onsite

Data Scientist (Risk)

Tabby is seeking a Data Scientist specializing in Risk to design, develop, and maintain advanced machine learning models for risk assessment. This role involves monitoring existing models, building data marts, optimizing risk models, and providing data-driven insights to inform strategic decisions.

About the role

Key Responsibilities

Design, develop, and maintain models using advanced machine learning techniques.
Regularly monitor and validate existing models to ensure accuracy and relevance in risk assessment.
Build and manage diverse data marts in BigQuery to support analytical requirements.
Continuously optimize risk models to improve predictive accuracy and operational efficiency.
Provide data-driven insights through ad-hoc analyses to inform strategic decisions.

Skills, Knowledge & Expertise

At least 2 years of experience in roles such as Data Scientist, ML Engineer, or Risk Analyst, with a proven track record of impactful contributions in credit scoring.
Proficiency in Python and experience with key data science and machine learning libraries (e.g., NumPy, Pandas, scikit-learn, CatBoost, XGBoost, LightGBM).
Strong knowledge of PyTorch, with the ability to implement and fine-tune complex machine learning models.
Advanced knowledge of SQL, with the ability to work effectively with large datasets.
Experience in building and deploying end-to-end machine learning solutions that drive measurable business impact.
Hands-on experience with tools like Airflow and Docker for deploying machine learning models into production.
Fluency in English to collaborate effectively with analysts and stakeholders.
Familiarity with cloud platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure for scalable data analysis and model deployment.
Proficiency in visualization tools like Tableau or Power BI to effectively communicate insights and support decision-making.
Prior experience working with financial datasets and a strong understanding of risk management principles.

About the role

Key Responsibilities

Design, develop, and maintain models using advanced machine learning techniques.
Regularly monitor and validate existing models to ensure accuracy and relevance in risk assessment.
Build and manage diverse data marts in BigQuery to support analytical requirements.
Continuously optimize risk models to improve predictive accuracy and operational efficiency.
Provide data-driven insights through ad-hoc analyses to inform strategic decisions.

Skills, Knowledge & Expertise

At least 2 years of experience in roles such as Data Scientist, ML Engineer, or Risk Analyst, with a proven track record of impactful contributions in credit scoring.
Proficiency in Python and experience with key data science and machine learning libraries (e.g., NumPy, Pandas, scikit-learn, CatBoost, XGBoost, LightGBM).
Strong knowledge of PyTorch, with the ability to implement and fine-tune complex machine learning models.
Advanced knowledge of SQL, with the ability to work effectively with large datasets.
Experience in building and deploying end-to-end machine learning solutions that drive measurable business impact.
Hands-on experience with tools like Airflow and Docker for deploying machine learning models into production.
Fluency in English to collaborate effectively with analysts and stakeholders.
Familiarity with cloud platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure for scalable data analysis and model deployment.
Proficiency in visualization tools like Tableau or Power BI to effectively communicate insights and support decision-making.
Prior experience working with financial datasets and a strong understanding of risk management principles.