Data Science Architect

Quantum Technologies. LLC; Dallas (TX); On-site; USD 150,000 - 190,000; Full time

Job summary

Quantum Technologies. LLC seeks a seasoned Data Science Architect to design and lead scalable data science, ML, and AI solutions across the enterprise.

You will establish architecture standards and collaborate with data scientists, engineers, and business stakeholders to deliver robust analytics platforms. The role emphasizes end-to-end model development, MLOps, CI/CD for ML workloads, and cloud-based data architectures on AWS, Azure, or Google Cloud.

Qualifications

• 10+ years of experience in data science, ML, or data engineering.
• Proven track record in data architecture and ML model lifecycle.
• Strong programming and cloud architecture skills.
• Experience leading enterprise analytics initiatives.

Responsibilities

• Design end-to-end Data Science, ML, and AI architectures for enterprise apps.
• Define scalable, secure data ingestion, processing, and analytics pipelines.
• Lead deployment of predictive analytics and ML models across environments.
• Collaborate with scientists, engineers, and stakeholders to build platforms.
• Establish standards for model development, validation, and monitoring.

Skills

Python

SQL

ML/AI Architecture

MLOps

Spark/PySpark

Cloud Platforms (AWS/Azure/GCP)

Data Pipelines

REST APIs

Docker/Kubernetes

Communication

Education

Bachelor's degree in Computer Science or related field

Tools

Databricks

TensorFlow

PyTorch

Scikit-learn

MLflow

XGBoost

Job 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.

Required Skills & Experience:

• 9+ 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 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.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.