Principal Data Scientist

Cornerstoneprovides an AI-powered Talent Experience Platform for unified content discovery,knowledge management, and personalized learning platform for your careerjourney. Our award-winning Platform is used globally by Fortune 500 companiesand government organizations to solve content discovery, curation, andrecommendation problems across external, internal, and tacit knowledge sources

Inthe Data Science and Machine Leaning team at Cornerstone we are looking forsolid hands-on technologists with solid time-management skills and experiencein highly autonomous roles. You also function effectively in a collaborative environmentand are comfortable making independent decisions.

In this role, you will...

• Lead and drive execution of new initiatives for Machine Learning using conversational NLP techniques and introduce ML-based learning for recommendation and coaching assistant in our products
• Lead team of ML engineers in evaluating ML technologies, building/training models, deployment of models and monitoring in production environments.
• Work with the engineering teams and ensure timely deliveries.
• Be part of a global Engineering team supporting Fortune 1000 customers worldwide.
• Ability to experiment and iterate rapidly and provide tangible improvements in the overall engagement
• A driven team player, collaborator, and relationship builder whose infectious can-do attitude inspires others and encourages excellent performance in a fast-moving environment.
• Results orientated, motivated by success.

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• Master's or bachelor's degree in computer science or a related study or equivalent experience
• 7+ years of hands-on experience architecting and designing highly scalable and resilient systems.
• 5+ years of experience in designing & scaling applications based on Data Science, NLP, and conversational AI
• Understanding of ML algorithms - classical and deep learning and ML frameworks
• Good understanding of system availability, security, and performance management.
• PyTorch and its ecosystem of libraries
• Word and document embeddings
• Transformers and Attention
• RNN, LSTM