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Lead Data Scientist
2 weeks ago
Lead Data Scientist
This role is Hybrid
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.
KeyResult Areas include:
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
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
YouAre:
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.
Self-motivated: You can work with minimal supervision and be able to strategically prioritize multiple tasks proactively.
Whatyou need:
Understanding of ML algorithms - classical and deep learning and ML frameworks Good understanding of system availability, security, and performance management.
In particular, the candidate shouldhave work experience with:
PyTorch and its ecosystem of libraries Word and document embeddings Transformers and Attention RNN, LSTM A background in BERT and its variants transfer-learning practices NLP Libraries: NLTK, Genism , Spacy ML-pipelines – Apache Airflow/ Kubeflow/ RAY OpenAI libraries and Models - LLMs
Besides this, the candidate must possess a matureunderstanding of, and hands-on experience in, the broad field of machinelearning and statistical methods. Must be conversant with:
Factorization Machine, Deep recommender
Experiencewith any of the following is considered a plus:
Tensorflow Big-data and PySpark Data wrangling libraries: Beautiful Soup or Scrapy R libraries Taking models to production Cloud Technologies: GCP/AWS ML Graph Models GNN and NetworkX-
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