About Us
Encord is building a comprehensive training data solution for computer vision AI applications. Our mission is to enable companies to unlock the power of AI while retaining 100% control of their data. In 2021, we raised $20M from top investors including CRV, Y Combinator Continuity, top industry executives, and other leading Bay Area investors.
Started by ex-computer scientists, physicists, and quants, we felt first-hand how the lack of tools to prepare quality training data was impeding the progress of building practical AI. AI feels to us like what the early days of computing or the internet must have felt like, where the potential of the technology is clear, but the tools and processes surrounding it are terrible. We have devised a unique methodology for automating the tasks related to preparing quality training data, in effect turning the training data problem into a data science problem.
The Role
This is a unique opportunity for an ambitious, multi-talented individual to help build our Data Operations function and have a massive impact on a high-growth business. As a Data Analyst, you will be working with our customers on building world-leading AI applications across a broad set of verticals including retail, insurance, and healthcare.
You will also work closely alongside the wider Customer Success and Engineering teams as well as the co-founders to shape Encord's direction and help us manage our rapidly expanding customer base. The ideal candidate is a superstar who is willing to work hard, roll up their sleeves and take responsibility for working with innovative companies adopting computer vision technologies. You will be in for a fun ride!
Responsibilities
- Actively participate in and oversee the delivery of high-quality annotations on client data annotation projects.
- Collaborate and communicate effectively within the team and with data annotators to understand the technical requirements of projects ensuring they are completed to a high degree of accuracy and in a timely fashion.
- Conduct analysis using scripts to accurately assess project-based metrics, ensuring alignment with project specifications and data quality standards.
- Ensure compliance with all project specifications and guidelines.
- Manage quality assurance queries through to resolution to ensure smooth operation of the project workflow.
- Work closely with members of the Data Operations function to improve the data annotation and project management process to subsequently iterate on the quality of delivered projects.
About you
- Experience in data annotation and/or quality assurance.
- Technical skills, particularly in scripting languages such as Python, familiarity with relational database management systems (RDBMS) and data annotation tools are desirable.
- Understand how to handle queries and communicate problems and solutions to team members at the appropriate level of detail and complexity.
- Experience of working in a fast-paced environment with varying deadlines.
- You love technology and being able to talk about it.
- Have a growth mindset and be willing to contribute to the development of the Data Operations team.
- Ability to thrive in a fast-paced, team-oriented environment.
What We Offer
- Competitive salary and equity in a fast growing business
- 25 days annual leave a year + public holidays
- Generous annual L&D budget
- Being one of the first employees in an exciting early-stage startup & opportunity to have a real impact on the business
- Fast-paced learning through direct hands-on experience
Encord offers a unique opportunity to be part of a startup with a clear mission and vision. You will get to explore and build services that enterprise AI companies use across many different industry verticals such as healthcare, surveillance, retail, and agriculture.
Our work is at the cutting edge of computer vision and deep learning, which also includes working on solving unsolved problems within those fields. We offer competitive compensation.
Encord's mission is to enable every company to harness the power of AI by alleviating the cumbersome process of making unstructured data readable by machines and make it easy to build computer vision models.
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