Data Science Manager

Role Overview

The Data Science Manager for Finance is tasked with leading financial data science initiatives aimed at improving financial outcomes and mitigating risks using advanced analytics and machine learning. This leadership position entails managing a team of data scientists, deploying predictive models, crafting dashboards, defining key performance indicators (KPIs), and producing financial insights to support strategic decision-making. Success hinges on delivering practical analytics tools that align with the company's business goals.

Key Responsibilities

• Develop and deploy predictive models such as residual value estimation, credit scoring, delinquency forecasting, and optimization of collection processes to enhance financial results and risk controls.
• Successfully deliver a minimum of two use cases demonstrating predictive model applications.
• Design financial KPIs and risk-oriented dashboards utilizing statistical methods and visualization libraries.
• Create and sustain at least three extensive finance dashboards that offer actionable insights to stakeholders.
• Perform financial analysis using methods like time series decomposition, anomaly detection, and stress testing to derive insights.
• Generate a minimum of three actionable financial insights each month to facilitate informed decisions and strategy development.

Required Qualifications and Skills

• Bachelor's or Master's degree in Financial Analytics, Computer Science, or related discipline.
• Five or more years of experience in data science with a strong emphasis on financial analytics.
• Proficiency in Python and SQL programming languages and familiarity with Databricks platform.
• Expert knowledge of machine learning frameworks including scikit-learn, TensorFlow, and PyTorch, and experience with time series models such as ARIMA, SARIMA, and Prophet.
• Experience implementing MLOps practices and version control using Git.
• Strong capabilities in statistical analysis and data visualization tools including Matplotlib, Seaborn, and Plotly.
• Ability to translate complex technical information into clear communication for non-technical stakeholders.