Product Manager

About NxtWave

NxtWave is a swiftly expanding ed-tech startup in India striving to create the next million software engineers. The company specializes in transforming youth into adept tech professionals regardless of their educational background through its innovative CCBP 4.0 programs. Supported by notable investors and government partnerships, NxtWave leverages vernacular content and interactive learning to enhance comprehension and retention among learners.

Role Overview

The Product Manager for NxtWave Academy will be responsible for the end-to-end ownership of the learning product journey, guiding students from enrollment to course completion. This role demands active management of retention and engagement metrics and involves hands-on experimentation and AI-driven feature development to improve learner activation, weekly active learning, progression, and reduce dropout rates.

Key Responsibilities

• Take accountability for the learner’s in-product journey covering activation, active learning, progression, and completion stages.
• Define and maintain the learning product’s key performance indicators including activation, weekly active learning (WAL), progression velocity, feature adoption, and drop-off points.
• Conduct data-driven analysis of learner drop-offs and implement fixes by instrumenting events and creating dashboards.
• Develop AI-powered features such as adaptive practice, doubt-solving, and personalized recommendations by collaborating with engineering, design, and data teams.
• Create clear product requirement documents and maintain the product backlog with clarity and priorities.
• Run hypothesis-driven experiments across the learner journey, evaluate outcomes impartially, and decide on continuation or termination of initiatives.
• Prioritize product roadmap under constraints, defending trade-offs when necessary.
• Coordinate alignment across cross-functional teams including engineering, design, data, curriculum, and retention leadership to maintain a unified roadmap.
• Present evidence-backed recommendations to leadership, including advisories against certain builds.
• Ensure learner privacy, data security, and ethical AI practices, overseeing pre-launch quality assurance and safe feature rollouts.

Success Metrics

• Achieve agreed-upon quarterly improvements in new learner activation rates through end-to-end ownership from hypothesis to iteration.
• Reduce in-journey drop-off rates at critical points by targeted percentages within two quarters.
• Increase weekly active learner rates and course progression velocities using product interventions like in-app nudges, streaks, and personalization.
• Deliver statistically valid product experiments quarterly with documented hypotheses, results, and decisions.
• Maintain ownership over the learning product’s metric tree with success metrics defined prior to engineering efforts.
• Launch AI-first learning features that demonstrably enhance learner progression or engagement within targeted groups.

Qualifications and Experience

• Proven experience managing a product end-to-end and impacting activation, engagement, retention, or progression metrics with measurable results.
• Experience conducting real experiments including hypothesis setting, design, result analysis, and decision-making about product iteration.
• Proficient in funnel and cohort analysis using SQL or product analytics tools such as Amplitude or Mixpanel.
• Ability to navigate ambiguity, manage multi-stakeholder feedback, and prioritize a constrained product roadmap effectively.

Preferred Qualifications

• Background in EdTech, learning science, gamification, or subscription growth for expedited understanding of learner journey mechanics.
• Experience with building or briefing on personalization, recommendation, or adaptive learning systems.
• Experience developing products from scratch in environments with minimal structure and processes.

Work Location and Schedule

• Position based in Hyderabad, India.
• Workweek is Monday through Friday (5 days).

Compensation

Offered compensation includes a fixed salary, annual performance-based variable pay, and a retention component. Final salary structure depends on level and experience.

Eligibility

Open to any graduate meeting the role’s requirements.