AIML Data Scientist

Technology / Subject Matter Expertise

• Sufficient expertise in Machine Learning, mathematical, and statistical sciences.
• Use of versioning & collaborative tools like Git / GitHub.
• Good understanding of the 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 solutions that can scale to industrial-grade strength.
• Ability to quantify & estimate the impact of ML models.

Soft Skills Profile

• Curiosity to think in fresh and unique ways with the intent of breaking new ground.
• Ability to share, explain, and 'sell' thoughts, processes, ideas, and opinions, even outside one's own span of control.
• Ability to think ahead and anticipate needs for problem-solving.
• 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 amounts of data using PySpark / Hadoop.
• Development & application of Reinforcement Learning.
• Knowledge of Optimization / Genetic Algorithms.
• Operationalizing Deep Learning models for customers and understanding nuances of scaling such models in real scenarios.
• Optimize and tune Deep Learning models for best possible accuracy.
• Understanding of stream data processing, RPA, edge computing, AR / VR, etc.
• Appreciation of digital ethics and data privacy.
• Experience working with AI & Cognitive Services platforms like Azure ML, IBM Watson, AWS SageMaker, Google Cloud.
• Experience in platforms like DataRobot, CognitiveScale, H2O.AI.

Skills Required

Cuda, Continuous Integration, Api Gateway, Data, Mathematics, Tensorflow, Pytorch