Roles & Responsibilities
Eligibility
Minimum Qualifications
Bachelor’s degree in computer science or a related field OR master’s degree in statistics, economics, business economics, econometrics, or operations research.
4-6 years of experience in the Analytics/Data Science domain.
Proficiency in programming languages such as Python.
Experience with Generative AI techniques and tools.
Familiarity with ETL methods, data imputation, data cleaning, and outlier handling.
Familiarity with cloud platforms (AWS, Azure, GCP) and AI/ML services.
Knowledge of databases and associated tools such as SQL.
Technical Skills – Desirable
Expertise in NLP and Generative AI concepts/methods/techniques like
— Prompt design/engineering
— Retrieval Augmented Generation (RAG), Corrective RAG and Knowledge Graph-based RAG using GPT-4o
— Fine-tuning through LORA/QLORA
— Multi-agentic frameworks for RAG
— Reranker etc. for enhancing the plain-vanilla RAG
— Evaluation frameworks like G-Eval etc.
Strong understanding of Deep Learning methods and Machine Learning techniques including Ensemble methods, Support Vector Machines, and Natural Language Processing (NLP).
Exposure to Big Data technologies like Hadoop, Hive, Spark.
Experience with advanced reporting tools such as Tableau, Qlikview, or PowerBI.
Specific Responsibilities
Requirement Gathering
Translate business requirements into actionable analytical plans in collaboration with the team.
Ensure alignment of analytical plans with the customer’s strategic objectives.
Data Handling
Identify and leverage appropriate data sources to address business problems.
Explore, diagnose, and resolve data discrepancies including ETL tasks, missing values, and outliers.
Development And Execution
— Individually deliver projects, proof-of-concept (POC) initiatives from inception to completion.
— Contribute to the development and refinement of technical and analytics architecture, ensuring it aligns with project and organizational goals.
— Implement scalable and robust analytical frameworks and data pipelines to support advanced analytics and machine learning applications.
— Coordinating with cross-functional teams to achieve project goals.
— Delivery of production-ready models and solutions, meeting quality and performance standards.
— Monitor success metrics to ensure high-quality output and make necessary adjustments.
— Create and maintain documentation/reports.
Innovation And Best Practices
Stay informed about new trends in Generative AI and integrate relevant advancements into our solutions.
Implement novel applications of Generative AI algorithms and techniques in Python.
Sample Projects
GenAI-powered self-serve analytics solution for a global technology giant, that leverages the power of multi-agent framework and Azure OpenAI services to provide actionable insights, recommendations, and answers to tactical questions derived from web analytics data.
GenAI bot for querying on textual documents (e.g., retail audit orientation, FAQ documents, research brief document etc.) of multinational dairy company and, and getting personalized responses in a natural and conversational way, based on the structured context of the user (like their personal details), along with the citations, so that one can effortlessly carry out their first-hand validation themselves
GenAI bot for querying on tabular dataset (like monthly KPI data) of leading global event agency to understand, process natural language queries on the data and generate appropriate responses in textual, tabular and visual formats.
GenAI-powered advanced information retrieval from structured data of a global technology leading organization
TimesFM modelling for advanced time series forecasting for a global retail chain
Knowledge-Graph-based GenAI solution for knowledge retrieval and semantic summarization for a leading global event agency
GenAI-powered shopping assistant solution for big-box warehouse club retail stores
GenAI solution using multi-agentic framework for travel-hospitality use case
Input Governance and Response Governance in GenAI Solutions
Development and implementation of evaluation frameworks for GenAI solutions/applications
Training Foundational Models on new data using open-source LLM or SLM
Experience
Skills
- Primary Skill: Data Science
- Sub Skill(s): Data Science
- Additional Skill(s): Data Science, Python (Data Science), GenAI Fundamentals