DATA SCIENTIST

Job Scope: This role will be at the intersection of data science, applied machine learning, and software engineering. You will be involved in: 1. 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. 2. 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. 3. 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. 4. 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: 1. Bachelor’s 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. 3. Applied Research & Experimentation • Familiarity with reading, synthesizing, and ability to translate emerging research into practical prototypes. 4. 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. 5. 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. 6. 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.