Data Analyst – ML Engineer

Experience Required

2 - 5

Location

Mumbai

Role Type

Full Time

Designation: Data Analyst – ML Engineer

Department: R&D

Location: Saki Vikar (Beside L&T)

Work Mode: Work from office

Working Days: Monday to Friday

Experience: 2 - 5 years

Job Description

About The Role

ETP Group is a leader in unified commerce and omni-channel retail technology. Our AI/ML team builds and ships production-grade Machine Learning and Gen AI capabilities into our enterprise SaaS platforms, Ordazzle and Unify, used by leading retail and e-commerce brands.

We are looking for a Data Analyst with Machine Learning with strong fundamentals in ML/DL modelling and data analysis, who has taken at least one model beyond the notebook.

Key Responsibilities

• AI/ML Modelling & Data Analysis
• 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.
• Continuous Improvement
• 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.