About the Company: Cognizant is one of the world's leading professional services companies, transforming clients' business, operating, and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build, and run more innovative and efficient businesses. Headquartered in the U.S., Cognizant (a member of the NASDAQ-100 and one of Forbes World’s Best Employers 2024) is consistently listed among the most admired companies in the world.

About the Role: Role: AIML Data Scientist (AIA Kolkata)Exp: 3 to 12 yearsLocation: KolkataJob Description:1. Be a hands on problem solver with consultative approach, who can apply Machine Learning & Deep Learning algorithms to solve business challengesa. Use the knowledge of wide variety of AI/ML techniques and algorithms to find what combinations of these techniques can best solve the problemb. Improve Model accuracy to deliver greater business impactc. Estimate business impact due to deployment of model2. Work with the domain/customer teams to understand business context , data dictionaries and apply relevant Deep Learning solution for the given business challenge3. Working with tools and scripts for sufficiently pre-processing the data & feature engineering for model development – Python / R / SQL / Cloud data pipelines4. Design , develop & deploy Deep learning models using Tensorflow / Pytorch 5. Experience in using Deep learning models with text, speech, image and video dataa. Design & Develop NLP models for Text Classification, Custom Entity Recognition, Relationship extraction, Text Summarization, Topic Modeling, Reasoning over Knowledge Graphs, Semantic Search using NLP tools like Spacy and opensource Tensorflow, Pytorch, etcb. Design and develop Image recognition & video analysis models using Deep learning algorithms and open source tools like OpenCVc. Knowledge of State of the art Deep learning algorithms6. Optimize and tune Deep Learnings model for best possible accuracy7. Use visualization tools/modules to be able to explore and analyze outcomes & for Model validation eg: using Power BI / Tableau8. Work with application teams, in deploying models on cloud as a service or on-prema. Deployment of models in Test / Control framework for trackingb. Build CI/CD pipelines for ML model deployment9. Integrating AI&ML models with other applications using REST APIs and other connector technologies10. Constantly upskill and update with the latest techniques and best practices. Write white papers and create demonstrable assets to summarize the AIML work and its impact.· Technology/Subject Matter Expertise· Sufficient expertise in machine learning, mathematical and statistical sciences· Use of versioning & Collaborative tools like Git / Github· Good understanding of landscape of AI solutions – cloud, GPU based compute, data security and privacy, API gateways, microservices based architecture, big data ingestion, storage and processing, CUDA Programming· Develop prototype level ideas into a solution that can scale to industrial grade strength· Ability to quantify & estimate the impact of ML models· · Softskills ProfileCuriosity to think in fresh and unique ways with the intent of breaking new ground.Must have the ability to share, explain and “sell” their thoughts, processes, ideas and opinions, even outside their own span of controlAbility to think ahead, and anticipate the needs for solving the problem will be important· Ability to communicate key messages effectively, and articulate strong opinions in large forums· Desirable Experience:· Keen contributor to open source communities, and communities like Kaggle· Ability to process Huge amount of Data using Pyspark/Hadoop· Development & Application of Reinforcement Learning· Knowledge of Optimization/Genetic Algorithms· Operationalizing Deep learning model for a customer and understanding nuances of scaling such models in real scenarios· Optimize and tune deep learning model for best possible accuracy· Understanding of stream data processing, RPA, edge computing, AR/VR etc· Appreciation of digital ethics, data privacy will be important· Experience of working with AI & Cognitive services platforms like Azure ML, IBM Watson, AWS Sagemaker, Google Cloud will all be a big plus· Experience in platforms like Data robot, Cognitive scale, H2O.AI etc will all be a big plus

Key Responsibilities:

1. Be a hands on problem solver with consultative approach, who can apply Machine Learning & Deep Learning algorithms to solve business challengesa. Use the knowledge of wide variety of AI/ML techniques and algorithms to find what combinations of these techniques can best solve the problemb. Improve Model accuracy to deliver greater business impactc. Estimate business impact due to deployment of model2. Work with the domain/customer teams to understand business context , data dictionaries and apply relevant Deep Learning solution for the given business challenge3. Working with tools and scripts for sufficiently pre-processing the data & feature engineering for model development – Python / R / SQL / Cloud data pipelines4. Design , develop & deploy Deep learning models using Tensorflow / Pytorch 5. Experience in using Deep learning models with text, speech, image and video dataa. Design & Develop NLP models for Text Classification, Custom Entity Recognition, Relationship extraction, Text Summarization, Topic Modeling, Reasoning over Knowledge Graphs, Semantic Search using NLP tools like Spacy and opensource Tensorflow, Pytorch, etcb. Design and develop Image recognition & video analysis models using Deep learning algorithms and open source tools like OpenCVc. Knowledge of State of the art Deep learning algorithms6. Optimize and tune Deep Learnings model for best possible accuracy7. Use visualization tools/modules to be able to explore and analyze outcomes & for Model validation eg: using Power BI / Tableau8. Work with application teams, in deploying models on cloud as a service or on-prema. Deployment of models in Test / Control framework for trackingb. Build CI/CD pipelines for ML model deployment9. Integrating AI&ML models with other applications using REST APIs and other connector technologies10. Constantly upskill and update with the latest techniques and best practices. Write white papers and create demonstrable assets to summarize the AIML work and its impact.