About the Role

As an AIML Data Scientist, you will leverage a wide array of AI/ML techniques and algorithms to identify optimal solutions for business problems. Your responsibilities will include improving model accuracy for greater business impact, estimating the business impact of model deployment, and collaborating with domain/customer teams to understand business context and data dictionaries. You will apply relevant Deep Learning solutions to address specific business challenges.

You will be proficient in pre-processing data and performing feature engineering for model development using tools and scripts such as Python, R, SQL, and Cloud data pipelines. The role involves designing, developing, and deploying Deep Learning models using frameworks like Tensorflow and Pytorch.

Key Responsibilities and Technologies

• Apply Machine Learning & Deep Learning algorithms to solve business challenges.
• Utilize AI/ML techniques to find optimal combinations for problem-solving.
• Enhance model accuracy to drive significant business impact.
• Quantify and estimate the business value derived from model deployment.
• Work with domain/customer teams to understand business needs and data structures.
• Implement Deep Learning solutions for complex business challenges.
• Pre-process data and engineer features using Python, R, SQL, and Cloud data pipelines.
• Design, develop, and deploy Deep Learning models with Tensorflow/Pytorch.
• Experience with Deep Learning models for text, speech, image, and video data.
• Design and develop NLP models for Text Classification, Custom Entity Recognition, Relationship Extraction, Text Summarization, Topic Modeling, Reasoning over Knowledge Graphs, and Semantic Search using NLP tools like Spacy and open-source frameworks.
• Design and develop Image Recognition and Video Analysis models using Deep Learning algorithms and tools like OpenCV.
• Possess knowledge of State-of-the-art Deep Learning algorithms.
• Optimize and tune Deep Learning models for maximum accuracy.
• Utilize visualization tools such as Power BI or Tableau for outcome exploration, analysis, and model validation.
• Collaborate with application teams to deploy models on cloud as a service or on-premise.
• Implement model deployment within Test/Control frameworks for tracking.
• Build CI/CD pipelines for ML model deployment.
• Integrate AI/ML models with other applications using REST APIs and connector technologies.
• Continuously upskill and stay updated with the latest techniques and best practices.
• Document AIML work and its impact through white papers and demonstrable assets.

Technology and Subject Matter Expertise

• Proficiency in machine learning, mathematical, and statistical sciences.
• Experience with versioning and collaborative tools like Git/Github.
• Strong understanding of AI solution landscapes, including cloud computing, GPU-based compute, data security and privacy, API gateways, microservices architecture, big data ingestion, storage, processing, and CUDA Programming.
• Ability to scale prototype ideas into industrial-grade solutions.
• Capability to quantify and estimate the impact of ML models.

Soft Skills Profile

• Curiosity and a drive for innovation.
• Effective communication and persuasion skills to share and articulate ideas and processes.
• Proactive and anticipatory problem-solving abilities.
• Strong communication skills to convey key messages and opinions effectively in various forums.

Desirable Experience

• Contributions to open-source communities and platforms like Kaggle.
• Ability to process large datasets using PySpark/Hadoop.
• Development and application of Reinforcement Learning.
• Knowledge of Optimization/Genetic Algorithms.
• Experience operationalizing Deep Learning models in real-world scenarios and understanding scaling nuances.
• Familiarity with stream data processing, RPA, edge computing, AR/VR.
• Understanding of digital ethics and data privacy.
• Experience with AI & Cognitive services platforms such as Azure ML, IBM Watson, AWS Sagemaker, or Google Cloud.
• Experience with platforms like DataRobot, CognitiveScale, or H2O.AI.