Lead Data Scientist

4 days ago

Bangalore, India Licious Full time

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).

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