We are seeking an experienced Senior Data Scientist to join our dynamic team. The ideal candidate will bring a deep understanding of statistical modeling, machine learning, and data engineering, and will apply these skills to solve complex business problems and drive strategic decision-making.
Required Qualifications:
- Bachelor's degree in Computer Science or a Master's degree (or higher) in Data Science, Information Systems, Computer Science, or a closely related field. A PhD is preferred.
- At least 7 years of hands-on experience in machine learning, deep learning, and predictive modeling in a commercial setting.
- Prior experience with ML is required.
- Proficiency in Python programming is required.
- Deep understanding of Natural Language Understanding (NLU), Computer Vision, Statistical Modeling, Data Visualization, and advanced Data Science methodologies.
- Experience in extracting insights from text data, including handling non-language tokens.
- Capable of performing thorough error analysis of models and clearly communicating findings to technical and non-technical stakeholders.
- Ability to translate image annotation concepts to text analysis tasks
- Expertise in dimensionality reduction techniques (e.g., PCA) and able to articulate their impact.
- Good understanding of model interpretability, the factors influencing model performance, and strategies for optimization
- Experience with modern deployment and delivery technologies including Kubernetes, Containers, Docker, REST APIs, GraphQL, and Event Streams.
Preferred qualifications:
- Experience applying discrete mathematics, differential equations, deterministic and probabilistic models to real-world problems particularly within Finance, FinTech, or scoring systems.
- Experience in Software Development, Data Engineering, or Data Science programming is highly desirable.
- Expertise in interpreting and validating models through statistical methods, classical machine learning, deep learning architectures, and cutting-edge techniques like Transformer models and attention mechanisms.
- Strong capabilities in data modeling, querying, structuring, and designing data architectures.
- Familiarity with risk assessment frameworks and credit risk modeling.
- Experience within Finance or FinTech domains is a strong plus.