Data Scientist
- Compensation
- Not disclosed
- Location
- Cairo, Egypt
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 0+ years
- Category
- Data science
- Posted
Skills
Must have
- Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field.
- 0-3 Years of Experience in Data Science or similar role.
- Proficiency in Python/R, SQL, and familiarity with visualization tools.
- Understanding of basic machine learning algorithms and statistical methods.
- Strong analytical and problem‑solving skills with an eagerness to learn.
- Good communication skills to explain findings to both technical and non‑technical audiences.
Full description
A subsidiary of Al Ahly Capital, Al Ahly Momkn is a fast-evolving Fin-tech company committed to promoting secure, convenient & seamless payment solutions and financial services for business owners, service providers and consumers across Egypt.
At Al Ahly Momkn, we foster a culture of innovation, collaboration, and personal growth.
We believe in empowering our employees to unleash their full potential and make a real impact in their roles. As part of our team, you'll have the opportunity to work on exciting projects, learn from industry experts, and be part of a supportive and inclusive community.
1- Data Collection & Preparation:
- Collect, clean, and pre process data from various sources to support analysis and modeling activities.
- Ensure datasets are structured, reliable, and ready for exploratory and predictive analysis.
- Support data quality checks and assist in improving data consistency.
2- Exploratory Data Analysis (EDA):
- Conduct exploratory data analysis to identify trends, patterns, anomalies, and key business insights.
- Assist in interpreting customer behavior and performance metrics across fin-tech products and services.
- Provide analytical summaries to support decision-making.
3- Predictive Modeling & Machine Learning Support:
- Assist in developing, testing, and evaluating predictive models and machine learning solutions.
- Support feature engineering and model validation processes.
- Measure model performance using appropriate evaluation metrics.
4- Visualization & Reporting
- Create dashboards, visualizations, and reports to communicate insights effectively.
- Present findings in a clear format tailored for both technical and non-technical audiences.
- Contribute to creating recurring performance reporting and ad-hoc analytics outputs
5- Cross-Functional Collaboration
- Collaborate with Product, Tech, Operations, and other business stakeholders to translate business questions into analytical solutions.
- Support the development of data-driven recommendations and actionable insights.
- Participate in meetings and discussions to align analysis with business priorities.
6- Documentation & Continuous Improvement
- Document workflows, methodologies, and analytical processes to ensure clarity and reproducibility.
- Stay up to date with data science tools, techniques, and industry best practices.