Data Scientist

Please note that we will never request payment or bank account information at any stage of the recruitment process. As we continue to grow our teams, we urge you to be cautious of fraudulent job postings or recruitment activities that misuse our company name and information. Please protect your personal information during any recruitment process. While Monks may contact potential candidates via LinkedIn, all applications must be submitted through our official website ( monks.com/careers ). About the Role As a Data Scientist at Monks, you will work on designing, developing, and deploying Machine Learning (ML) and Generative AI (GenAI) models aimed at optimising marketing effectiveness. You will collaborate closely with senior data scientists to build scalable solutions, leveraging public cloud platforms such as GCP, AWS, or Azure. You should have a strong foundation in data literacy and the ability to interpret technical concepts into practical business insights. Ideally, you have some experience in marketing or product data science, coupled with a strong foundation 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! Responsibilities: Assist in designing, developing, and deploying Machine Learning (ML) and Generative AI (GenAI) models on cloud platforms such as AWS and GCP. Support the development and implementation of predictive models, such as propensity and churn models, to drive predictive insights and enhance decision-making. Contribute to the design of GenAI architectures using APIs like ChatGPT and Gemini, integrated with LangChain libraries. Write SQL queries to extract insights from large datasets Conduct statistical analysis, causal inference, and ITS models. Support in API authentication and automating ETL processes for data integration and transformation in cloud environments. Contribute to implementing and managing data models using tools like dbt and Dataform to convert raw data into structured formats for data warehousing and machine learning applications. Learn to navigate the lifecycle of a data science project, understanding trade-offs between model accuracy, deployment cost, and data availability. Follow and contribute to the documentation and best practices for building and deploying machine learning models to ensure consistency and scalability. Communicate basic technical concepts and results to both technical and non-technical stakeholders, including clients. About You The essentials: Bachelor's or Master's in a quantitative subject such as Data Science, Computer Science, Mathematics, Statistics, or a related field. 1+ years of working experience in data science, data engineering, or a related field. Familiarity in designing and deploying cloud solutions on platforms such as GCP or AWS. Associate Cloud Certification such as GCP Cloud Architect, Data Engineer, or any equivalent. Strong proficiency in Python and SQL. Solid understanding of statistics, machine learning algorithms, and Gen-AI applications, with a strong interest in learning more. Good understanding of APIs, including authentication methods (e.g., API keys, OAuth), with a curiosity to explore and troubleshoot. Awareness of data modelling tools such as dbt or Dataform. Good understanding of data warehousing and ETL concepts, with exposure to tools like Amazon Redshift or Google BigQuery being beneficial. Fluent English communication and written skills. Strong analytical and problem-solving skills with the ability to work independently and in a team environment. Not a must, but a plus:…