Data Scientist

Overview:

Your work will drive innovation and help create scalable, high-performance solutions that solve critical business challenges.

You will collaborate closely with senior data scientists, product development teams, and business stakeholders to understand real-world problems and deliver data-driven solutions. This is a great opportunity to gain hands-on experience with cutting-edge technologies in a rapidly growing fintech company.

The ideal candidate is passionate about solving business challenges using data science, particularly in the financial services sector. You should have a strong analytical mindset, excellent programming skills, and the ability to apply machine learning techniques to real-world business problems.

• Sponsorship is not available for this role now or in the future. This role is based out of the McLean, VA office with flexibility for occasional remote work.

Responsibilities:

• Model Development & Improvement: Design, develop, and deploy machine learning models to solve business problems, with a focus on fraud detection, identity verification, and consumer behavior modeling.
• Data Wrangling & Preparation: Collect, clean, and preprocess large datasets for model development, ensuring data quality and feature engineering to improve model performance.
• Advanced Analytics & Machine Learning: Apply machine learning, NLP, and entity disambiguation techniques to build models that can identify patterns and anomalies in financial transactions and user behaviors.
• Collaborative Problem-Solving: Work with cross-functional teams, including data scientists, engineers, and business stakeholders, to identify opportunities for model improvement and enhance existing solutions.
• Experimentation & Evaluation: Design and run experiments to evaluate model performance, develop baselines, and analyze results to improve accuracy, efficiency, and scalability.
• Continuous Learning: Stay current with the latest trends in AI, machine learning, and data science and apply this knowledge to improve models and methodologies.
• Fraud Prevention & Risk Analysis: Work closely with the counter-fraud team to develop data-driven strategies for identifying and mitigating financial fraud risk.

Qualifications:

• Master's degree in Data Science, Business Analytics, Computer Science, or a related field.
• 3+ years of hands-on experience in building machine learning models for business applications.
• Experience with data preprocessing, feature engineering, and model validation.
• Familiarity with statistical modeling and machine learning techniques, such as regression, classification, clustering, and anomaly detection.
• Proficiency in programming languages such as Python and R for data analysis and model development.
• Experience with NLP or entity disambiguation models and working with text-based data.
• Familiarity with version control (Git) and agile development methodologies.
• Strong understanding of statistical concepts such as hypothesis testing, confidence intervals, and error analysis.
• Experience preparing and cleaning large datasets, handling missing data, and ensuring data quality.
• Strong written and verbal communication skills with the ability to explain technical concepts to non-technical stakeholders.
• High level of accuracy and attention to detail when working with data and developing models.
• Experience with deep learning techniques or advanced NLP models.
• Familiarity with cloud platforms (AWS, GCP, Azure) for model deployment and scaling.
• Experience working in the financial services or fintech industry.
• Knowledge of fraud detection and risk analysis techniques.