Requirements

Key Responsibilities

Pricing Elasticity Modeling

• Build econometric and ML models
  for price elasticity across products, channels, and segments.
  
• Develop dynamic and competitive
  pricing using regression, Bayesian, causal inference, and RL techniques.
  
• Create scenario simulation
  tools to forecast revenue and margin impact of pricing strategies.
  

Customer Segmentation

• Develop behavioral, RFM,
  lifecycle, and propensity -based segmentation using clustering and embeddings.
  
• Leverage LLMs to enrich
  customer representations from unstructured data (reviews, tickets,
  communications).
  
• Operationalize segments into
  CRM, CDP, and personalization platforms for marketing and sales activation.
  

AI -Driven Supply Chain Intelligence

• Architect demand forecasting
  using classical (ARIMA, Prophet) and deep learning (LSTM, TFT, N -BEATS)
  methods.
  
• Build inventory optimization,
  replenishment, and supplier risk models using ML and operations research.
  
• Design LLM -powered agents and
  copilots for analytics, anomaly detection, and decision support.
  

Technical Leadership & Delivery

• Own end -to -end solution design
  — data ingestion, feature engineering, deployment, monitoring, governance.
  
• Mentor data scientists/ML
  engineers; lead code, model, and design reviews.
  
• Establish MLOps/LLMOps best
  practices: CI/CD, versioning, drift detection, and responsible AI guardrails.
  

Required Qualifications

• 3+ years in AI/ML engineering
  or applied data science, with 1+ year of production LLM experience.
  
• Bachelor's or Master's in CS,
  Data Science, Statistics, OR, Economics, or related quantitative field.
  
• Strong Python skills with
  scikit -learn, XGBoost/LightGBM, and PyTorch or TensorFlow.
  
• Hands -on with LLM tooling:
  LangChain/LlamaIndex, Hugging Face, OpenAI/Anthropic/Bedrock/Vertex APIs.
  
• Experience building RAG
  systems, agentic workflows, and prompt engineering for enterprise use cases.
  
• Solid grounding in statistics,
  time -series forecasting, and causal inference.
  
• Production deployment
  experience on AWS (SageMaker), Azure ML, or GCP Vertex AI.
  
• Strong SQL and modern data
  stack exposure: Snowflake, Databricks, BigQuery, or Redshift.
  
• MLOps experience with MLflow,
  Airflow, Docker, Kubernetes, and CI/CD pipelines.
  
• Excellent stakeholder
  communication; able to present to executive audiences.
  

Preferred Qualifications

• Domain experience in retail,
  CPG, e -commerce, manufacturing, or logistics.
  
• Familiarity with vector DBs
  (Pinecone, Weaviate, FAISS, pgvector), uplift modeling, and bandits.
  
• Cloud certifications: AWS ML
  Specialty, Azure AI Engineer, or GCP ML Engineer.
  
• Prior consulting or
  client -facing delivery leadership; OSS contributions or publications a plus.