• Strong experience in Python
• 3+ years’ experience of working on feature/data pipelines using PySpark
• Understanding and experience around data science
• Exposure to AWS cloud services such as Sagemaker, Bedrock, Kendra etc.
• Experience with machine learning model lifecycle management tools, and an understanding of MLOps principles and best practice
• Experience with statistical models e.g., multinomial logistic regression
• Experience of technical architecture, design, deployment, and operational level knowledge
• Exploratory Data Analysis
• Knowledge around Model building, Hyperparameter tuning and Model performance metrics.
• Statistics Knowledge (Probability Distributions, Hypothesis Testing)
• Time series modelling, Forecasting, Image/Video Analytics, and Natural Language Processing (NLP).

Roles & Responsibilities

• Work closely with business and product management teams to develop and implement analytics solutions.
• Collaborate with data engineers & architects to implement and deploy scalable solutions.
• Communicate results to diverse technical and non-technical audiences.
• Design accurate and scalable prediction algorithms
• Generate actionable insights for business improvements.
• Ability to understand business requirements.
• Use case derivation and solution creation from structured/unstructured data.
• Actively drive a culture of knowledge-building and sharing within the team
• Encourage continuous innovation and out-of-the-box thinking.
• Experience applying theoretical models in an applied environment.
• MLOps, Data Pipeline, Data engineering
• Statistics Knowledge (Probability Distributions, Hypothesis Testing)