Candidate Profile:

- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or A related field is preferred

- 13-15 years of experience in technology, product management, or related roles

- Strong working understanding of software development, databases, and system architecture

- Experience managing external vendors or outsourced development teams

- Exposure to data-driven systems and platforms from Edtech/Education environments ONLY

Key Responsibilities:

1. Lead the identification and prioritization of high-impact technology applications to enhance learning outcomes, teacher effectiveness, and operational efficiency

2. Focus on deploying AI and data science across key workflows, including assessment generation, learning analytics, personalization, and automation.

3. Translate organizational workflows into clear, actionable product and technology requirements.

4. Ensure all technology decisions prioritize long-term scalability, usability, and cost-efficiency, especially considering the paying capacity and resource constraints of target low-resource schools. Evaluate and prioritize technology investments based on impact, feasibility, and scalability.

5. Actively seek strategic partnerships with leading technology players (Google, OpenAI, Microsoft), academic institutions, and government bodies to co-develop relevant solutions. Secure grants and funding for innovations tailored to low-resource settings, such as low-cost devices and study aids.

6. Serve as the primary liaison with external technology partners. Define detailed product requirements, workflows, and specifications. Manage timelines, deliverables, and quality for both outsourced and internal development. Ensure internal readiness and seamless integration of platform components - content, assessment, analytics, and workflows.

7. Establish robust data storage, access, and usage protocols while ensuring compliance with privacy and security standards. Protect company IP, maintain data integrity, and enable secure, structured use of data for decision-making and analytics.

8. Own the post-launch lifecycle, including maintenance, bug resolution, feature enhancements, testing, release management, and version control. Drive continuous improvement based on user feedback and evolving organizational needs.

9. Bridge internal teams and external partners, facilitate alignment, and develop necessary documentation and training for smooth adoption.

10. A scalable, operational technology platform aligned with workflows, successful integration of AI in high-impact areas, timely delivery through strong partner coordination, and structured processes for ongoing platform management and iteration.