Data Scientist

Requirements:Education: Bachelor's/Master’s/PhD in Computer Science, Data Science, AI, or a related field. Experience: 4 – 5+ years in Data Science and/or ML Engineering with successful project delivery on AWS. Programming: Python (must-have), SQL, Java/C++ (optional). ML Frameworks: TensorFlow, PyTorch, Scikit-learn, PyCaret. AWS Services: SageMaker, Bedrock, Redshift, Glue, Lambda, MWAA, Athena, Step Functions. MLOps: MLflow, Docker, Kubernetes, CI/CD on AWS. Data Viz: Tableau, Power BI, Streamlit.Soft Skills: Strong problem-solving, communication, and business acumen.Responsibilities:Data Science & Advanced Analytics Perform exploratory data analysis (EDA), statistical modelling, and feature engineering. Develop predictive and prescriptive models to drive business insights. Conduct A/B testing and hypothesis testing for model validation. Build interactive dashboards using Power BI or Tableau to generate business insights. Machine Learning Engineering & MLOps Design and train ML models using TensorFlow, PyTorch, Scikit-learn. Implement ML pipelines using Amazon SageMaker Pipelines, Feature Store, and AWS Step Functions. Deploy scalable ML models using SageMaker endpoints, ECS, or EKS. Automate workflows using Amazon MWAA (Managed Airflow) and MLflow on AWS. Optimize model performance, inference speed, and real-time AI integrations. AWS Lake House for Unified Data Platform Utilize AWS Lake Formation, Amazon Redshift, Glue, and Athena for unified data access and processing. Build and optimize end-to-end ML pipelines integrated with the AWS Lake House ecosystem. Collaborate with Data Engineers for seamless data ingestion, transformation, and governance using Glue ETL and DataBrew. Interactive AI Applications & Data Visualization Build real-time AI-powered applications using Streamlit. Design dashboards with Power BI and Tableau to visualize AI/ML outputs. Integrate ML outputs with BI platforms through Redshift or S3/Athena connectors. Software Engineering & Deployment Develop APIs using FastAPI or Flask to expose ML services. Deploy solutions on ECS, EKS, or AWS Lambda. Build automated CI/CD pipelines using CodePipeline, CodeBuild, Terraform, and GitHub Actions. Maintain data pipelines using SQL, PySpark, and AWS Glue. Generative AI & NLP Exploration & Development Use Amazon Bedrock to develop Generative AI applications with foundation models (e.g., Anthropic, Cohere, Meta). Fine-tune and optimize large language models (LLMs) for text generation, summarization, and chatbots. Integrate LLMs with business workflows using AWS API Gateway, Lambda, and other AWS cognitive services. Implement prompt engineering, embeddings with Amazon Titan, and retrieval-augmented generation (RAG) on AWS. Business Collaboration & AI Strategy Collaborate with business users, engineers, and stakeholders to define the AI/ML roadmap. Translate business needs into AI-powered solutions. Communicate insights through effective data storytelling and structured documentation. Stay informed on AWS AI/ML advancements and enterprise AI trends.