Data Science Manager

Overview of the role:

The manager data science - finance is responsible for leading the execution of financial data science projects aimed at enhancing financial performance and managing risk through the application of advanced analytics and machine learning techniques. The role involves overseeing a team of data scientists, implementing predictive models, developing dashboards and KPI metrics, and generating financial insights that drive strategic decisions. Success in this role is measured by the ability to deliver actionable analytics solutions that align with business objectives.

What you will do:

Predictive modeling
Implement various predictive models including residual value prediction, credit scoring, delinquency prediction, and collections optimization to improve financial performance and risk management.
Deliver at least 2 use cases successfully demonstrating the application of predictive models.

Data visualization and KPI development
Create financial KPI metrics and risk dashboards using statistical techniques and data visualization libraries.
Develop and maintain 3 comprehensive finance-related dashboards that provide actionable insights to stakeholders.

Financial insights and analysis
Generate and communicate financial insights by leveraging techniques such as time series decomposition, anomaly detection, and stress testing.
Produce 3 actionable financial insights monthly to aid decision-making and strategy formulation.

Required skills to be successful:

• Proficiency in modeling techniques such as ARIMA and LSTM networks for predictive analytics.
• Expertise in machine learning methods including gradient boosting, ensemble methods, and neural networks.
• Strong skills in statistical techniques and data visualization for financial analysis and modeling.
• Ability to effectively communicate complex technical concepts to non-technical stakeholders.

What qualifies you for the role:

• Bachelor's or MSc in financial analytics, computer science, or related field.
• 5+ years of experience in data science and analytics with a strong focus on finance.
• Proficient in Python, SQL, Databricks, and experienced with machine learning libraries including scikit-learn, TensorFlow, PyTorch, ARIMA, SARIMA, and Prophet.
• Hands-on experience with MLOps, Git, and data visualization tools like Matplotlib, Seaborn, and Plotly.