Job Title: Data Scientist Architect
Location: Garland, TX / McKinney, TX
Duration: 12+ Months
Type: Long-Term Contract
Position Overview:
We are seeking an experienced Data Scientist Architect to design and lead advanced data science, machine learning, and AI solutions across enterprise environments. The ideal candidate will have a strong combination of data science, machine learning, data architecture, cloud platforms, and AI/ML engineering experience.
The Data Scientist Architect will work closely with business stakeholders, data engineers, software engineers, and technology leadership to define data and AI strategies, develop scalable analytical solutions, and drive machine learning initiatives from concept through production.
Key Responsibilities:
- Design and architect scalable data science, machine learning, and AI solutions aligned with business objectives.
- Lead the development and implementation of advanced machine learning and predictive analytics solutions.
- Translate complex business problems into data-driven analytical and machine learning solutions.
- Design end-to-end data science architectures covering data ingestion, processing, feature engineering, model development, deployment, and monitoring.
- Collaborate with Data Engineers, ML Engineers, Software Engineers, and business stakeholders to deliver production-ready solutions.
- Develop and optimize machine learning models using structured and unstructured data.
- Establish best practices for model development, validation, deployment, monitoring, and lifecycle management.
- Design and implement MLOps processes and frameworks for automated model deployment and monitoring.
- Work with large-scale datasets and develop solutions for data preparation, transformation, feature engineering, and statistical analysis.
- Evaluate and implement appropriate machine learning algorithms and AI technologies based on business requirements.
- Develop solutions using Python and modern data science/ML frameworks.
- Design and integrate data science solutions with enterprise data platforms, APIs, and applications.
- Collaborate with cloud teams to develop scalable AI/ML solutions using AWS, Azure, and/or Google Cloud.
- Establish data quality, governance, security, and model risk management practices.
- Perform exploratory data analysis and communicate insights and recommendations to technical and business stakeholders.
- Provide technical leadership, architectural guidance, and mentorship to data science and engineering teams.
- Stay current with emerging technologies in AI, Generative AI, Machine Learning, Deep Learning, and Data Engineering.
Required Technical Skills:
- Strong experience in Data Science and Data Science Architecture.
- Advanced Python programming skill
- Strong knowledge of Machine Learning, Statistical Modeling, Predictive Analytics, and Data Mining.
- Experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar.
- Strong understanding of data architecture, data pipelines, ETL/ELT, and data processing.
- Experience with SQL and relational/non-relational databases.
- Hands-on experience with cloud-based data and AI platforms.
- Strong understanding of MLOps, model deployment, model monitoring, and ML lifecycle management.
- Experience with distributed data processing technologies such as Spark/PySpark.
- Experience with data visualization and analytical tools such as Power BI, Tableau, or similar.
- Strong understanding of APIs, microservices, data integration, and enterprise architecture.
Preferred Skills:
- Experience with Generative AI, Large Language Models (LLMs), NLP, or Deep Learning.
- Experience with Azure Machine Learning, AWS SageMaker, Google Vertex AI, or equivalent platforms.
- Experience with Databricks and modern cloud data platforms.
- Knowledge of Vector Databases, RAG, embeddings, and AI application architecture.
- Experience implementing CI/CD pipelines for machine learning solutions.
- Knowledge of data governance, security, privacy, and compliance requirements.
- Experience working with large-scale enterprise data environments.
- Strong communication, presentation, problem-solving, and stakeholder-management skills.
Education & Experience:
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
- 8+ years of experience in Data Science, Machine Learning, Data Engineering, or related technology roles.
- Demonstrated experience designing and delivering enterprise-scale data science and AI/ML solutions.
- Proven ability to provide technical leadership and work across business and technology teams.
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