What are the responsibilities and job description for the Data Scientist position at P3Hired?
Position Overview
As a Data Scientist, you will have the opportunity to work on high-impact projects that directly influence the financial services industry. You will leverage advanced analytics, machine learning, and natural language processing (NLP) to develop and enhance fraud detection systems, identity verification processes, and customer propensity models. 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.
Desirable Qualifications
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.
