This role is for one of Weekday’s clients
Min Experience: 5+ years
Location: Delhi, India
JobType: full-time
We are looking for an experienced Scientific Software Engineer with approximately 5–10 years of relevant professional experience combining applied data science, scientific software development, and deployment of decision-support applications.
The role involves translating complex scientific, engineering, and economic models into robust, scalable, and user-friendly software applications. The ideal candidate will have experience building end-to-end analytical solutions, integrating computational models with backend systems, and developing intuitive graphical user interfaces (GUIs), dashboards, and visualisation tools.
The candidate should be capable of designing modular software architectures that keep analytical models independent of the user interface while ensuring security, maintainability, and accessibility through controlled application interfaces.
Requirements
Key Responsibilities
1. Scientific Software Engineering
•Translate scientific, engineering, and economic models into modular, reusable, and maintainable software applications.
•Develop software solutions that support scientific analysis, computational modelling, and data-driven decision-making.
•Design scalable application architectures that allow analytical models to operate independently of graphical user interfaces.
•Ensure the underlying models and software components remain secure, reliable, and maintainable.
2. GUI & Frontend Development
•Design and develop intuitive, interactive, web-based applications and graphical user interfaces.
•Build dashboards, data visualisations, and analytical tools that enable users to interact with complex models and datasets.
•Create user-friendly interfaces that simplify technical outputs and support effective decision-making.
•Ensure applications are accessible, responsive, and suitable for different user requirements.
3. Backend Development & Model Integration
•Integrate scientific and computational models with application backends and computational engines.
•Develop APIs and application interfaces to manage model execution, data processing, and analytical workflows.
•Build robust backend services that support efficient communication between analytical models, databases, and user interfaces.
•Ensure reliable execution and integration of computational workflows.
4. Data Integration & Management
•Manage structured and unstructured datasets from multiple sources.
•Integrate and process data to support scientific analysis and analytical applications.
•Work with relational and geospatial databases to organise and manage complex datasets.
•Support data processing and integration workflows across application components.
5. Analytical Platforms & Decision-Support Tools
•Develop and deploy interactive, GUI-based decision-support applications and scientific software platforms.
•Build analytical dashboards, visualisation tools, and automated reporting solutions.
•Collaborate with scientific, engineering, and domain experts to translate analytical requirements into functional software.
•Deliver end-to-end applications that connect computational models with intuitive user interfaces.
Must-Have Skills & Qualifications
•Approximately 5–10 years of relevant professional experience in scientific software engineering, applied data science, or computational application development.
•Master's degree in Computer Science, Software Engineering, Computer Engineering, or a related discipline.
•Experience developing software applications for scientific or environmental analysis is preferred.
•Strong scientific software engineering skills, including the ability to translate scientific, engineering, and economic models into modular applications.
•Experience in graphical user interface (GUI) development, particularly interactive web-based applications and dashboards.
•Backend development experience, including API development and computational model integration.
•Understanding of model execution, data processing, and analytical workflows.
•Experience managing and integrating structured and unstructured datasets from multiple sources.
•Familiarity with relational or geospatial databases.
•Ability to develop secure, scalable, modular, and maintainable software architectures.
Good-to-Have Skills
•Experience developing web-based scientific applications.
•Backend application development.
•Development of interactive dashboards, analytical platforms, and automated reporting tools.
•Familiarity with SQL, APIs, and cloud-based data processing.
•Experience building decision-support tools for industry, government, or research institutions.
•Exposure to applied scientific software engineering and computational-modelling assignments.