Product Analyst

Nanonets has a vision to help computers see the world starting with reading and understanding documents. Machine Learning (ML) is no longer a futuristic concept—it's a present-day powerhouse transforming the business landscape. Nanonets is at the forefront of this transformation, offering innovative ML solutions designed to make document-related processes faster than ever before.

From automating data extraction processes to enhancing reconciliation, our solutions are designed to revolutionize workflows, optimize operations, and unlock untapped potential for our clients. Our client footprint spans across brands such as Toyota, Boston Scientific, Bill.com and Entergy to name a few enabling businesses across a myriad of industries to unlock the potential of their visual and textual data.

We recently announced a series B round of $29 million in funding by Accel and are backed by the likes of existing investors including Elevation Capital and YCombinator. This infusion of capital underscores our commitment to driving innovation and expanding our reach in delivering cutting-edge AI solutions to businesses worldwide.

https://www.forbes.com/sites/davidprosser/2024/03/12/why-enterprises-are-learning-to-love-nanonets-automation/?sh=6d79ec8f3ca1

https://techcrunch.com/2024/03/12/nanonets-funding-accel-india/amp/

We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity.

We are seeking a Product Analyst to take ownership of key metrics, reporting, and experimentation within Nanonets. Working directly with our founders, this role involves goal setting, tracking, and prioritizing features based on metrics-driven insights. As a Product Analyst, you’ll be critical to helping teams set data-informed goals, clarifying the key metrics that drive success, and enabling a hypothesis-driven approach to product development.

• →Metrics Ownership: Manage and report on company metrics, setting measurable goals and tracking progress against them.
• →Outcome Definition: Help teams define data-backed goals and establish measurable outcomes, ensuring efforts align with strategic company objectives.
• →Input Metrics Clarity: Identify and communicate the key levers that drive output metrics, providing teams with actionable insights.
• →Hypothesis-Driven Development: Build systems that support hypothesis testing through product features, creating, running, and measuring A/B tests and experiments to validate customer behavior hypotheses.
• →Product Development Optimization: Partner with product teams to establish high-quality goals, focus on the most impactful metrics, and support agile development based on data-driven decision-making.