Purpose of the Role: The Full Stack Developer will be part of Maricos Data Science team and will be responsible for building, deploying, and maintaining scalable applications and technology solutions that enable data science and analytics use cases across the organization. The role will work closely with Data Scientists, Data Engineers, Analytics teams and business stakeholders to convert analytical models, data products and business requirements into intuitive, reliable and production-ready applications. The individual will be expected to work across the application stack including frontend development, backend services and APIs, databases, data pipelines, cloud services, deployment and application monitoring. Qualification & Experience: Bachelor’s degree in Computer Science, Information Technology, Engineering or a related discipline. 2–4 years of hands-on experience in full stack application development. Experience developing and deploying web applications in a production environment. Experience working with cloud platforms such as AWS, Azure or GCP. Hands-on exposure to building data pipelines, APIs and integrations with databases and other enterprise systems. Experience working with data, analytics or Data Science/ML teams would be preferred. Experience in an FMCG, retail, e-commerce or consumer-facing organization would be an advantage but is not mandatory. Knowledge, Skills & Competencies : Full Stack Development Strong programming skills in Python. Experience with backend frameworks such as FastAPI, Flask or Django. Working knowledge of modern frontend technologies such as React.js, JavaScript/TypeScript, HTML and CSS. Ability to develop REST APIs and integrate frontend applications with backend services. Understanding of authentication, authorization and application security principles. Data & Pipeline Development Strong working knowledge of SQL and relational databases. Ability to build and maintain ETL/ELT and data processing pipelines. Experience integrating applications with databases, data warehouses, APIs and cloud storage. Familiarity with data processing libraries and frameworks such as Pandas and PySpark would be an advantage. Understanding of data quality, error handling, logging and pipeline monitoring. Cloud & Deployment Hands-on experience with at least one cloud platform – AWS, Azure or GCP. Familiarity with relevant cloud services for compute, storage, databases, serverless applications and application hosting. Working knowledge of Docker/containerization. Understanding of CI/CD pipelines, Git and standard software deployment practices. Exposure to Kubernetes or other container orchestration platforms would be an advantage but is not mandatory. Data Science / ML Application Integration Ability to work with Data Scientists to integrate analytical and machine learning models into applications. Understanding of how models can be exposed through APIs and consumed by business applications. Familiarity with model deployment, batch processing and real-time inference concepts would be an advantage. Exposure to GenAI/LLM-based applications, APIs or frameworks would be a plus. Core Competencies Strong problem-solving and debugging skills. Ability to translate business requirements into practical technology solutions. Comfortable working in an agile and collaborative environment. Ownership mindset with the ability to independently take features from requirement to production. Strong communication skills and ability to collaborate with both technical and business teams. Willingness and ability to learn new technologies based on evolving business requirements. Job Responsibilities: Develop and maintain full stack applications for Data Science and Analytics use cases. Build APIs and data pipelines to integrate applications, data sources and analytical models. Deploy and manage applications using appropriate cloud services . Partner with Data Scientists to productionize and integrate ML/analytical solutions into business applications. Ensure applications are scalable, secure, reliable and maintainable . Implement testing, version control, CI/CD, monitoring and deployment best practices. Troubleshoot application and pipeline issues and continuously improve performance. Maintain technical documentation and collaborate with relevant technology and business stakeholders.