Manhattan Construction Group
AI & Data Science Engineer (Tulsa)
Manhattan Construction Group, Tulsa, Oklahoma, United States, 74145
About the Role
We are seeking an
AI & Data Science Engineer
to help build and operationalize the next generation of data, analytics, and AI capabilities within our organization. This hybrid role blends data science, AI engineering, and data engineering within a primarily Microsoft-centric environment. The successful candidate will design machine learning models, build data pipelines, implement AI solutions, and deploy production-ready analytics that support strategic business decision-making.
Responsibilities:
Data Engineering & Analytics Build and maintain ETL/ELT data pipelines using SQL, Python, and cloud data services. Work with
SQL Server , Azure databases, and data warehouses to ensure high quality data for analytics and AI. Create analytical datasets for dashboards and insights delivered through
Power BI . Translate complex model outputs into clear business insights and recommendations.
Data Science & Machine Learning Develop and validate predictive models. Perform data exploration and statistical analysis across structured and unstructured data. Apply responsible AI practices, including model explainability and performance monitoring.
AI Engineering & Generative AI Develop AI-driven applications such as chatbots, copilots, recommendation systems, or natural language interfaces. Integrate AI models with business applications through APIs, automation tools, or custom services.
Model Deployment Deploy models into production using Azure-based workflows, CI/CD pipelines, or containerized services. Implement model monitoring, retraining, and version management. Collaborate with IT and development teams to ensure scalability, security, and maintainability of deployed solutions.
Collaboration & Business Partnership Work closely with business leaders to identify high-value opportunities for AI and advanced analytics. Communicate technical concepts in a clear, actionable manner to both technical and non-technical audiences. Help establish standards, documentation, and reusable frameworks for AI and analytics initiatives.
Qualifications:
Required Bachelors or Masters degree in Data Science, Computer Science, Engineering, Statistics, or related field; or equivalent experience. 25+ years of experience in data science, AI/ML engineering, or related data-driven roles. Strong proficiency in
Python
and
SQL . Experience developing and deploying models using
Azure Machine Learning
or similar cloud platforms. Experience with
Power BI
or another enterprise BI tool. Strong understanding of data modeling, machine learning algorithms, and statistical methods. Experience working with
Microsoft Azure
or other cloud ecosystems.
Preferred Experience integrating or fine-tuning models using
Azure OpenAI Service
or other large language model platforms. Familiarity with model deployment tools and techniques (e.g., CI/CD pipelines, automated retraining). Experience building APIs or microservices to expose machine learning models. Knowledge of modern data lake, warehouse, or data platform concepts.
Key Competencies: Strong analytical and problem-solving abilities. Excellent written and verbal communication skills. Ability to work collaboratively across IT and business teams.
Curiosity, innovation mindset, and willingness to stay current with emerging AI technologies.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, disability or protected veteran status.
AI & Data Science Engineer
to help build and operationalize the next generation of data, analytics, and AI capabilities within our organization. This hybrid role blends data science, AI engineering, and data engineering within a primarily Microsoft-centric environment. The successful candidate will design machine learning models, build data pipelines, implement AI solutions, and deploy production-ready analytics that support strategic business decision-making.
Responsibilities:
Data Engineering & Analytics Build and maintain ETL/ELT data pipelines using SQL, Python, and cloud data services. Work with
SQL Server , Azure databases, and data warehouses to ensure high quality data for analytics and AI. Create analytical datasets for dashboards and insights delivered through
Power BI . Translate complex model outputs into clear business insights and recommendations.
Data Science & Machine Learning Develop and validate predictive models. Perform data exploration and statistical analysis across structured and unstructured data. Apply responsible AI practices, including model explainability and performance monitoring.
AI Engineering & Generative AI Develop AI-driven applications such as chatbots, copilots, recommendation systems, or natural language interfaces. Integrate AI models with business applications through APIs, automation tools, or custom services.
Model Deployment Deploy models into production using Azure-based workflows, CI/CD pipelines, or containerized services. Implement model monitoring, retraining, and version management. Collaborate with IT and development teams to ensure scalability, security, and maintainability of deployed solutions.
Collaboration & Business Partnership Work closely with business leaders to identify high-value opportunities for AI and advanced analytics. Communicate technical concepts in a clear, actionable manner to both technical and non-technical audiences. Help establish standards, documentation, and reusable frameworks for AI and analytics initiatives.
Qualifications:
Required Bachelors or Masters degree in Data Science, Computer Science, Engineering, Statistics, or related field; or equivalent experience. 25+ years of experience in data science, AI/ML engineering, or related data-driven roles. Strong proficiency in
Python
and
SQL . Experience developing and deploying models using
Azure Machine Learning
or similar cloud platforms. Experience with
Power BI
or another enterprise BI tool. Strong understanding of data modeling, machine learning algorithms, and statistical methods. Experience working with
Microsoft Azure
or other cloud ecosystems.
Preferred Experience integrating or fine-tuning models using
Azure OpenAI Service
or other large language model platforms. Familiarity with model deployment tools and techniques (e.g., CI/CD pipelines, automated retraining). Experience building APIs or microservices to expose machine learning models. Knowledge of modern data lake, warehouse, or data platform concepts.
Key Competencies: Strong analytical and problem-solving abilities. Excellent written and verbal communication skills. Ability to work collaboratively across IT and business teams.
Curiosity, innovation mindset, and willingness to stay current with emerging AI technologies.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, disability or protected veteran status.