GeoComply is looking for a Data Scientist (Anti-spoofing ML) Intern to develop AI-powered solutions to combat fraud and location spoofing, applying academic knowledge to solve real-world problems in digital trust.
Requirements
- Proficiency: Deep knowledge of and hands-on experience with Python and its data science ecosystem (e.g., scikit-learn, pandas, numpy).
- AI/ML Expertise: A strong understanding of machine learning algorithms, statistical modeling, and the application of AI for data analysis.
- Experience with large datasets and platforms like PySpark is highly valued.
- Analytical & Detail-Oriented: You have a keen eye for detail and the ability to interpret complex data, transforming it into clear, actionable insights.
- Publications or research experience in fraud detection or anomaly detection.
- Experience working in fraud detection.
Responsibilities
- Develop and Implement AI/ML Models: Architect and implement advanced machine learning and deep learning models with a focus on anti-spoofing and anti-fraud.
- Conduct Advanced Statistical Analysis: Perform rigorous statistical analysis to identify anomalies and trends that are indicative of fraudulent activity, contributing to the core logic of our anti-spoofing systems.
- Design and Execute Experiments: Plan and run experiments to evaluate the effectiveness of different AI and machine learning algorithms, ensuring our models are robust and performant.
- Collaborate Cross-Functionally: Work closely with engineering managers, data engineers, and other stakeholders to refine requirements, integrate your models into our ML platform, and ensure your work makes a measurable impact on our products.
- Present Findings: Clearly communicate and present your findings and recommendations to the team through concise reports and presentations.
Other
- Internship term: September- December
- Duration: 4 Months
- Hours: 40 hours per week
- Work: Hybrid 3 days in our Vancouver office
- We are looking for a highly motivated rising senior who is currently pursuing or has recently completed a Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline.
