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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** ACI GROUP INC - **Location:** United States - **Experience:** Expert - **Contract:** Temporary to permanent - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Business Analytics Applications, Data Analysis, Automation of Tests, Data Integration, Data Stores, Decision Support Systems, Amazon DynamoDB, Github, Statistical Hypothesis Testing, Python (Programming Language), PostgreSQL, Machine Learning, Natural Language Processing, NumPy, Operational Data Store, Tensorflow, Search Technologies, Systems Integration, Unstructured Data, Web Applications, Data Logging, Pytorch, Large Language Models, Snowflake, Multi-Agent Systems, Prompt Engineering, Deep Learning, Topic Modeling, Generative AI, Backend, Pandas, Build Management, AI Platforms, Scikit Learn, Optimization Algorithms, Performance Monitor, Operational Systems, Machine Learning Operations, Virtual Agents, Functional Programming, Api Gateway, Document Classification, Software Version Control, Devsecops, Unsupervised Learning, Jenkins, Servicenow, Databricks - **Published:** September 29, 2026 - **Apply:** https://www.dice.com/job-detail/370de28e-c7e7-44ae-a478-dbb60ab70535 ## About the Role * Requires a minimum of six (6) years of relevant experience. Bachelor's degree is required. Master's or PhD preferred. * Experience in machine learning and statistical modeling, including supervised and unsupervised learning techniques, deep learning, and a solid foundation in probability, hypothesis testing, and regression. * Demonstrated experience delivering, monitoring, troubleshooting, and supporting production AI systems, including Generative AI, Retrieval-Augmented Generation (RAG), LLM-powered applications, semantic retrieval systems, enterprise AI assistants, or agent-based workflows in enterprise environments. * Hands-on experience designing and implementing RAG architectures, vector databases, embeddings, semantic retrieval, retrieval optimization, or enterprise knowledge engineering solutions. * Experience evaluating AI system performance, including retrieval quality, groundedness, response relevance, hallucination reduction, and task-completion effectiveness. * Experience with AI observability, evaluation frameworks, tracing, telemetry, prompt monitoring, or operational analytics is preferred. * Advanced programming skills in Python, with practical experience using ML and data libraries such as pandas, NumPy, scikit-learn, PyTorch, and TensorFlow is preferred. * Experience with AWS cloud and MLOps tooling, including SageMaker, S3, Glue, Bedrock, Lambda, API Gateway, Airflow, and data stores such as Postgres RDS, Redshift and DynamoDB, along with version control (GitHub) and CI/CD pipelines (e.g., Jenkins) is a plus. * Experience with backend systems and data integration, including data modeling and supporting APIs for web-based and production applications is a plus. * Strong written and verbal communication skills with the ability to explain complex technical concepts to diverse audiences. * Ability to work collaboratively across Product, Architecture, Engineering, UX, QA, DevSecOps, and business stakeholders. * Experience supporting CMS or other federal healthcare agencies is a plus ## Description We are seeking a Sr. Data Scientist to support our Government client. Will create value from structured and unstructured data by applying domain knowledge, statistical analysis, and advanced machine learning techniques to solve complex healthcare challenges. This role emphasizes end-to-end development of machine learning and AI systems, including traditional ML, deep learning, NLP, and modern LLM-based architectures such as Retrieval-Augmented Generation (RAG) and agentic AI systems. Responsibilities * Design, develop, and maintain machine learning, deep learning, and Generative AI solutions using structured and unstructured data to solve complex healthcare and business challenges, including predictive analytics, NLP, Retrieval-Augmented Generation (RAG), agentic AI systems, and enterprise AI applications. * Build and deploy end-to-end ML and AI pipelines on AWS (e.g., SageMaker, S3, Glue, Bedrock) for scalable training, evaluation, inference, and production operations. * Develop and implement advanced NLP solutions, including text classification, entity recognition, topic modeling, and semantic search using models such as BERT and transformer-based architectures. * Design, build, and optimize enterprise Retrieval-Augmented Generation (RAG) systems, including document ingestion, chunking strategies, metadata enrichment, embeddings, vector search, retrieval optimization, and LLM orchestration. * Develop LLM-powered applications, including prompt engineering, evaluation frameworks, and optimization techniques for accuracy, consistency, and cost efficiency. * Design and implement agent-based AI systems utilizing reasoning, planning, memory, tool orchestration, and multi-step workflows to automate complex business processes. * Develop integrations between AI systems and enterprise platforms such as ServiceNow, Snowflake, Databricks, APIs, workflow platforms, and operational systems. * Contribute to enterprise AI platform capabilities including agent orchestration, tool execution frameworks, knowledge retrieval services, AI governance controls, and reusable AI patterns. * Design and implement AI evaluation frameworks and quality methodologies to measure response quality, groundedness, relevance, hallucination rates, task-completion effectiveness, and business value. * Create evaluation datasets, automated testing strategies, benchmarks, and validation pipelines to continuously improve AI system quality and reliability. * Implement AI observability and monitoring capabilities including tracing, telemetry, evaluation logging, prompt tracking, usage analytics, performance monitoring, and cost optimization. * Develop dashboards, operational insights, and analytical tools to communicate findings and support data-driven decision making for technical and non-technical stakeholders. * Implement predictive analytics, statistical modeling, and exploratory data analysis to uncover patterns, trends, and opportunities within healthcare and operational data. * Evaluate model and AI system performance using appropriate statistical, ML, and GenAI metrics and communicate findings in a clear, actionable manner. * Collaborate in an Agile environment with Product, Architecture, Engineering, UX, QA, DevSecOps, Business SMEs, and stakeholders to deliver production-grade AI solutions. ## Related Videos - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Vectorize all the things! 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