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About the Role:
We seek a skilled Data Scientist to join a Saudi Fintech. This role involves building and maintaining data pipelines, designing and managing data warehouses (DWH), and developing reports and dashboards to drive business decisions. The ideal candidate has experience with data modeling, machine learning, and analytics, ensuring that insights are actionable and aligned with business goals.
Key Responsibilities:
Data Management & Engineering
Design, build, and maintain ETL/ELT data pipelines for collecting, processing, and storing structured and unstructured data.
Develop and manage the data warehouse (DWH) architecture to ensure scalability and efficiency.
Integrate and optimize data from multiple sources, including databases, APIs, third-party tools, and business applications.
Ensure data integrity, consistency, and security across all systems.
Business Intelligence & Reporting
Collaborate with business teams to understand data needs and develop dashboards and reports for key performance indicators (KPIs).
Use SQL, Python, R, or BI tools (Tableau, Power BI, Looker, etc.) to analyze and visualize data effectively.
Provide actionable insights to drive business strategies, optimize operations, and improve customer experiences.
Data Science & Advanced Analytics
Apply machine learning and statistical modeling to uncover trends, predict outcomes, and drive strategic decisions.
Implement A/B testing frameworks and experiments to measure business impact.
Optimize algorithms for fraud detection, customer segmentation, demand forecasting, and operational efficiency.
Cross-functional Collaboration
Work closely with engineers to optimize data infrastructure and pipelines.
Partner with business stakeholders to define data-driven strategies and objectives.
Act as a bridge between technical and non-technical teams, ensuring that analytics solutions align with business needs.
Role Requirements
Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
5+ years of experience in data science, analytics, or related fields.
Strong SQL skills and experience working with relational and NoSQL databases.
Proficiency in Python (Pandas, NumPy, Scikit-Learn, etc.) or R for data analysis and machine learning.
Hands-on experience with ETL pipelines, data processing, and data warehousing (e.g., Snowflake, Redshift, BigQuery).
Knowledge of cloud platforms (OCI, GCP, Azure) and experience with data tools like Airflow, DBT, Spark, or Kafka.
Experience with BI tools (Power BI, Tableau, Looker, Metabase, etc.) for data visualization and reporting.
Strong understanding of statistics, machine learning algorithms, and predictive modeling.
Experience in Fin-Tech, banking, or finance is a Plus.
Familiarity with big data technologies (Hadoop, Spark, Databricks, etc.) is a Plus.
Knowledge of data governance, compliance, and security best practices is a Plus.
Experience with real-time analytics and streaming data is a Plus.