Job Category: AI & Data Science : Key Responsibilities Business-Centric AI & Modeling
Partner with internal stakeholders (Brand Marketing, Merchandising, CRM, and Business Strategy teams) to identify opportunities to apply ML, LLMs, and agent-based intelligence for high-value retail use cases.
Design, develop, and deploy advanced Demand Forecasting, Markdown Optimization, and Price Elasticity models to optimize inventory and pricing strategy.
Build and operationalize Customer Lifetime Value (CLTV), Churn Prediction, and RFM segmentation models for personalized marketing and retention strategy.
Develop Market Basket Analysis, Cross-Sell/Recommender Systems, and Promotion Effectiveness frameworks using both classical ML and deep learning approaches.
Create and maintain Agentic AI workflows for campaign orchestration, customer service automation, and marketing optimization (e.g., autonomous agents for A/B test selection or personalized product recommendations).
Advanced AI & Generative Intelligence
Fine-tune Small and Large Language Models (e.g., LLaMA, Mistral, GPT, Claude) for tasks such as customer sentiment analysis, product description enrichment, and insight summarization.
Design retrieval-augmented generation (RAG) pipelines integrating structured and unstructured data (CRM, catalog, social, and web data).
Experiment with Agentic AI frameworks (LangChain, CrewAI, AutoGen, OpenDevin, etc.) to build multi-agent systems that can autonomously gather insights, recommend actions, or run simulations.
Apply multimodal models (text + image) for fashion tagging, trend analysis, and visual recommendation.
MLOps, Engineering, and Deployment
Partner with the Data Engineering and MLOps teams to productionize models at scale via CI/CD pipelines.
Develop scalable APIs and deploy models into cloud environments (Azure, AWS, or GCP).
Build robust monitoring frameworks for model drift, bias detection, and performance tracking.
Contribute to development of internal AI/ML model registry and experimentation frameworks.
Qualifications Core Data Science & ML
3-5 years of experience in applied data science, ideally within retail or e-commerce sectors.
Expertise in supervised, unsupervised, and deep learning techniques (Regression, Random Forest, XGBoost, LightGBM, ARIMA, SARIMAX, GARCH, CNNs, RNNs, Transformers, etc.).
Strong experience with time series forecasting, optimization modeling, and causal inference.
Experience with feature engineering, model tuning, and validation at scale.
LLM, SLM & Agentic AI Exposure
Experience fine-tuning or prompt-engineering LLMs for enterprise tasks (e.g., summarization, insight generation, or dialogue systems).
Familiarity with LangChain, LlamaIndex, or CrewAI for developing autonomous or semi-autonomous agents.
Understanding of retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Weaviate), and embedding models.
Experience leveraging multimodal AI (vision + language) for product recommendations or catalog enrichment.
Programming & Tools
Advanced proficiency in Python, PySpark, and SQL.
Experience with TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers, and LLM APIs.
Exposure to any of the MLOps frameworks (MLflow, Kubeflow, Airflow, Vertex AI, SageMaker).
Comfortable with distributed computing tools (Spark, Hadoop, Hive) and data lakes (Databricks, Delta Lake).
Business & Communication
Proven ability to translate complex data insights into business strategy.
Experience collaborating with non-technical stakeholders in retail, marketing, and merchandising domains.
Excellent written and verbal communication skills.
Preferred
Master's or PhD in Statistics, Computer Science, Applied Mathematics, or related field.
Exposure to fashion retail analytics, customer behavior modeling, and omnichannel data systems.
Familiarity with Web Analytics tools (Google Analytics, Adobe Analytics) and APIs (Google Maps, Geocoding, etc.).
Why Join Us
Shape the future of AI-led decisioning in one of the region's largest fashion retail conglomerates.
Work on real-world generative and agentic AI projects beyond prototypes.
Be part of a collaborative, innovative, and impact-driven Data Science & AI team.