RentoMojo - Machine Learning Engineer - Data Modeling

Job Summary :

optimization.

You'll work with large-scale transactional data (e.g., orders, customer behavior) to create robust systems that predict rental demand, identify at-risk customers, and manage inventory efficiently, including handling returns and refurbishments.

This role is ideal for someone passionate about e-commerce/retail analytics and proficient in Python-based ML workflows.

Key Responsibilities :

- Demand Prediction : Develop and implement time-series forecasting models (e.g., using Prophet, ARIMA, or LSTM) to predict rental demand by product (SKU), category, and city.

- Churn Prediction : Build classification models (e.g., XGBoost, Random Forests) to predict customer churn based on subscription history, order patterns, and behavioral features.

- Use outputs to inform retention strategies and integrate with inventory models (e.g., estimating returns from churned users).

- Inventory Management : Design optimization models (e.g., using PuLP or linear programming) to manage stock levels, reorder points, and refurbishment cycles, leveraging demand and churn forecasts to minimize stockouts and overstock costs.

- End-to-End ML Pipeline : Create data pipelines (ETL) for ingesting and preprocessing order data (e.g., from CSV sources with timestamps, SKUs, cities).

- Feature engineering : Generate 50-100+ features like lagged orders, customer tenure, day-of-week effects, and holiday flags.

- Model Deployment & Monitoring : Deploy models as APIs (e.g., using FastAPI, Docker, Kubernetes) for real-time predictions.

- Implement monitoring for model drift and retraining workflows.

- Conduct A/B testing and evaluate models using metrics like RMSE (for demand), AUC-ROC (for

churn), and cost savings (for inventory).

- Scalability & Experimentation : Optimize models for large datasets (e.g., millions of orders)

using cloud platforms (AWS/GCP).

- Experiment with advanced techniques like reinforcement learning for dynamic pricing tie-ins.

Required Qualifications :

- Education : Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or

a related field.

- Experience : 3-5+ years as an ML Engineer or similar role, with hands-on experience in

retail/e-commerce analytics (e.g., demand forecasting, churn, inventory).

Technical Skills :

- Proficiency in Python (pandas, NumPy, scikit-learn) and ML libraries (Prophet, XGBoost, TensorFlow/PyTorch).

- Time-series forecasting (ARIMA, Prophet) and optimization tools (PuLP, SciPy).

- Data pipelines (Airflow, Spark) and deployment (Docker, Kubernetes, AWS SageMaker).

- SQL for data querying and cloud computing (AWS/GCP/Azure).

- Soft Skills : Strong problem-solving, ability to work in a small team, and experience with

Agile/Scrum methodologies.

- Domain Knowledge : Familiarity with subscription/rental models (e.g., handling returns,

refurbishments) in e-commerce.

Preferred Qualifications :

- Experience with reinforcement learning or advanced optimization for dynamic pricing.

- Knowledge of big data tools (e.g., Hadoop, Spark) for scaling models.

- Familiarity with RentoMojo-like platforms or the Indian e-commerce market.