Data Science Manager - Artificial Intelligence/Machine Learning

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Skills

Responsibilities

• Implement the complete analytical section of the product based on the defined functional scope, including necessary documentation and prototypes.
• Extract, collate, and cleanse data from various entities.
• Identify suitable databases (SQL/NoSQL) for data storage and various technology stacks and libraries for data analysis and processing.
• Implement industry-standard statistical models with Machine Learning and Deep Learning algorithms, incorporating predictive and auto-learning capabilities.
• Identify meaningful insights and foresights from data and metadata sources based on the models.
• Interpret and communicate findings to the product manager to derive more business strategies.
• Visualize various dimensions of data for both web and mobile platforms.
• Showcase output through demos, prototypes, and working systems in regular review meetings.
• Continuously refine models for accuracy.
• Analyze, review, and track trends and tools in Data Sciences, Machine Learning, Artificial Intelligence, and Augmented Intelligence, and appropriately apply these learnings to the product.
• Interface the analytical system with transaction systems for both information reporting and to process actionable items derived through analytics.
• Ensure Quality, Security, Performance, Scalability, Reliability, and Availability are key factors in the implementation of the analytics system.
• Deploy and train various Large Language Models (LLMs) for AI agents and domain-specific applications.
• Implement Retrieval Augmented Generation (RAG) pipelines along with Vector databases to enhance the intelligence of LLMs and agent performance.
• Stay up-to-date with advancements in AI, including emerging LLM architectures and agentic AI frameworks and technologies.
  

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related quantitative field.
• 5-7 years of experience in data science, with a proven track record of leading and implementing data science projects.
• Strong expertise in statistical modeling, machine learning, and deep learning algorithms.
• Proficiency in programming languages such as Python or R.
• Experience with SQL/NoSQL databases and big data technologies.
• Familiarity with data visualization tools.
• Experience with LLMs, RAG pipelines, and vector databases is highly desirable.
• Excellent communication and interpersonal skills, with the ability to interpret and present complex data insights to non-technical
  

• Ability to work independently and collaboratively in a fast-paced environment.
• Experience in the banking domain is an added advantage.