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About the Role
As a Data and Product Operations Lead, you will own data sets end to end, from conception through delivery, treating the data itself as the product. This is far more than delivery work. You will partner across product, research, engineering, and deployment to define goals for each data set, manage the project to a high quality bar, and problem-solve how the data will perform when used to train machine learning models. You will work in a small pod alongside a handful of teammates on each project, touching nearly every part of the business in a tight-knit, low-ego, and highly collaborative environment.
Responsibilities
- Own data sets end to end, from conception through delivery
- Partner with product, research, and deployment teams to define goals for each data set
- Manage projects on the company's data platform, including onboarding speakers and coordinating QA
- Work closely with engineering on bug fixes, noise reduction, and automation
- Hold the quality bar, prioritizing quality over speed and volume
- Problem-solve with researchers and ML engineers on how data will perform in model training
- Occasionally join customer-facing meetings depending on the project
Requirements
- 2+ years of experience in consulting, strategy and operations, or heavy operations roles
- Strong generalist who can work across many functions and adapt as priorities shift
- Comfort grinding through intense work in spurts in an output-focused environment
- Low ego, naturally curious, and humble
- Background in private equity or investment banking, MBB consulting with a strong promotion trajectory, two-sided marketplace or heavy-operations companies, or early-stage startup operations is a plus
Location
- New York, NY
Work Type
- Hybrid
Experience Level
- 2+ years
Salary/Compensations
- $135K-$175K base, plus equity
About the Company
- Our partner is an audio and voice focused human data company that operates more like a research lab than a services shop. Rather than building one-off custom data sets and never seeing them again, they create and own off-the-shelf data sets they can iterate on, guided by in-house research and product leadership. They partner long-term with leading research labs and major technology companies, and the work is deep, intentional, and squarely focused on quality.