Enterprise Data Governance Specialist
5 days ago
We are Concentric AI, a venture-backed company dedicated to addressing unmet data security needs. Our Data Security Posture Management (DSPM) and data security governance solution uses deep learning to accurately categorize structured and unstructured data, assess risk, and remediate security issues – without relying on upfront rules or complex configuration.
Our culture is built on Trust, Humility, and Learning, guiding our interactions with colleagues, partners, and customers. We strive to deliver exceptional value to our clients through innovative solutions and expert support.
The role you will be joining is part of the Metrics, Insights, and Data (MIND) team, which drives all data-related efforts at the company. As a key member of this team, you will:
- Analyze customer data on the Concentric AI platform to extract relevant insights and identify data risks.
- Develop, curate, and maintain training datasets for AI models.
- Explore, annotate, and label large datasets for supervised learning of ML models.
- Work closely with AI and software development teams to drive automation.
- Serve as a data subject matter expert and partner with Customer Success to drive adoption of the Concentric AI platform.
Your Key Responsibilities
In this role, you will analyze customer data to extract relevant insights and identify data risks. You will also develop, curate, and maintain training datasets for AI models, explore, annotate, and label large datasets for supervised learning of ML models, and work closely with AI and software development teams to drive automation. Additionally, you will serve as a data subject matter expert and partner with Customer Success to drive adoption of the Concentric AI platform.
Your Requirements
To succeed in this role, you will need strong experience and expertise in data analytics, including proven ability to extract relevant insights from large volumes of data to solve specific problems. You will also need a keen eye for detecting trends, anomalies, and patterns in data. Experience developing training datasets, data annotation, and labeling for supervised learning of AI/ML models is essential. Knowledge of Python, scripting, visualization, or analytics tools is a plus, as well as experience with data governance in a large enterprise setting and data catalog tools like Collibra. A broad knowledge of data security, regulations, and standards is an advantage.
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