Technical Consulting: Acted as a technical consultant for enterprise customers, diagnosing complex data discrepancies and proposing architectural solutions.

• Full Stack Development: Independently designed and developed a web application (Python, JavaScript, PHP, PostgreSQL) to guide student program selection.
• Data Acquisition: Built a database of 500+ university programs via web scraping (Beautiful Soup) and rigorous data cleaning.
• UX/UI: Created an intuitive management dashboard for non-technical staff.
• Stakeholder Consulting: Consulted with college stakeholders (district, counselors) to define user needs.

Selected for national fellowship applying engineering & leadership skills to advance educational equity.

• Capacity Building: Building organizational capacity through strategic engineering projects at placement site.