Cognizant is looking for an AIML Data Scientist to solve business challenges using a consultative approach and advanced algorithms. This hands-on role focuses on applying AI/ML techniques, improving model accuracy, and deploying solutions in production environments.

What You'll Do

Apply Machine Learning & Deep Learning algorithms to solve business challenges using a consultative approach.
Use a wide variety of AI/ML techniques and algorithms to find optimal solutions.
Improve model accuracy to deliver greater business impact.
Estimate business impact due to deployment of models.
Work with domain/customer teams to understand business context and apply relevant Deep Learning solutions.
Pre-process data and perform feature engineering using Python/R/SQL/Cloud data pipelines.
Design, develop & deploy Deep learning models using Tensorflow/Pytorch.
Develop NLP models for Text Classification, Entity Recognition, Relationship extraction, Summarization, and Topic Modeling.
Design and develop Image recognition & video analysis models using Deep learning algorithms and OpenCV.
Optimize and tune Deep Learning models for best possible accuracy.
Use visualization tools like Power BI/Tableau for exploring outcomes and model validation.
Deploy models on cloud or on-prem, including building CI/CD pipelines for ML model deployment.
Integrate AI&ML models with other applications using REST APIs and connector technologies.
Upskill with latest techniques, write white papers, and create demonstrable assets.

What We're Looking For

5+ to 13 years of relevant experience.
Sufficient expertise in machine learning, mathematical and statistical sciences.
Hands-on experience with Python/R/SQL/Cloud data pipelines for data pre-processing and feature engineering.
Experience designing, developing & deploying Deep learning models using Tensorflow/Pytorch.
Experience using Deep learning models with text, speech, image and video data.
Knowledge of State of the art Deep learning algorithms.
Experience using versioning & collaborative tools like Git/Github.
Good understanding of AI solutions landscape: cloud, GPU compute, data security, API gateways, microservices, big data, CUDA Programming.
Ability to quantify & estimate the impact of ML models.
Curiosity to think in fresh and unique ways with intent of breaking new ground.
Ability to share, explain and 'sell' thoughts, processes, ideas, and opinions.
Ability to think ahead and anticipate needs for solving problems.
Ability to communicate key messages effectively and articulate strong opinions in large forums.

Nice to Have

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.
Experience operationalizing Deep learning models for customers and scaling in real scenarios.
Understanding of stream data processing, RPA, edge computing, AR/VR.
Appreciation of digital ethics and data privacy.
Experience with AI & Cognitive services platforms like Azure ML, IBM Watson, AWS Sagemaker, Google Cloud.
Experience in platforms like Data robot, Cognitive scale, H2O.AI.

Technical Stack

Languages & Frameworks: Python, R, SQL, Tensorflow, Pytorch, Spacy, OpenCV
Tools & Platforms: Power BI, Tableau, Git, Github, Cloud data pipelines, REST APIs, Pyspark, Hadoop