Lead Data Scientist

Lead Data Scientist

Key Responsibilities:

Data Science & Statistical Modeling

• Lead the design of experiments using

  hypothesis testing

  ,

  confidence intervals

  ,

  p-values

  , and other inferential methods.
• Guide the team on

  bias-variance trade-offs

  ,

  model selection

  , and

  validation strategies

  like k-fold cross-validation, stratified sampling, etc.
• Build scalable ML models for structured and unstructured data using Python, Scikit-learn, PyTorch, or TensorFlow.
  

Cloud & Deployment (Azure Focused)

• Architect ML solutions on

  Azure

  , leveraging services such as

  Azure ML, Azure Functions, AKS, and Azure Blob/Data Lake

  .
• Design and implement

  deployment pipelines

  for both

  batch

  and

  real-time inference

  use cases.
• Develop and manage

  containerized services

  (Docker, Kubernetes) and robust

  API endpoints

  for ML integration.
• Handle

  model packaging, versioning

  , and

  rollback strategies

  for safer deployments.
• Set up

  end-to-end monitoring systems

  for drift detection, model accuracy tracking, and performance logging.

Agentic AI & LLM Orchestration

• Build and orchestrate

  Agentic AI frameworks

  using

  LangChain, CrewAI, AutoGen, or AutoGPT

  .
• Apply

  ReAct patterns

  for chaining reasoning and tool usage.
• Design LLM agents with

  memory systems

  ,

  reasoning loops

  , and

  multi-agent coordination

  for complex workflows.
• Use

  LLMs as orchestrators

  , integrating APIs, databases, and external tools.

Required Skills & Experience:

• 10+ years of experience in

  data science and ML

  , with 3+ years in a

  leadership

  or

  technical mentorship

  role.
• Strong statistical background: hypothesis testing, confidence intervals, p-values, A/B testing, etc.
  
• Hands-on expertise with

  Azure cloud services

  for data engineering, modeling, and deployment.
• Proven track record of

  deploying machine learning models

  in production (batch and real-time).
• Experience with

  Docker, Kubernetes

  , and

  RESTful API development

  .
• Proficiency in

  Python

  , and experience with frameworks like Scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Hands-on knowledge of

  LangChain, ReAct, or other LLM orchestration frameworks

  .
• Strong understanding of

  LLM tool usage

  , reasoning loops, and agent-based design patterns.