Principal Data Scientist

Tiger Analytics is pioneering what AI and analytics can do to solve some of the toughest problems faced by organizations globally. We develop bespoke solutions powered by data and technology for several Fortune 100 companies. We have offices in multiple cities across the US, UK, India, and Singapore, and a substantial remote global workforce.

We are also market leaders in AI and analytics consulting in the retail & CPG industry with over 40% of our revenues coming from the sector. This is our fastest-growing sector, and we are beefing up our talent in the space.

We are looking for a Principal Data Scientist with a good blend of data analytics background, who holds solid knowledge of Data Science Models, is quick learner, and has strong coding capabilities to add to our team.

Key Responsibilities:

• Work on the latest applications of data science to solve business problems of the Retail and CPG space.
• Effectively communicate the analytics approach and how it will meet and address objectives to business partners.
• Develop clear, concise, actionable solutions and recommendations for Client's business needs
• Undertake hands on work on data analytics, model development and testing and preparing the data files for visualization platforms
• Undertake business analysis on the data and provide insights
• Coordinate with decision makers to translate business questions into a verifiable hypothesis and data models
• Work hands-on across various analytics problems and provide thought leadership on problems
• Interact with onsite team as well as client on daily/weekly basis to gather requirements/ provide updates
• Stay connected with external sources of ideas through conferences and community engagements.
• Support demands from regulators, investor relations, etc., to develop innovative solutions to meet objectives utilizing cutting-edge techniques and tools.

Requirements

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related field.
• Minimum of 8 years of experience in Data Science and Machine Learning.
• Solid understanding of machine learning algorithms and statistical analysis.
• 5+ years of Model building and Enterprise-level deployment experience.
• Proficiency in Python and PySpark programming languages commonly used in Data Science.
• Real Time Series for FE and Real Time Process Execution/Scoring.
• Creation of Docker Containers to execute the code in Kubernetes Environment
• Proficiency using Azure databricks.
• Experience with cloud platforms such as Azure and AWS is a plus.
• Ability to preprocess and clean large datasets for analysis.
• Strong problem-solving skills and ability to think analytically.
• Excellent communication and presentation skills.
• Ability to work effectively in cross-functional teams and manage multiple projects simultaneously.
• Experience in Manufacturing space will be a bonus (good to have).