Data Scientist

Aditi Consulting; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

A technology consulting company in Bengaluru seeks an experienced AI expert with 5+ years in the field. You will enhance data accessibility and automate tasks using AI-driven workflows. Strong skills in Python and AIML, along with experience in Snowflake, are required. Familiarity with Azure AI and a background in finance or regulated industries will be advantageous. This is a unique opportunity to lead innovative projects within a dynamic team.

Qualifications

• 5+ years of overall experience in business intelligence or AI roles.
• Some leadership qualities are expected.
• Experience in financial or regulated industries preferred.

Responsibilities

• Enhance data accessibility through AI-driven workflows.
• Automate tasks using agent workflows.
• Build and deploy custom ML models.

Skills

Python

AIML libraries

Agent workflow

Custom ML models

Vector databases

Snowflake Cortex

Azure AI (GPT-4)

Unit testing

Tools

Snowflake

Azure

AWS S3

Job description

Overview

• Team: Business Intelligence and AI Team.
• Focus: Enhancing data accessibility, conversational AI, and building AI-driven workflows.
• Goal: Optimize internal operations using AI solutions before expanding externally.
• Experience Level: 5+ years of overall experience.
• Leadership Quality: Some leadership qualities are expected.

Technical Requirements

• Programming: Python and AIML libraries.
• Key Concept: Agent workflow – automating tasks by chaining AI agents (e.g., for recruitment sourcing).
• Traditional ML: Ability to build, train, and deploy custom ML models.
• Vector Database: Hands-on experience with vector databases for semantic search and efficient data retrieval in RAG implementations.
• Snowflake Cortex: Strong experience in Snowflake, specifically Snowflake Cortex for building internal UIs and applications. 1-2 years hands-on experience in Snowflake is sufficient.
• LLMs Used: Azure AI (GPT-4, GPT 4.2), exploring Gemini Pro. Traditional models include Llama-based models (e.g., 70 billion parameters).
• Data Sources: Primarily Snowflake data lake, potentially Azure and AWS S3.
• Financial Background: Good to have experience in financial or regulated industries, or an understanding of finance-related business questions.
• Testing: Basic unit testing and generation of unit test cases are required.