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
Want more jobs like this?
Get Data and Analytics jobs delivered to your inbox every week.
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:
- Master's or bachelor's degree in computer science or a related study or equivalent experience.
- 6+ years of hands-on experience architecting and designing highly scalable and resilient systems.
- 4+ 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.
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:
- scikit-learn
- Pandas
- Numpy
- plotting using matplotlib, seaborn, etc.
- HuggingFace
- Besidethe knowledge of framework or libraries, having experience in building therecommendation systems with following algorithms will be considered as a plus
- Generative Models i.e. VAE & GAN
- Collaborative based filtering/ Context aware/Graph based recommender systems
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