Junior Data Scientist

CGI•Reston, VA

2d•Hybrid

About The Position

CGI has an immediate need for a Junior Data Scientist to join our team. This is an exciting opportunity to work in a fast-paced team environment supporting one of the largest customers. We take an innovative approach to supporting our client, working side-by-side in an agile environment using emerging technologies. We partner with 15 of the top 20 banks globally, and our top 10 banking clients have worked with us for an average of 26 years!. This role is located at a client site in Reston, VA. A hybrid working model is acceptable. Your future duties and responsibilities: Assist in gathering and analyzing user requirements and translating them into technical tasks. Support the development and maintenance of quantitative models for mortgage and loan risk. Prepare and transform data for analysis, ensuring accuracy and consistency. Work with large datasets in cloud environments under guidance from senior team members. Participate in testing activities and troubleshoot basic data-related issues. Collaborate with technical and business teams to communicate findings clearly. Extract, clean, transform, and analyze large datasets using Python and SQL. Build and experiment with ML models (e.g., decision trees, classification, predictive analytics). Conduct data profiling and gather business requirements from internal stakeholders. Assist in developing analytical insights to support business units (Underwriting, Closing, Delivery, etc.). Collaborate with engineering teams to operate models and tools. Work with AWS cloud environment, including S3, Redshift, RDS, and explore AWS Bedrock for LLM/AI use cases. Required qualifications to be successful in this role: 1–3 years of Data Science experience or strong academic/project background. Proficiency in Python (experience with Pandas, NumPy; Scikit-learn is a plus). Strong SQL skills and understanding of relational databases. Familiarity with AWS services (S3, Lambda, Glue) or willingness to learn. Understanding of fundamental statistical concepts and data modeling techniques. Exposure to software engineering practices (Git, unit testing) is desirable Ability to learn quickly and adapt to new tools and technologies. Good communication skills for working with technical and non-technical stakeholders. Ability to manage tasks with guidance and prioritize effectively. Exposure to risk modeling concepts (Monte Carlo simulations, time-series analysis) is a plus. Familiarity with data visualization tools or frameworks. Relevant coursework or certifications in data analytics, cloud technologies, or quantitative methods. Internship or academic project experience in Python, SQL, or cloud technologies is highly valued. Nice-to-Haves Experience with R programming. Exposure to JSON and XML datasets. Understanding of the mortgage/financial industry. Experience with model deployment or MLOps concepts.

Requirements

1–3 years of Data Science experience or strong academic/project background.
Proficiency in Python (experience with Pandas, NumPy; Scikit-learn is a plus).
Strong SQL skills and understanding of relational databases.
Familiarity with AWS services (S3, Lambda, Glue) or willingness to learn.
Understanding of fundamental statistical concepts and data modeling techniques.
Exposure to software engineering practices (Git, unit testing) is desirable
Ability to learn quickly and adapt to new tools and technologies.
Good communication skills for working with technical and non-technical stakeholders.
Ability to manage tasks with guidance and prioritize effectively.
Exposure to risk modeling concepts (Monte Carlo simulations, time-series analysis) is a plus.
Familiarity with data visualization tools or frameworks.
Relevant coursework or certifications in data analytics, cloud technologies, or quantitative methods.
Internship or academic project experience in Python, SQL, or cloud technologies is highly valued.

Nice To Haves

Experience with R programming.
Exposure to JSON and XML datasets.
Understanding of the mortgage/financial industry.
Experience with model deployment or MLOps concepts.

Responsibilities

Assist in gathering and analyzing user requirements and translating them into technical tasks.
Support the development and maintenance of quantitative models for mortgage and loan risk.
Prepare and transform data for analysis, ensuring accuracy and consistency.
Work with large datasets in cloud environments under guidance from senior team members.
Participate in testing activities and troubleshoot basic data-related issues.
Collaborate with technical and business teams to communicate findings clearly.
Extract, clean, transform, and analyze large datasets using Python and SQL.
Build and experiment with ML models (e.g., decision trees, classification, predictive analytics).
Conduct data profiling and gather business requirements from internal stakeholders.
Assist in developing analytical insights to support business units (Underwriting, Closing, Delivery, etc.).
Collaborate with engineering teams to operate models and tools.
Work with AWS cloud environment, including S3, Redshift, RDS, and explore AWS Bedrock for LLM/AI use cases.