Job Title

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:1. 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.2. 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.3. 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.4. 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).