Lead Data Scientist
4 days ago
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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