Data Scientist at Tamara

Dubai, Dubai, United Arab Emirates -

Full Time

Start Date

Immediate

Expiry Date

20 Jan, 26

Salary

0.0

Posted On

22 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analysis, Predictive Modeling, Machine Learning, Python, SQL, Big Data Technologies, Cloud Platforms, Statistical Techniques, Fraud Detection, Risk Management, Collaboration, Communication, Problem-Solving, Critical Thinking, A/B Testing, Real-Time Applications

Industry

Financial Services

Description

About Us Tamara is the leading fintech platform in Saudi Arabia and the wider GCC region with a mission to help people make their dreams come true by building the most customer-centric financial super-app on earth. The company serves millions of users in the region and partners with leading global and regional brands such as SHEIN, Jarir, noon, IKEA and Amazon, as well as small and medium businesses. Tamara is Saudi Arabia’s first fintech unicorn and is backed by Sanabil Investments, a wholly owned company by the Public Investment Fund (PIF), SNB Capital, Checkout.com, amongst others. The company operates from its headquarters in Riyadh, with additional regional and global support offices. Your Role We are seeking a highly skilled and motivated Data Scientist for our Fraud team. As a Data Scientist specializing in Fraud, you will be at the forefront of protecting Tamara and its users from fraudulent activities. Your primary focus will be on the end-to-end development of real-time features and sophisticated AI/Machine Learning models to enhance our fraud detection and prevention capabilities for both consumer and merchant transactions. Leveraging your expertise in data analysis, predictive modeling, and machine learning, you will be responsible for transforming complex datasets into actionable insights and automated decisions. You will collaborate closely with engineering, product, and operations teams to build and deploy robust, scalable, and real-time fraud prevention systems. Your work will directly contribute to minimizing financial losses and maintaining the integrity of our platform, ensuring a trustworthy experience for our customers and partners. Your Responsibilities Design, develop, and deploy real-time features and machine learning models to detect and prevent fraud across consumer and merchant transactions. Utilize advanced statistical and machine learning techniques to identify fraudulent patterns, trends, and anomalies in large, complex datasets. Collaborate with data engineers to build and maintain scalable and efficient data pipelines for model training and real-time feature generation. Work closely with the fraud operations team to understand emerging fraud tactics and incorporate their feedback into model improvements. Partner with product and engineering teams to integrate fraud models into our production systems and decision-making workflows. Continuously monitor and evaluate the performance of fraud models, conducting A/B testing and making necessary adjustments to optimize their effectiveness. Communicate complex technical concepts and findings to both technical and non-technical stakeholders in a clear and concise manner. Stay up-to-date with the latest industry trends, technologies, and best practices in fraud detection and machine learning. Your Expertise Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline. 3+ years of hands-on experience in a data science role, with a focus on fraud detection, risk management, or a related area. Proven experience in developing and deploying machine learning models in a production environment, particularly for real-time applications. Strong proficiency in Python and its data science libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch). Advanced knowledge of SQL for data extraction and manipulation. Experience with big data technologies (e.g., Spark, Hadoop) and cloud platforms (e.g., AWS, GCP, Azure) is highly desirable. Solid understanding of machine learning algorithms, including but not limited to logistic regression, gradient boosting, random forests, and neural networks. Excellent analytical, problem-solving, and critical thinking skills. Strong communication and collaboration skills, with the ability to work effectively in a cross-functional team. Fluency in English is required. All qualified individuals are encouraged to apply.

Responsibilities

Design, develop, and deploy real-time features and machine learning models to detect and prevent fraud across consumer and merchant transactions. Collaborate with engineering, product, and operations teams to build and deploy robust fraud prevention systems.