About Lifelong :

Since 2015, Lifelong Online has been redefining everyday living with innovative, reliable, and thoughtfully designed products. With a portfolio of over 300 products, we've become a trusted part of10 million households across India, delivering to over 10,000 pin codes. Our offerings span two key verticals : Sports, Fitness, and Personal Grooming, where we empower active lifestyles and self-care, and Home Solutions, encompassing kitchen and home essentials, home improvement tools, electronics, and

baby care. At Lifelong Online, we're committed to making life simpler, smarter, and more enjoyable crafted for you and proudly made for India.

What You'll Work On :

- Build and ship production ML models for demand forecasting, secondary sales analytics, and supply chain optimization.

- Design and run statistical analyses on operational data - supply chain leakage, marketplace performance, partner channel ROI, category-level insights.

- Translate raw operational signals into actionable insights for operators, category managers, and leadership.

- Own the analytical layer of Operations - replace fragmented reports with intelligent decision dashboards.

- Partner with the AI Engineer to feed model outputs into agentic decision workflows.

- Partner with Full-Stack engineers to ship analytical insights as API-driven dashboard features.

- Build experimentation infrastructure (A/B test design, causal inference) for operational interventions.

Who Should Apply

- 4+ years of professional data science experience, with at least 2 years shipping production ML.

- Strong Python data stack - pandas, NumPy, scikit-learn, stats models, plus at least one DL framework (PyTorch or TensorFlow).

- Deep understanding of time-series forecasting (ARIMA, Prophet, exponential smoothing, hierarchical models) and classical ML (regression, classification, clustering).

- SQL fluency - comfortable with complex queries, window functions, query optimization on large operational datasets.

- Working knowledge of MLOps tooling - experiment tracking (MLflow, Weights & Biases), model registry, and feature stores.

- Strong statistical foundation - hypothesis testing, causal inference, experiment design beyond A/B.

- Bonus : Background in retail / D2C / consumer / supply chain / FMCG.

- Bonus : Exposure to LLM-powered analytics (text-to-SQL, RAG over reports, conversational BI).

- Bonus : Production experience with AWS Sagemaker, Vertex AI, or equivalent ML platforms.

What We Offer :

- End-to-end ownership of the analytical layer - production models and statistical analyses that drive operational decisions.

- Modern stack - Bedrock + Vertex AI for ML serving, modern data infrastructure.

- High-leverage work - every model has direct, measurable impact on revenue, margin, or operational efficiency.