Job Scope

This role will be at the intersection of data science, applied machine learning, and software engineering. You will be involved in:

Model Development

• Design and conduct experiments to evaluate emerging SDG models (e.g., DDPM, ARF, Gaussian Copula).
• Investigate failure cases (e.g., when models fail with certain data types, size, or cardinality).
• Tune hyperparameters, refine architectures, and propose new modeling strategies.

Feature & Product Development

• Collaborate with software engineers to build product features that require ML/DS input (e.g., imputation methods, handling of constraints, preprocessing pipelines).
• Recommend and develop suitable approaches for features like single-/multi-column constraints, imputation strategies, and privacy metrics.

Diagnostics & Debugging

• Work directly with users and the engineering team to diagnose user issues with training failures, poor outputs, or integration challenges.
• Provide actionable fixes and communicate technical insights in a user-friendly way.

Documentation & Knowledge Sharing

• Write user-facing documentation pages. This could include explaining model choice, hyperparameters, and utility/privacy metrics in a user-friendly manner.
• Translate complex technical Data Science concepts into clear, approachable explanations. 5. Collaboration
• Work closely with the SWE team (Next.js, FastAPI, AWS) to integrate the generation engine into production-ready systems.
• Participate in Agile rituals, code reviews, and design discussions.

Requirements

• Bachelors degree or higher in Computer Science, Data Science, Business Analytics or a related field, with at least 2-3 years of relevant professional experience. 2. Core Data Science & ML skillset
• Strong foundation in machine learning, with hands-on experience in model development and experimentation.
• Strong programming proficiency in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Ability to analyze model behavior, diagnose training issues, and design experiments to improve performance.

Applied Research & Experimentation

• Familiarity with reading, synthesizing, and ability to translate emerging research into practical prototypes.

Software Engineering

• Working knowledge of backend development (REST APIs, FastAPI, Flask, or similar).
• Comfortable working with cloud environments (AWS preferred).
• Ability to debug and fix software-level issues when they affect ML workflows.
• Familiarity with Git, CI/CD, and collaborative coding best practices.

Nice-to-Haves

• Experience with privacy-enhancing technologies, anonymisation, synthetic data generation or differential privacy.
• Familiarity with frontend integration workflows (Next.js/React).
• Prior experience working in multi-disciplinary product teams.

Mindset & Collaboration

• Curiosity and willingness to learn new domains (esp. data privacy).
• Strong communication skills to explain technical concepts to both engineers and non-technical stakeholders.
• Inclination to work in a collaborative, fast-moving Agile environment.