Senior Data Scientist
Remote Worldwide CairoTashkent Full-time Senior Data Science
At a glance
Senior Data Science role at SOUM. Remote · full-time.
Pay not stated Data Scientist salary
RoleData Science
SenioritySenior
LocationWorldwide
WorkplaceRemote
EmploymentFull-time
PostedSep 21, 2025 · 1y ago
Also hiring inCairo, Tashkent
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Build and refine machine learning models for recommendations, fraud detection, customer groups and demand forecasts
- Take models from design through live deployment, then monitor them and automate retraining and pipeline checks
- Work with data engineers and product teams to keep models reliable, scalable and tied to business needs
- Maintain data quality and explain analytical findings and their business value to stakeholders
- Guide junior data scientists and analysts while helping shape data science direction with engineering and product leaders
What you bring
- At least five years in data science, including practical model building and deployment
- At least three years in analytics, statistical modeling and experimentation
- Strong Python and SQL skills, plus hands-on use of machine learning frameworks
- Production machine learning system experience and working knowledge of deployment and MLOps practices
- Dataform experience is required
Who this fits
This role suits a senior data scientist who can lead production machine learning work and mentor junior colleagues. It is remote for people in Egypt, Uzbekistan or Pakistan, with a Sunday-to-Thursday work week and hours from 9:00 AM to 6:00 PM Saudi time.
From the employer
Role: Senior Data Scientist
Location: Egypt, Uzbekistan, and Pakistan (Remote)
Work Week: Sunday – Thursday
Work Timings: 9:00 AM – 6:00 PM (Saudi Arabian Time Zone)
Overview:
We’re looking for a Senior Data Scientist to lead the development and deployment of advanced machine learning models that power critical business decisions. In this role, you’ll drive end-to-end ownership of ML solutions, from design and optimization to deployment and monitoring in production environments. You’ll also play a key role in shaping our data science strategy, mentoring junior team members, and ensuring that analytics insights translate into measurable business impact. This is a high-visibility role where your expertise will directly influence product innovation and growth.
Role & Responsibilities:
- Model Development and Optimization:
- Lead the design, development, and deployment of advanced ML models for complex use cases, such as recommendation systems, fraud detection, customer segmentation, and demand forecasting.
- Partner with data engineers and product teams to ensure models are scalable, reliable, and aligned with business needs.
- Continuously optimize algorithms for performance, accuracy, and efficiency.
- Deployment and Integration:
- Own end-to-end model deployment processes into production environments using Kubernetes and cloud platforms (AWS, GCP).
- Define and manage MLOps best practices, including model monitoring, automated retraining, and CI/CD for ML pipelines.
- Champion automation of model training, validation, and deployment workflows to improve system reliability.
- Analytics Strategy & Enablement:
- Act as a custodian of organizational data, ensuring data quality, consistency, and readiness for advanced analytics and modeling.
- Translate complex data insights into business impact, clearly communicating ROI to stakeholders.
- Drive adoption of analytics and data-driven decision-making across teams by mentoring and enabling business stakeholders.
- Leadership & Collaboration:
- Mentor junior data scientists and analysts, providing technical guidance and career development support.
- Collaborate closely with engineering and product leadership to shape the company’s data science strategy.
- Stay ahead of emerging AI/ML trends, tools, and research, and advocate for their adoption when relevant.
Requirements:
- Proficiency in Python and SQL, with hands-on expertise in ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
- Strong knowledge of deployment tools (Docker, Kubernetes, cloud platforms) and MLOps best practices.
- Proven ability to design and maintain production-grade ML systems.
- Deep understanding of statistical analysis, hypothesis testing, and data visualization.
- Knowledge of cloud-serverless technologies (AWS Lambda, GCP Functions, Azure Functions).
- Strong familiarity with GCP is a plus.
- Prior experience deploying ML solutions in E-commerce or high-growth environments is highly desirable.
- Familiarity to work with Git and GitHub.
- Dataform is a must
Experience:
- 5+ years in data science, including hands-on ML model development and deployment.
- 3+ years in data analytics, statistical modeling, and experimentation.
- Experience mentoring or leading junior data scientists.
- Exposure to fast-scaling startup or tech environments is a strong plus