Dynamic Yield, a Mastercard company, is seeking a Data Scientist II-3 to join our Data Science and Analytics team. In this role, you'll be at the forefront of leveraging vast datasets to derive actionable insights and build innovative machine learning solutions for various financial applications. We're looking for a passionate and intellectually curious individual who can tackle novel challenges and clearly communicate complex findings to diverse audiences.
The Role
As a Data Scientist II-3, you will:
- Data Manipulation & Analysis: Manipulate large datasets and apply various technical and statistical analytical techniques (e.g., OLS, multinomial logistic regression, LDA, clustering, segmentation) to extract valuable insights.
- Machine Learning & Deep Learning: Apply various Machine Learning techniques (e.g., SVM, Random Forest, XGBoost, LightGBM, CATBoost) and Deep Learning techniques (e.g., LSTM, RNN, Transformer) to solve complex analytical problems.
- Model Design & Implementation: Design and implement machine learning models for a range of financial applications, including but not limited to: Transaction Classification, Temporal Analysis, and Risk modeling from structured and unstructured data.
- Model Performance & Improvement: Measure, validate, implement, monitor, and continuously improve the performance of both internal and external-facing machine learning models.
- Innovative Problem Solving: Propose creative solutions to existing challenges that are new to the company, the financial industry, and the field of data science.
- Communication & Presentation: Succinctly and clearly present technical problems and findings to business leaders internally and to clients. You will be a strong, confident, and excellent writer and speaker, capable of effectively communicating your analysis, vision, and roadmap to a wide variety of stakeholders.
- Scalable Solutions: Leverage best practices in machine learning and data engineering to develop robust and scalable solutions.
- Resource Identification: Identify areas where resources fall short of needs and provide thoughtful and sustainable solutions to benefit the team.
All About You
- Experience:3-5 years of experience in data science/machine learning model development and deployments.
- Domain Knowledge (Plus): Exposure to financial transactional structured and unstructured data, transaction classification, risk evaluation, and credit risk modeling is a plus.
- Technical Understanding: A strong understanding of NLP (Natural Language Processing), Statistical Modeling, Visualization, and advanced Data Science techniques/methods.
- Text Analysis: Ability to gain insights from text, including non-language tokens, and apply the thought process of annotations in text analysis.
- Problem-Solving: Proven ability to solve problems that are new to the company, the financial industry, and to data science.
- SQL Skills:SQL / Database experience is preferred.
- Deployment Experience: Experience with Kubernetes, Containers, Docker, REST APIs, Event Streams, or other delivery mechanisms.
- Relevant Technologies: Familiarity with relevant technologies (e.g., TensorFlow, Python, scikit-learn, Pandas, etc.).
- Collaboration: Strong desire to collaborate and ability to come up with creative solutions.
- Industry Experience (Preferred): Additional Finance and FinTech experience is preferred.
- Education: Bachelor's or Master's Degree in Computer Science, Information Technology, Engineering, Mathematics, or Statistics.
Corporate Security Responsibility
Every person working for, or on behalf of, Mastercard is responsible for information security. All activities involving access to Mastercard assets, information, and networks come with an inherent risk to the organization. Therefore, it is expected that the successful candidate for this position must:
- Abide by Mastercard's security policies and practices.
- Ensure the confidentiality and integrity of the information being accessed.
- Report any suspected information security violation or breach.
- Complete all periodic mandatory security training in accordance with Mastercard's guidelines.
