JD for Data Scientist

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 Data Scientist with 5+ 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 in Computer Science, Statistics, or related fields.
• Experience: 4+ 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).