Senior Data Scientist - Supply (Incentive)

About the role

Snapp is hiring a Senior Data Scientist for its Supply (Incentive) team in Tehran. The role uses statistical modeling, machine learning and data-driven approaches to optimize business decisions and improve campaign performance in a dynamic marketplace. The position is on-site.

Key responsibilities

• Design, develop and implement predictive models using machine learning and AI to improve the accuracy, efficiency and effectiveness of internal and external products.
• Identify, integrate and use relevant data sources to strengthen modeling, analytics and decision-making within the Incentive team.
• Translate business requirements into analytical frameworks, design experiments and analyze data to support strategy and growth.
• Act as a subject-matter expert in machine learning and predictive modeling, advising the Commercial team and other stakeholders.
• Own the full machine learning model lifecycle, from feature engineering and model selection to validation, deployment support and performance monitoring.
• Keep business objectives, KPIs and operational challenges in mind so that analytical solutions deliver measurable value.

Required profile

• 3+ years of experience in data science, business analytics or related fields, with hands-on experience solving business problems with data.
• Strong understanding of business logic, KPIs and data-driven decision-making.
• Attention to detail, commitment to deadlines, ability to manage multiple priorities and adapt to a fast pace.
• Collaborative mindset and ability to work with cross-functional teams and stakeholders.

Required skills

• Python and data science libraries such as Pandas, NumPy, SciPy, Statsmodels, Scikit-learn, Seaborn and Matplotlib.
• Strong SQL skills with relational and non-relational databases, complex queries and large datasets.
• Solid expertise in machine learning, predictive modeling and deep learning.
• Familiarity with backend development, APIs, data pipelines and production programming practices.
• Nice to have: familiarity with research methods, market dynamics and competitive analysis; strong communication with technical and non-technical audiences.