Research Associate

At CloudFactory we believe that talent is equally distributed around the world, but opportunity is not. We also believe that the future of work is distributed and on-demand. We are leveling the playing field with technology that effectively shrinks the distance between companies and the world’s massive untapped talent pool. CloudFactory helps companies grow by connecting them to a scalable WorkStream, staffed by a global workforce, that is tightly integrated with their teams, processes and systems. CloudFactory helps companies scale by deeply integrating a global workforce into their teams, processes and systems. By harnessing the massive, untapped talent pool that exists in every corner of our planet, we will be able to maximize the potential of people, companies and ideas — regardless of where they originate.

As a Research Associate, you will work with the Data Science team to develop rule-based and machine learning models for fraud detection and other AI-driven research areas. You will be expected to rapidly prototype, experiment with different machine learning approaches, and stay up to date with the latest advancements in AI. You possess strong mathematical intuition, Python skills, and a passion for AI research. Problem-solving, staying ahead of AI trends, and demonstrating your expertise through research and development define you.

Requirements

Responsibilities

• Apply rule-based and machine learning techniques to develop and enhance fraud detection models.
• Generate comprehensive reports with actionable insights for stakeholders.
• Develop fraud confidence scoring models to assess and prioritize suspicious activities.
• Conduct network analysis to uncover hidden relationships and fraud rings.
• Assist in fraud data labeling to improve supervised learning models.
• Maintain an audit trail, ensuring transparency and traceability of data processes.
• Beyond fraud detection: Contribute to research and development in performance management, GenAI, MLOps, process automation, data pipelines, forecasting, and other prescriptive analytics use cases.

Skills and Qualifications

• A background in Computational Mathematics, Statistics, Computer Science, Data Science, or a related field (or equivalent experience).
• Experience or interest in working with Python’s data science stack (Pandas, NumPy, Scikit-learn) and interactive development tools like Jupyter.
• Familiarity with SQL for data extraction and transformation.
• An understanding of applied statistics, including regression, classification, and clustering—or a strong willingness to learn.
• Curiosity about machine learning, with an eagerness to explore and implement new techniques.
• Experience with (or an interest in learning) data visualization tools like Matplotlib, Seaborn, Plotly, Tableau, or QuickSight.
• A passion for telling stories with data and effectively communicating insights.
• A growth mindset—adaptability and a willingness to learn are just as important as existing skills.