Full Stack Data Scientist

CGI•Washington, DC

21h•Hybrid

About The Position

CGI is seeking a Full Stack Data Scientist to design, develop, and deploy scalable AI/ML solutions that solve complex business challenges within a highly regulated financial services environment. This role is responsible for the complete machine learning lifecycle, including problem formulation, data preparation, model development, application engineering, deployment, monitoring, and operational support. The ideal candidate combines strong data science expertise with software engineering and MLOps capabilities to build production-ready AI/ML applications. Working closely with business stakeholders, engineering teams, and cloud platform teams, this individual will develop reliable, scalable solutions leveraging AWS-based technologies and modern machine learning practices. Key responsibilities include building and maintaining machine learning models, developing data pipelines and model-driven applications, implementing monitoring and governance controls, and ensuring solutions meet performance, compliance, and operational requirements.

Requirements

5+ years of hands-on experience developing and deploying production-ready machine learning models and AI applications.
Experience supporting solutions in production environments through MLOps practices.
Strong experience designing and deploying machine learning and AI solutions in production environments.
Advanced Python development skills, including experience building maintainable, scalable applications and data pipelines.
Solid background in statistical modeling, predictive analytics, and machine learning algorithms.
Experience working with AWS cloud services such as SageMaker, S3, Redshift, and related compute services.
Hands-on knowledge of MLOps practices including model deployment, version control, CI/CD, monitoring, and retraining strategies.
Proficiency with data processing and analytics tools including Pandas, NumPy, Scikit-learn, TensorFlow, and PySpark.
Strong SQL skills and experience working with large-scale datasets and distributed processing frameworks.
Experience developing real-time and batch-processing AI/ML applications and services.
Understanding of model performance monitoring, data drift detection, and operational reliability best practices.
Familiarity with model governance, validation, auditability, and documentation requirements common in financial services organizations.
Strong analytical thinking, problem-solving ability, and communication skills.
Ability to collaborate effectively with data engineers, software developers, cloud architects, and business stakeholders.
Bachelor's degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, or a related quantitative field required.

Nice To Haves

Master's degree in a related discipline preferred.
Relevant cloud or machine learning certifications (AWS Certified Machine Learning, AWS Certified Solutions Architect, etc.) are beneficial but not required.

Responsibilities

Design, develop, and deploy scalable AI/ML solutions.
Manage the complete machine learning lifecycle: problem formulation, data preparation, model development, application engineering, deployment, monitoring, and operational support.
Build and maintain machine learning models.
Develop data pipelines and model-driven applications.
Implement monitoring and governance controls.
Ensure solutions meet performance, compliance, and operational requirements.
Develop reliable, scalable solutions leveraging AWS-based technologies and modern machine learning practices.
Collaborate with business stakeholders, engineering teams, and cloud platform teams.