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Data Analyst – ML Engineer

ETP Group -
Mumbai, Maharashtra

Apply Now

Job details

Full-time
7 days ago

Benefits

Health insurance

Qualifications

TensorFlow
CI/CD
Power BI
Law
Kubernetes
Tableau
Master's degree
SQL
Docker
Sentiment analytics
Machine learning
Continuous integration
REST
Forecasting
Natural language processing
APIs
Fraud prevention and detection
Keras
Flask
AI
Python

Full job description

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.

Apply Now

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