Junior Data Scientist

About the Role

As a Data Scientist at Monks, you will support the design, development, and deployment of 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 the EMEA region 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 simple statistical analysis and modeling. 
• Support API authentication and automate 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 work experience in data science, data engineering, or a related field.
• Familiarity with 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.
• Fluent English communication and writing skills.
• Strong analytical and problem-solving skills with the ability to work independently and in a team environment.

Not a must, but a plus:

• Bachelor's or Masters Degree in Data Science. 
• Previous agency or consultancy experience is a plus.
• Professional Cloud Certification.
• Familiarity with marketing analytics tools and data (e.g. Google Analytics, Adobe Analytics).
• Awareness of MLOps, CI/CD, and version control tools.
• Familiarity with social data  (e.g. Instagram, Facebook, TikTok).
• Knowledge of digital advertising industry practices and client verticals (retail, finance, etc.) is a plus.
• Experience with Looker Studio or any visualisation tool.

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