Data Scientist

Description :

Key Skills :

- Strong programming skills in Python or R

- Experience with machine learning libraries (Scikit-learn, TensorFlow, PyTorch)

- Knowledge of SQL and data querying

- Experience with data analysis libraries (Pandas, NumPy)

- Familiarity with data visualization tools (Matplotlib, Seaborn, Tableau, Power BI)

- Proven experience deploying and optimizing Foundation Models (FMs) using AWS Bedrock, including Bedrock AgentCore for agent orchestration and tool execution

- Hands-on experience implementing Retrieval-Augmented Generation (RAG) patterns using Amazon OpenSearch or Amazon Kendra

- Demonstrated ability to build LLM-powered, agent-driven applications from use-case definition through production deployment on AWS

- Strong understanding of agentic AI concepts, including planning, reasoning, tool use, memory, and multi-agent collaboration

Analytical Skills :

- Strong understanding of statistics and probability

- Experience with predictive modeling and hypothesis testing

- Ability to interpret complex data and derive insights

Data Engineering Awareness :

- Familiarity with big data tools such as Spark, Hadoop, or distributed data platforms

- Understanding of data pipelines and ETL processes

Good to have skills :

- Experience with cloud platforms (AWS, Azure, or GCP)

- Knowledge of MLOps and model lifecycle management

- Experience working with large-scale data platforms

- Experience with feature stores or model monitoring tools

Education & Experience :

- Bachelors or Masters degree in Computer Science, Data Science, Statistics, Mathematics, or a related field

- Typically 37 years of experience in data science, machine learning, or advanced analytics roles

- Demonstrated experience delivering data-driven solutions in production environments.

- Design and implement autonomous AI agents using LLMs, planning algorithms, and decision-making frameworks

- Integrate AI agents into enterprise applications, APIs, and workflows (e.g., copilots, chatbots, automation pipelines)

- Deploy and optimize Foundation Models (FMs) using AWS Bedrock, including prompt orchestration and guardrails

- Implement RAG architectures using Amazon OpenSearch or Kendra for enterprise knowledge access

- Optimize agent behavior using feedback loops, reinforcement learning concepts, and user interaction signals

- Maintain clear documentation covering architecture, Data Science Model, agent logic, design decisions, and dependencies