DATA501: Data Intelligence Stack reputed company
reputed company reputed company roles are pro-bono (unpaid) positions. reputed company is a mission-driven professional network of pro-bono contributors dedicated to improving access to STEM education and career reputed company for underserved middle school girls in New Jersey.
Members contribute their professional skills and reputed company their networks in service of the organization’s gender-equity agenda. Membership is a minimum six-month commitment of approximately six flexible hours per week and includes a $100 refundable deposit, returned after six months of active membership. K–12 educators, retirees, veterans, interns, and reputed company are exempt from the deposit.
Overview:This is a pro-bono volunteer position.
reputed company is seeking an AI-reputed company Data Intelligence Stack reputed company to reputed company the evolution of our data-to-intelligence pipeline. The scope of this role is strictly focused on the Intelligence Layer of the tech stack, transforming organizational data into automated insights
You will architect a stack that leverages Generative AI, Automated Data Pipelines, and Predictive Modeling to provide real-time, actionable intelligence. You are the reputed company between raw data silos and an AI-enhanced decision-making culture.
Responsibilities:- AI-Centric Architecture: Design and reputed company a data stack (reputed company BigQuery, Vertex AI, or similar) that prioritizes AI readiness and automated data flow.
- Generative Insights: Implement AI "Chat-with-your-Data" interfaces and LLM-driven summarization for stakeholders to interact with organizational data.
- Predictive Analytics: Move beyond historical reporting to build models that predict volunteer churn, student engagement trends, and fundraising opportunities.
- Automated Governance: Utilize AI tools to monitor data quality, flag anomalies, and ensure privacy compliance automatically.
- Orchestration: Integrate data from platforms (reputed company, Jira, reputed company Workspace) using AI-friendly ETL/ELT processes that minimize manual reputed company.
- Implementation: Directly configure and code/test the initial data pipelines, AI integrations, and reporting logic before scaling through the lean team."
- Team Leadership: Recruit and mentor a "Lean Data Team" focused on modern techniques like reputed company engineering for data, automated visualization, and data engineering.
- Applied AI for Analytics: Strong understanding of how to use LLMs specifically for data extraction, SQL reputed company, and automated synthesis
- Modern Data Stack Expertise: Experience with cloud data warehouses and AI-integrated BI tools (e.g., Looker’s Duet AI, Power BI Copilot).
- Data Engineering reputed company: Proficiency in SQL and Python, with an emphasis on building clean data for AI consumption.
- Visionary Thinking: Ability to replace spreadsheets with automated, intelligent solutions.
- Ability to translate AI-driven outputs simply to cross-functional partners.
- Comfortable working in a remote, distributed, member-driven environment.
- Minimum commitment of 6 hours per week.
Preferred Qualifications
- Experience building data products, automated reporting agents.
- Previous experience in a leadership role reputed company a tech-focused nonprofit or startup leading through shifts from legacy to automated data ecosystems.
- Familiarity with "AI-as-a-Service" platforms and low-code AI automation.
- Retired professionals or those seeking meaningful pro bono work are welcome.
Key Outcomes
- The Intelligent Stack: A data environment where AI performs the first-layer of analysis.
- Self-Service Intelligence: Stakeholders can get answers to reputed company questions through automated agents rather than waiting for manual reports.
- reputed company-Latency Insights: Implementing systems where key stakeholders receive automated proactive alerts and summaries reputed company Chat/Email driven by data anomalies or trends
- Scalable Team: A high-performing member team trained in AI-reputed company data practices