Associate Data Scientist

Overview

We are building the next-generation AI-powered Retail Technology platform—covering ERP, POS, OMS, WMS, Analytics and Agentic Workflows. As part of this transformation, we are hiring an Associate Data Scientist who is AI-native first—not restricted by languages, tools, or frameworks, but motivated by problem-solving, business impact, and product innovation.

This is not a typical entry-level role. You will work directly with senior product leaders, own data science features end-to-end, experiment with cutting-edge models, and drive a small cross-functional team to deliver meaningful AI/ML capabilities inside our software products.

Key Responsibilities

AI-Native Solutioning & Product Innovation

Understand business requirements deeply and translate them into robust data science solutions.

Conceptualize, build, and iterate AI-driven features across the product suite (ERP, POS, Analytics, OMS, CRM, etc.).

Work with product managers to convert ideas into well-defined problem statements, prototypes, and production-ready components.

Rapidly experiment with multiple ML/LLM approaches to identify the best-fit solution, not restricted by tool or technology.

Machine Learning & Statistical Modelling

Build, tune, evaluate and deploy classical ML models including regression, tree-based models, clustering, anomaly detection, recommendation engines, and forecasting models.

Conduct strong statistical analysis to ensure model validity, robustness, and interpretability.

Participate in data cleaning, feature engineering, and data pipeline optimisation.

LLM & Agentic AI

Design and prototype solutions using LLMs (open-source and API-based), embeddings, vector databases, prompt engineering, and RAG pipelines.

Build agentic workflow automations using open-source frameworks (e.g., LangGraph, CrewAI, AutoGen, LlamaIndex).

Work with Multi-Agent Systems, chain-of-thought reasoning, and tool-using agents for real-world retail operations.

Exposure to MCP (Model Context Protocol) and A2A (Agent to Agent) patterns for enabling AI-native interactions across systems.

Team Collaboration & Delivery

Lead and guide a small internal team of analysts/engineers for delivering AI/ML modules end-to-end.

Collaborate with engineering teams to integrate models into the product using APIs, microservices, or embedded inference engines.

Ensure documentation, reproducibility, and deployment-ready code for all experiments.

AI Automation & Workflow Engineering

Build AI workflows to automate repetitive business processes across retail value chain (procurement, merchandising, operations, stores, customer engagement).

Work with tools like n8n, LangGraph Studio, automation agents, infrastructure orchestration etc.

Mindset & Ownership

Demonstrate a self-driven learning approach—staying updated with latest research, frameworks, and techniques.

Constantly experiment with open-source tools, contribute to internal knowledge base, and drive innovation.

Bring a product-first mindset—balancing feasibility, performance, and user experience.

Mandatory Skills

Required Skills & Qualifications

Strong understanding of statistics, probability, hypothesis testing, and experimental design.

Hands-on experience with classical ML algorithms (regression, classification, clustering, tree models, boosting, time series).

Good understanding of LLMs, RAG, embeddings, vector DBs, prompt engineering.

Exposure to open-source agentic frameworks (LangChain, LangGraph, AutoGen, CrewAI, LlamaIndex).

Understanding of multi-agent architecture, agent tool usage, decision-making engines.

Knowledge of MCP, A2A protocols or equivalent agent orchestration approaches.

Ability to convert business problems into ML problem definitions.

Ability to lead a small team or interns and drive outcomes.

Proficiency in Python (or any equivalent AI-first language)—but open to exploring any platform/tools.

Experience with Git, APIs, Docker, and modern deployment workflows.

Strong communication skills, analytical thinking, and product mindset.

Preferred Skills

Experience with cloud platforms (Azure, AWS, GCP) for model deployment.

Exposure to data lakehouse systems (Iceberg, Delta, Parquet), ETL/ELT pipelines.

Experience with AutoML frameworks, MLOps stacks, CI/CD for ML.

Knowledge in retail domain (merchandising, demand planning, POS, loyalty, inventory).

Experience using agent-based workflow automation tools (n8n, Airflow + agents, Make.com).

Familiarity with reinforcement learning, causal inference, and advanced forecasting techniques.

Understanding of microservices, event-driven architecture, and message queues (Kafka).

Contributions to open-source AI/ML projects.

Educational Background

B.Tech / M.Tech / M.Sc in Data Science, Computer Science, AI/ML, Statistics, Mathematics or related fields.

Candidates with strong portfolios/GitHub/LLM experiments may also apply irrespective of formal degree.

What We Offer

Opportunity to build AI-native retail tech products used by top brands.

Work directly with CTO, Head of AI, and senior architects.

Fast-track career progression for high performers.

Access to cutting-edge infrastructure, tools, and research-driven environment.

Ownership, autonomy, and freedom to experiment.