Data Scientist

Data Scientist - ML, GenAI & Agentic AI

Education

• • Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field
  • Strong academic foundation in statistics, probability, and machine learning

Core Experience

• • 5-8 years of professional experiencein Data Science, Machine Learning, or Applied AI
  • Hands-on experiencebuilding, training, and deploying ML models in production environments
  • Strong understanding ofdata preprocessing, feature engineering, and model evaluation
  • Experience working on end-to-end ML workflows under guidance of senior/principal team members
  • Ability to translatebusiness and product requirements into data-driven solutions

Machine Learning & MLOps

• • Practical experience usingAmazon SageMaker for:
  • Model training and tuning
  • Model deployment and inference
  • Experience with common ML algorithms usingscikit-learn, TensorFlow, or PyTorch
  • Familiarity withMLOps concepts such as:
  • Model versioning
  • Experiment tracking
  • Basic monitoring and retraining workflows
  • Working knowledge ofDocker and exposure toKubernetes-based deployments
  • Experience collaborating with ML engineers to operationalize models

Generative AI & LLMs

• • Hands-on experience developingLLM-powered applications usingAmazon Bedrock or similar platforms
  • Experience building or contributing to Retrieval-Augmented Generation (RAG) pipelines, including:
  • Document ingestion and chunking
  • Embedding generation
  • Similarity search using vector databases
  • Practical knowledge ofprompt engineering, prompt tuning, and output evaluation
  • Understanding of commonLLM failure modes such ashallucinations and grounding issues
  • Ability to evaluate LLM responses for accuracy, relevance, and safety

Agentic AI (Growing Expertise)

• • Exposure toAgentic AI concepts and frameworks such asAgentCore
  • Experience implementing:
  • SimpleAutonomous agents or workflows
  • Multi-step reasoning with predefined tools
  • Familiarity withtool-calling, agent orchestration, and workflow automation
  • Understanding the importance of:
  • Guardrails
  • Human-in-the-loop mechanisms
  • Logging and observability for agent behavior

Cloud Platform:

• • Working knowledge of keyAWS services, including:
  • S3, Lambda, Redshift, IAM

Professional & Collaboration Skills

• • Strong Python programming skills and experience working with APIs
  • Ability to work effectively in cross-functional teams (product, engineering, analytics)
  • Willingness to learn quickly in a fast-evolving AI landscape
  • Comfortable taking technical guidance and implementing feedback
  • Clear communication of findings, limitations, and model behavior to non-technical stakeholders

Nice to Have

• • Initial exposure to AI governance, compliance, or ethical AI practices
  • Experience with monitoring LLM outputs and basic evaluation frameworks
  • Certifications in AWS, Machine Learning, or Data Science
  • Prior experience in enterprise or cloud-native environments