Senior Data Curator

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Requirements:

• Proficiency in Python (mid-level experience), with the ability to work in libraries like TensorFlow or PyTorch.
• Strong SQL skills (mid-level), capable of querying and manipulating data efficiently for analysis and annotation purposes.
• Experience with cloud platforms, especially AWS or Azure, and a familiarity with cloud computing best practices
• Knowledge of version control systems such as Git, ensuring proper management of code and data.
• A solid understanding of machine learning algorithms (supervised and unsupervised learning) and data analysis techniques, with a focus on improving model performance through data quality optimization.
• Experience in data annotation and extraction: Familiarity with data annotation processes and an understanding of how data is labeled for machine learning use cases.
• A strong interest in machine learning and a desire to transition into a more advanced data scientist role.
• Ability to communicate effectively with both technical and non-technical stakeholders, providing clear insights from data and ensuring the alignment of models with business objectives.
• A commitment to data quality: An understanding of best practices in data curation, quality assurance, and validation.

Responsibilities:

• Curating, annotating, and preparing high-quality data to feed into machine learning models, ensuring data is accurate, clean, and aligned with the company’s standards.
• Extracting and manipulating large datasets from various sources to support machine learning efforts and contribute to data model creation.
• Interfacing with machine learning models: You’ll not be responsible for creating new models but will adapt and modify existing models as needed to enhance performance.
• Ensuring data quality: Implement data validation techniques to assess data integrity and ensure the quality of annotations and datasets used in training.
• Using version control systems like Git to track changes and maintain efficient workflows within the data team.
• Applying statistical methods and machine learning algorithms (such as supervised and unsupervised learning techniques) to optimize data models.
• Collaborating cross-functionally with data scientists to help interpret and utilize data in machine learning projects.
• Continuing to enhance your skills in machine learning and data science, with a strong focus on using Encord (a data annotation tool) for optimizing data processing.