Data Analyst – ML Engineer

Etp Group; Mumbai; On-site; INR 1,200,000 - 1,800,000; Full time

Benefits offered by this job

Pick & Drop facility from Saki Naka

Breakfast and lunch facility

Medical insurance coverage

Job summary

ETP Group in Mumbai is hiring a Data Analyst with ML focus to design, build, and deploy ML/DL models for retail and e-commerce. You will work on large datasets, create production-ready code, and collaborate across data science, software, and DevOps teams to deliver scalable AI solutions.

Strong Python/SQL skills, ML/DL framework experience, and a solid grounding in statistics are essential. Familiarity with MLOps, Docker, and cloud concepts is a plus. Join us to drive AI-powered retail insights.

Qualifications

• 2–5 years of experience with ML model development and data analysis.
• Strong Python and SQL for data analysis, modelling, and data workflows.
• Hands-on experience with ML/DL algorithms and frameworks (scikit-learn, TensorFlow/Keras or PyTorch, XGBoost).
• Foundations in statistics, data preprocessing, feature engineering, evaluation and tuning.
• Experience delivering ML use cases in production and knowledge of model serving and monitoring.
• Familiar with MLOps: versioning, experiment tracking, workflow scheduling, containers, and CI/CD.

Responsibilities

• Design, build, and evaluate ML and DL models for retail/e-commerce use cases.
• Perform EDA, preprocessing, and feature engineering on large structured datasets.
• Fine-tune models to meet accuracy and performance benchmarks in production.
• Translate models into production-ready code with scalable, reliable implementations.
• Collaborate with data scientists, software engineers, and DevOps for model delivery.
• Build data pipelines and expose models as REST services (FastAPI/Flask).
• Support deployment, monitoring, and logging of models in production using MLOps tooling.
• Research best practices in ML engineering, versioning, containerization, deployment strategies.
• Contribute reusable components and libraries for the AI team.
• Stay updated with advances in ML/DL, Generative AI capabilities.

Skills

Python

SQL

ML/DL frameworks

Statistics

MLOps concepts

Model deployment

REST APIs

Containerization

Tools

MLflow

Airflow

Docker

Kubernetes

Job description

ETP Group is an AI-first SaaS company serving the Retail and e-Commerce industries across Asia Pacific. With 39 years of trust in the market, it supports 500+ brands in 17 countries through enterprise-grade platforms.ETP’s cloud-native solutions—ETP Unify and Ordazzle—cover POS, CRM, Inventory, Promotions, PIM, OMS, WMS, LMS, and seamless marketplace integration. For large-format retail, ETP V5 offers a hybrid omni-channel suite.Built on secure, scalable M.A.C.H architecture. ETP delivers frictionless, personalized experiences across channels. Its intuitive, asset-light platforms accelerate cloud transformation, reduce IT overhead, and help retailers enhance CX, drive growth, and lead in a fast-evolving commerce environment.Here is a glimpse of what we do - http://www.etpgroup.com/Videos.htmlFor more information, log on to : www.etpgroup.com

Experience Required

2 - 5

Location

Role Type

Full Time

Key Responsibilities:

• Design, build, and evaluate ML and Deep Learning models — classification, regression, time-series forecasting, anomaly/fraud detection, churn prediction, and recommendation systems for retail and e-commerce use cases.
• Perform exploratory data analysis, data preprocessing, and feature engineering on large structured and unstructured retail datasets (orders, transactions, customers, catalog, POS data).
• Optimize and fine-tune models through rigorous evaluation, validation, and hyperparameter tuning to meet accuracy and performance benchmarks in real-world scenarios.
• Translate models from research/prototype into production-ready code, ensuring scalability, efficiency and reliability.
• Collaborate with data scientists, software engineers, Business Analyst, and DevOps teams to identify technical requirements, use cases, and user stories for model delivery.
• Build data pipelines and workflows to integrate models with our software products, exposing them as services via REST APIs (FastAPI/Flask).
• Support deployment, monitoring, and logging of models in production to track performance and detect issues, working with established MLOps tooling in the team.
• Continuously research and apply best practices in machine learning engineering — model versioning, containerization, and deployment strategies.
• Contribute to internal tools, reusable components, and libraries that enhance the efficiency of the AI team.
• Keep up-to-date with advancements in ML/DL frameworks, libraries, and emerging Generative AI capabilities.

The Job responsibilities of the candidate shall include but not limited to the Job Description & to perform any other tasks/functions as required by the Company.

Experience and Skills:

Must-Have

• 2–5 years of experience as Data Analyst with a strong focus on ML model development.
• Strong proficiency in Python and SQL for data analysis, modelling, and building data workflows.
• Hands-on experience with ML/DL algorithms and frameworks — Supervised, Unsupervised ML algorithms, Scikit-learn, TensorFlow/Keras or PyTorch, XGBoost/ensemble methods.
• Strong grounding in statistics and ML fundamentals — data preprocessing, feature engineering, model evaluation, validation strategies, and hyperparameter tuning.
• Experience with at least one ML use case delivered to production — understanding how models are served, integrated, and monitored in real applications (not just POCs or notebooks).
• Basic knowledge of MLOps concepts — model versioning, experiment tracking (e.g., MLflow), workflow scheduling (e.g., Airflow), containerization (Docker), and CI/CD — with willingness to deepen these skills on the job.
• Basic understanding of Generative AI and LLMs — what RAG, embeddings, and prompt engineering are, and how LLM APIs (OpenAI, Gemini, Claude) are used in building Gen AI applications.

Good-to-Have

• Experience with time-series forecasting, anomaly/fraud detection, churn prediction, recommendation systems, LLM Models.
• Exposure to NLP — text classification, sentiment analysis, or extracting insights from customer feedback data.
• Familiarity with cloud platforms (GCP preferred) and Docker and Kubernetes.
• Awareness of LLM-based application patterns (chatbots, AI assistants, conversational AI) — hands-on experience is a plus but not required.
• Familiarity with the retail/e-commerce domain (orders, inventory, POS, pricing, promotions, customer behavior data).
• Experience with BI/visualization tools (Power BI, Tableau) for communicating insights.

Perks and Benefits

• Pick & Drop facility from Saki Naka Metro.
• Complimentary breakfast and subsidized lunch facility available.
• Medical insurance coverage.