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Senior Data Scientist

Soum

Key Facts

Remote From: 
Egypt , Uzbekistan
Category:  Data Scientist
Full time
Senior (5-10 years)
English

Other Skills

  • •
    Mentorship
  • •
    Collaboration
  • •
    Communication

Roles & Responsibilities

  • Proficiency in Python and SQL
  • Hands-on expertise in ML frameworks such as TensorFlow, PyTorch, Scikit-learn
  • Strong knowledge of deployment tools (Docker, Kubernetes, cloud platforms)
  • 5+ years in data science, including hands-on ML model development and deployment

Requirements:

  • Lead the design, development, and deployment of advanced ML models for complex use cases
  • Own end-to-end model deployment processes into production environments using Kubernetes and cloud platforms
  • Act as a custodian of organizational data, ensuring data quality and readiness for advanced analytics
  • Mentor junior data scientists and analysts, providing technical guidance and career development support

Job description

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
    •
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