Data Scientist / Analyst - GenAI, Analytics & Prototype Application Development

The contractor will support data science and analytics initiatives focused on building practical, business-facing solutions that improve information retrieval, workflow automation, data-driven decision support, and prototype application development. The role will involve working with structured and unstructured technical/business data, developing GenAI-enabled workflows, building lightweight prototype applications, and helping stakeholders evaluate solution quality, usability, and business value.

The contractor will partner with internal technical and business teams to translate user needs into analytical workflows, prototype tools, dashboards, and GenAI-powered applications. Key work may include building retrieval-augmented generation workflows, developing prompt and evaluation approaches, designing user-facing interfaces, creating analytical summaries, automating recurring data analysis tasks, and supporting validation of outputs with subject matter experts., * Develop GenAI-enabled workflows for document search, summarization, question answering, information extraction, and traceable response generation.

• Build prototype applications and dashboards using tools such as Streamlit, Python, and cloud-hosted services.

• Use AWS Bedrock or similar platforms to prototype LLM-based workflows, including prompt design, retrieval workflows, response evaluation, and controlled output generation.

• Use Amazon SageMaker or similar environments for model experimentation, notebook-based analytics, model deployment prototypes, and repeatable analytical workflows.

• Analyze structured and unstructured datasets to identify trends, relationships, risks, gaps, and opportunities for process improvement.

• Design evaluation approaches for GenAI and analytics outputs, including accuracy checks, traceability review, user feedback capture, and quality scoring.

• Collaborate with data engineers to ensure analytical outputs are connected to trusted source data, metadata, and downstream applications.

• Prepare concise summaries, visualizations, and business-facing outputs to support decision-making.

• Support iterative testing with stakeholders and incorporate feedback into improved workflows, tools, and outputs.

Top 3 Must-Have Skill Sets

1. GenAI / LLM Application Development in AWS

A strong candidate should be comfortable working in an AWS-based analytics environment and should have practical familiarity with tools such as Amazon SageMaker, Amazon Bedrock, S3, Athena, Lambda, and related cloud services. The role requires both hands-on technical capability and the ability to communicate clearly with non-technical stakeholders., Hands-on experience developing GenAI workflows using tools such as AWS Bedrock, retrieval-augmented generation, prompt engineering, embedding-based search, response evaluation, and traceable output generation. Familiarity with responsible use of GenAI, source attribution, hallucination reduction, and user-validation workflows is important.

1. Applied Data Science, Analytics & Python Prototyping

Strong Python skills for data analysis, automation, transformation, visualization, and rapid prototype development. Experience with pandas, SQL, notebooks, APIs, data wrangling, statistical analysis, dashboarding, and lightweight app development is required. Familiarity with SageMaker notebooks, experiments, pipelines, or model endpoints is strongly preferred.

1. Prototype Application Development & Stakeholder-Facing Analytics

Ability to build practical user-facing prototypes using Streamlit, Dash, Plotly, or similar tools. The contractor should be able to convert stakeholder requirements into usable interfaces, dashboards, feedback loops, and decision-support workflows.

Years of Experience Required

3-6 years of relevant experience in data science, analytics, GenAI solution development, business analytics, or technical prototype development.

• 3+ years: Able to independently build analyses, dashboards, and basic GenAI prototypes.

• 5-6 years: Able to shape solution design, define validation criteria, guide stakeholder testing, and improve prototype quality based on user feedback.

Education Requirements

• Required: Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, Information Systems, or a related technical field.

• Preferred: Master’s degree or equivalent experience in data science, AI/ML, analytics engineering, applied statistics, or cloud-based analytics.