Licious is a fast-paced, innovative D2C brand revolutionizing the meat and seafood industry in India.

We leverage cutting-edge technology, data science, and customer insights to deliver unmatched quality, convenience, and personalization.

Join us to solve complex problems at scale and drive data-driven decision-making!.

Role Overview :

We are seeking a Lead Data Scientist with 6+ years of experience to build and deploy advanced ML models (LLMs, Recommendation Systems, Demand Forecasting) and generate actionable insights.

You will collaborate with cross-functional teams (Product, Supply Chain, Marketing) to optimize customer experience, demand prediction, and business growth.

Key Responsibilities :

Machine Learning & AI Solutions :

- Develop and deploy Large Language Models (LLMs) for customer support automation, personalized content generation, and sentiment analysis.

- Enhance Recommendation Systems (collaborative filtering, NLP-based, reinforcement learning) to drive engagement and conversions.

- Build scalable Demand Forecasting models (time series, causal inference) to optimize inventory and supply chain.

Data-Driven Insights :

- Analyze customer behavior, transactional data, and market trends to uncover growth opportunities.

- Create dashboards and reports (using Tableau/Power BI) to communicate insights to stakeholders.

Cross-Functional Collaboration :

- Partner with Engineering to productionize models (MLOps, APIs, A/B testing).

- Work with Marketing to design hyper-personalized campaigns using CLV, churn prediction, and segmentation.

Innovation & Scalability :

- Stay updated with advancements in GenAI, causal ML, and optimization techniques.

- Improve model performance through feature engineering, ensemble methods, and experimentation.

Qualifications :

Education : BTech/MTech/MS/Ph.D in Computer Science, Statistics, or related fields.

Experience : 6+ years in Data Science, with hands-on expertise in:

- LLMs (GPT, BERT, fine-tuning, prompt engineering).

- Recommendation Systems (matrix factorization, neural CF, graph-based).

- Demand Forecasting (ARIMA, Prophet, LSTM, Bayesian methods).

- Python/R, SQL, PySpark, and ML frameworks (TensorFlow, PyTorch, scikit-learn).

- Cloud platforms (AWS/GCP) and MLOps tools (MLflow, Kubeflow).