Data Scientist
Job description
About Snapp
Snapp is the pioneer provider of ride-hailing mobile solutions in Iran that connects smartphone owners in need of a ride to drivers who use their private cars offering transportation services. We are ambitious, passionate, engaged, and excited about pushing the boundaries of the transportation industry to new frontiers and be the first choice of each user in Iran.
About the Role
The mission of the Data Scientist in our CRM team is to leverage data and analytical thinking to shape the company's product and business decisions. By applying machine learning techniques and algorithms, the Data Scientist will identify opportunities, recommend solutions, design experiments, and measure impact, ultimately driving improved campaign performance and decision-making processes.
Key Responsibilities
- Model Development: Develop and implement predictive models using AI/ML technologies to improve the accuracy and effectiveness of our internal and external products.
- Data Integration: Integrate additional data sources relevant to the CRM team's goals to enhance modeling, analysis, and decision-making.
- Subject Matter Expertise: Serve as a subject matter expert on machine learning and predictive modeling, sharing knowledge and expertise with the CRM team.
- Python Proficiency: Demonstrate proficiency in Python libraries (Pandas, NumPy, SciPy, Stats Models, scikit-learn, Seaborn, and Matplotlib) for data analysis, modeling, and visualization.
- SQL Expertise: Utilize advanced SQL knowledge to work with relational and non-relational databases, author complex queries, and retrieve/manipulate data.
- Model Lifecycle Management: Successfully navigates the model build life-cycle, including feature selection, optimization, model selection, validation, and ongoing maintenance.
Requirements
- 2+ years of experience in related fields
- Attention to detail
- Multitasking ability
- Team player mentality
- Flexibility
- Advanced SQL knowledge
- Advanced Python programming skills
- Expertise in machine learning and predictive modeling