AIML Data Scientist

• Be a hands-on problem solver with a consultative approach, applying Machine Learning & Deep Learning algorithms to solve business challenges.
• Use knowledge of a wide variety of AI/ML techniques and algorithms to determine which combinations best solve the problem.
• 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, data dictionaries, and apply relevant Deep Learning solutions for given business challenges.
• Work with tools and scripts for sufficient data pre-processing & feature engineering for model development – Python / R / SQL / Cloud data pipelines.
• Design, develop & deploy Deep Learning models using TensorFlow / PyTorch.
• Experience in using Deep Learning models with text, speech, image, and video data.
• 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 open-source TensorFlow, PyTorch, etc.
• Design and develop Image Recognition & Video Analysis models using Deep Learning algorithms and open-source tools like OpenCV.
• Knowledge of state-of-the-art Deep Learning algorithms.
• Optimize and tune Deep Learning models for best possible accuracy.
• Use visualization tools/modules to explore and analyze outcomes & for model validation, e.g., Power BI / Tableau.
• Work with application teams to deploy models on cloud as a service or on-premises.
• Deployment of models in Test / Control framework for tracking.
• Build CI/CD pipelines for ML model deployment.
• Integrate AI & ML models with other applications using REST APIs and other connector technologies.
• Constantly upskill and update with the latest techniques and best practices. Write white papers and create demonstrable assets to summarize AI/ML 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 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.