The mission of the Data Scientist in the Supply (Incentive) team is to leverage statistical modeling, machine learning, and data-driven approaches to optimize business decisions and improve campaign performance in a dynamic marketplace.

Model Development: Design, develop, and implement predictive models using machine learning and AI techniques to improve the accuracy, efficiency, and effectiveness of internal and external products.

Data Integration: Identify, integrate, and leverage relevant data sources to enhance modeling capabilities, analytical insights, and data-driven decision-making within the Incentive team.

Data & Business Analytics: Translate complex business requirements into analytical frameworks, develop actionable insights, design experiments, and analyze data to support strategic decisions and business growth.

Subject Matter Expertise: Act as a subject-matter expert in machine learning and predictive modeling, providing analytical guidance and sharing knowledge with the Commercial team and other stakeholders.

Model Lifecycle Management: Own the end-to-end machine learning model lifecycle, including feature engineering, model selection, optimization, validation, deployment support, and ongoing performance monitoring.

Business Mindset: Maintain a strong understanding of business objectives, KPIs, and operational challenges to ensure analytical solutions deliver measurable impact and actionable business value.

Requirements

3+ years of experience in Data Science, Business Analytics, or related fields, with hands-on experience solving business problems through data-driven approaches.

Strong understanding of business logic, KPIs, and data-driven decision-making processes.

Strong attention to detail, commitment to deadlines, ability to manage multiple priorities, and adaptability in a fast-paced environment.

Strong collaborative mindset with the ability to work effectively with cross-functional teams and stakeholders.

Proficiency in Python and data science libraries, including Pandas, NumPy, SciPy, Statsmodels, Scikit-learn, Seaborn, and Matplotlib, for data analysis, modeling, and visualization.

Strong SQL skills with experience working with relational and non-relational databases, writing complex queries, and retrieving and manipulating large-scale datasets.

Solid expertise in machine learning, predictive modeling, and deep learning techniques.

Software & Data Engineering: Familiarity with backend development, APIs, data pipelines, and production programming practices to support the integration and deployment of machine learning models.

Nice to Have

Market & Business Understanding: Familiarity with research methodologies, market dynamics, and competitive analysis, with the ability to connect analytical findings to business opportunities and strategic decisions.

Collaboration & Communication: Strong communication and collaboration skills, with the ability to work effectively with diverse teams and communicate complex analytical concepts to technical and non-technical audiences.