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.
This infusion of capital underscores our commitment to driving innovation and expanding our reach in delivering cutting-edge AI solutions to businesses worldwide.
Bachelor’s degree preferable from a Tier 1 college.
Proficiency in SQL (required).
Strong analytical skills and understanding of key product metrics.
Willingness to learn Python and enhance data engineering skills.
Robust product sense, with the ability to interpret and act on data for feature prioritization.
Innovative Culture: Be part of a forward-thinking company at the forefront of AI and ML innovation.
Experience
2-4 years of relevant experience, such as Data Analyst or Product Analyst, ideally within a B2B SaaS company. Technical Skills :
Benefits
Nanonets has a vision to help computers see the world starting with reading and understanding documents.
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’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. Role Overview:
We are seeking a Product Generalist 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 Generalist, 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. Key Responsibilities
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.