> Markdown version of [/jobs/ext/2448108-data-science-architect](https://www.wearedevelopers.com/jobs/ext/2448108-data-science-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Science Architect - **Company:** Mars - **Location:** McKinney, TX, United States - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Cloud Database, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Mining, Data Structures, Data Warehousing, Distributed Computing Environment, Distributed Data Store, Github, Information Systems Security Architecture Professional, Python (Programming Language), Machine Learning, Tensorflow, Standard Sql, Azure Machine Learning, Search Technologies, SQL Databases, Unstructured Data, Data Processing, Google Cloud, Enterprise Software Applications, Data Ingestion, Pytorch, Large Language Models, Apache Spark, IT Architecture, Model Validation, Generative AI, Git, SC Clearance, Data Lakes, Pyspark, Scikit Learn, Kubernetes, Information Technology, Data Analytics, Xgboost, Apache Kafka, Data Management, Machine Learning Operations, Restful APIs, Terraform, Data Pipelines, Docker, Jenkins, Databricks, Microservices - **Published:** August 28, 2026 - **Apply:** https://www.dice.com/job-detail/735fe016-a8d6-4f09-b3b4-d76298f62ffb ## About the Role Secret clearance is NOT required at the time of hire. However, candidates must be eligible and able to obtain a U.S. Secret clearance., * 8+ years of experience in Data Science, Machine Learning, AI, Data Engineering, or related technology disciplines. * Strong experience in Data Science Architecture / Machine Learning Architecture / AI Architecture. * Advanced programming experience with Python. * Strong knowledge of SQL, data modeling, data structures, and algorithms. * Strong understanding of machine learning algorithms, statistical modeling, predictive analytics, and data mining. * Experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar. * Hands-on experience with MLOps, ML lifecycle management, model deployment, monitoring, and CI/CD. * Experience designing scalable data pipelines and distributed data processing solutions. * Strong experience with Apache Spark / PySpark and/or Databricks. * Experience with one or more cloud platforms: AWS, Azure, or Google Cloud Platform. * Knowledge of data platforms such as Data Lakes, Data Warehouses, Lakehouse architectures, and distributed databases. * Experience with REST APIs, microservices, containers, Docker, and Kubernetes is preferred. * Strong understanding of data governance, security, privacy, and enterprise architecture principles. * Excellent analytical, problem-solving, communication, and stakeholder management skills. Preferred / Nice-to-Have Skills * Experience with Generative AI, Large Language Models (LLMs), RAG, NLP, or AI Agents. * Experience with MLflow, Azure Machine Learning, Amazon SageMaker, or equivalent MLOps platforms. * Experience with Kafka, Airflow, dbt, or other data engineering/orchestration technologies. * Knowledge of Vector Databases, embeddings, semantic search, and AI/ML APIs. * Experience with Terraform, Git, Jenkins, GitHub Actions, or Azure DevOps. * Experience working in large enterprise or highly regulated environments. * Experience leading architecture initiatives and mentoring technical teams. Education * Bachelor s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field. * Master s degree is preferred., Secret clearance is not required for this position at the time of hire. Candidates must be eligible and able to obtain a U.S. Secret clearance. ## Description We are seeking an experienced Data Science Architect to design, architect, and lead the development of scalable data science, machine learning, and AI solutions. The ideal candidate will have strong expertise in Data Science, Machine Learning, Python, SQL, cloud technologies, data architecture, and MLOps, with the ability to translate complex business and technical requirements into enterprise-grade analytical solutions. The Data Science Architect will work closely with data scientists, data engineers, software engineers, cloud architects, and business stakeholders to establish scalable architecture, technical standards, and best practices for advanced analytics and AI/ML initiatives. Key Responsibilities * Design and develop end-to-end Data Science, Machine Learning, and AI architectures for enterprise applications. * Define scalable and secure architectures for data ingestion, processing, analytics, machine learning, and model deployment. * Lead the architecture and implementation of predictive analytics, machine learning, statistical modeling, and advanced analytics solutions. * Collaborate with Data Scientists and Data Engineers to develop robust data pipelines and analytical platforms. * Design solutions for both structured and unstructured data and large-scale data processing. * Develop and implement machine learning models using appropriate algorithms and frameworks. * Establish standards and best practices for model development, validation, deployment, monitoring, and lifecycle management. * Design and implement MLOps processes and CI/CD pipelines for machine learning workloads. * Architect cloud-based data science solutions using AWS, Azure, or Google Cloud Platform (Google Cloud Platform). * Work with technologies such as Python, SQL, Spark, Databricks, MLflow, TensorFlow, PyTorch, and Scikit-learn. * Evaluate and recommend data science, AI/ML, and cloud technologies based on scalability, performance, security, and cost. * Ensure data quality, governance, security, privacy, and compliance requirements are incorporated into solution architecture. * Design solutions supporting real-time and batch data processing. * Establish architecture patterns for APIs, microservices, model serving, and integration with enterprise applications. * Monitor and optimize model performance, scalability, reliability, and resource utilization. * Provide technical leadership and mentorship to Data Scientists, ML Engineers, Data Engineers, and development teams. * Work with stakeholders to translate business requirements into technical and data-driven solutions. * Create architecture diagrams, technical documentation, standards, and solution design specifications. * Stay current with emerging technologies in AI, Machine Learning, Generative AI, Data Science, and Cloud Computing. ## Related Videos - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)