Data Scientist

About Company

Metayb is a fast-growing digital transformation company empowering organizations to thrive in the digital-first era. In just three years, weve built a team of 500+ experts focused on creating seamless customer experiences, boosting operational efficiency, and delivering actionable insights. Our core strengths include Digital Transformation, Data Science, SAP Implementation, Workflow Automation, Finance, and Supply Chain Services, with aspirations to expand into IoT, AI/ML, and Virtual Reality. By leveraging emerging technologies, Metayb aims to be a trusted global partner in delivering impactful, future-ready solutions.

Job Summary

Metayb is looking for an experienced Senior Data Scientist to lead the design, development, deployment, and optimization of enterprise-scale Machine Learning, Operations Research (OR), and Process Mining solutions. This role requires deep expertise in AI/ML, MLOps, cloud platforms, and process analytics to build scalable, production-ready solutions that drive measurable business outcomes.

You will work closely with business stakeholders, data engineers, software developers, and product teams to transform complex business challenges into AI-powered solutions. As a senior individual contributor, you will define technical standards, mentor data scientists, lead architectural decisions, and drive innovation by collaborating with industry and academic partners.

Key Responsibilities

• Lead the design, architecture, and delivery of scalable Machine Learning, Operations Research (OR), and Process Mining solutions from business problem identification through production deployment.
• Collaborate with business stakeholders to conduct discovery workshops, define analytical requirements, and translate business challenges into data-driven solutions.
• Develop, deploy, and optimize advanced Machine Learning models, including classification, clustering, anomaly detection, time-series forecasting, graph analytics, and prescriptive optimization models.
• Apply process mining techniques to analyze event data, identify process inefficiencies, perform root cause analysis, and recommend improvements that enhance business performance and operational KPIs.
• Implement and maintain production-grade MLOps practices, including CI/CD pipelines, model versioning, experiment tracking, automated testing, monitoring, observability, Responsible AI (RAI), and Explainable AI (XAI).
• Partner with Data Engineers and Software Engineers to integrate AI/ML models into cloud-based enterprise platforms such as Celonis, Signavio, Databricks, and other real-time analytics environments.
• Define model evaluation frameworks, conduct performance validation through benchmarking, A/B testing, shadow deployments, and continuously monitor models to ensure accuracy, reliability, and business impact.
• Drive technical excellence by mentoring Data Scientists, conducting design and code reviews, promoting engineering best practices, and contributing to reusable frameworks, documentation, and knowledge-sharing initiatives.
• Collaborate with academic institutions, technology partners, and cross-functional teams to evaluate emerging technologies, lead proof-of-concepts (PoCs), and present data-driven insights and strategic recommendations to business leaders and executive stakeholders.

• Bachelors or Masters degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative discipline.
• 6+ years of experience in Applied Data Science, Machine Learning, or Operations Research.
• Proven experience delivering at least three end-to-end production Machine Learning or Operations Research solutions with measurable business impact.
• Strong understanding of software engineering principles and production-grade ML system development.
• Excellent analytical, communication, stakeholder management, and problem-solving skills.
• Ability to work independently while collaborating effectively across cross-functional teams.

Disclaimer: The job title mentioned in this description is generic and intended for broad categorization purposes. The final designation will be determined based on the candidates performance during the interview process, relevant experience, and alignment with the organizational hierarchy.