Lead a small data science team to extract metadata from PDF/HTML/Excel using GenAI. Manage model improvement lifecycle, deploy ML models to production, perform error analysis, scope experiments, break down tasks, estimate effort, and update stakeholders on progress and roadmap.
Job Summary
Use Case:
Extraction of information (metadata) from structured / unstructured documents which are in PDF, HTML, Excel, etc. formats using GenAI capabilities (Gemini, Â GPT, etc.)
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Must have:
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Responsibilities
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Use Case:
Extraction of information (metadata) from structured / unstructured documents which are in PDF, HTML, Excel, etc. formats using GenAI capabilities (Gemini, Â GPT, etc.)
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Must have:
- Extensive experience applying data science methods to solve real world problems with a proven track record of deploying AI models and solutions to production
- Proficiency in Object Oriented programming in Python with expertise in key data science and machine learning libraries (e.g. TensorFlow, PyTorch and Scikit Learn).Â
- Proven ability to establish and uphold coding standards to ensure high quality, maintainable code.
- Excellent communication skills for summarizing complex technical concepts clearly and effectively to both technical and non technical partners.Â
- Expertise in cloud technologies (emphasis on Azure and/or AWS) and deployment practices, ensuring seamless and scalable delivery.
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Responsibilities
- Being able to lead a team of 3 6 data scientists
- Plan and manage the model improvement lifecycle enhance the model, review model performance results, perform error analysis, identify opportunities to improve, scope the effort (experimentations, fixes, etc.) and repeat the process iteratively until the success criteria for the use case is met.
- Break down complex problems into manageable tasks, provide estimate on required effort, etc.
- Manage stakeholders with periodic update on progress, aspirational delivery roadmap, etc.
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