Senior Data Scientist

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As a Senior Data Scientist at Monks, you will lead exciting projects focused on designing, developing, and deploying scalable Machine Learning (ML) and Generative AI (GenAI) models aimed at optimising marketing effectiveness. This role requires a data expert with proven experience in deploying production‑grade models on public cloud platforms such as GCP, AWS, or Azure.

You should have strong data literacy with the ability to translate complex technical applications into meaningful business value. Ideally, you have a background in marketing or product data science, coupled with a deep understanding of machine learning, statistics, and cloud technologies. While the role mainly focuses on data science, it also offers opportunities to tap into data engineering projects.

This role spans across the EMEA and MENA regions and involves close collaboration with our Analytics, Strategy, and Solutions Engineering teams. We offer a dynamic and challenging work environment at the forefront of AI‑driven innovation in marketing. If you're passionate about pushing the boundaries of what’s possible, we encourage you to apply!

• Provide technical mentorship and guidance to junior team members.

• Upskill the wider team on the best practices of using AI and data.

• Design, develop, deploy, and manage scalable Machine Learning (ML) and Generative AI (GenAI) models across multiple cloud platforms, including AWS and GCP.

• Build and deploy advanced machine learning models, such as propensity, churn, and attribution models, to drive predictive insights and enhance decision‑making.

• Design and implement scalable GenAI architectures using APIs like ChatGPT and Gemini, integrated with Lang Chain libraries.

• Write optimised SQL queries to extract insights from large, complex datasets, leveraging advanced techniques such as window functions and DDL statements.

• Conduct and interpret statistical tests, causal inference, or ITS models to validate hypotheses and evaluate the impact of tests.

• Authenticate through multiple platform APIs and manage an end‑to‑end automation of ETL processes for data integration and transformation in the cloud.

• Implement efficient data models using tools like dbt and Dataform to convert raw data into structured formats for data warehousing and machine learning applications.

• Collaborate with internal and external stakeholders to understand their business challenges and support them in building tailored AI solutions and roadmaps.

• Navigate and lead the complexities of a data science project, demonstrating the ability to differentiate between various solutions and apply the most suitable approach based on trade‑offs between model accuracy, deployment cost, and data requirements.

• Create detailed documentation and establish best practices for building and deploying machine learning models to ensure consistency and scalability.

• Communicate complex technical concepts and results to both technical and non‑technical stakeholders, including clients.

• Bachelor's or Master's in a quantitative subject such as Data Science, Computer Science, Mathematics, Statistics, or a related field.

• 3+ years of working experience in data science, data engineering, or a related field.

• Experience in designing and deploying cloud solutions on platforms such as GCP or AWS.

• Professional Cloud Certification such as GCP Cloud Architect, Data Engineer, Machine Learning Engineer, or any equivalent.

• Strong proficiency in Python and SQL.

• Solid understanding of statistics, machine learning algorithms, and Gen‑AI applications.

• Expertise in authenticating and authorising API requests using methods such as OAuth and API keys, and troubleshooting…