Data Scientist (Synthetic Data Generation (SDG))

Key Responsibilities

Model Development

Design and conduct experiments to evaluate Synthetic Data Generation-SDG models (e.g., DDPM, ARF, Gaussian Copula).

Investigate failure cases and propose new modeling strategies.

Tune hyperparameters and refine architectures.

Feature & Product Development

Collaborate with SWE team to build ML-driven product features.

Develop approaches for constraints handling, imputation strategies, and privacy metrics.

Diagnostics & Debugging

Work with users and engineers to diagnose training failures and poor outputs.

Provide actionable fixes and communicate insights clearly.

Documentation & Knowledge Sharing

Write user-facing documentation explaining models, hyperparameters, and metrics.

Translate complex DS concepts into clear explanations.

Collaboration

Work closely with SWE team (Next.js, FastAPI, AWS) for integration.

Participate in Agile rituals, code reviews, and design discussions.

Required Skills & Qualifications

Bachelor's degree or higher in Computer Science, Data Science, or related field.

2-3 years of relevant experience.

Strong foundation in ML: model development, experimentation, diagnostics.

Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).

Ability to analyze model behavior and design experiments.

Applied Research & Experimentation

Ability to read and translate emerging research into practical prototypes.

Software Engineering

Working knowledge of backend development (FastAPI/Flask).

Familiarity with cloud environments (AWS), Git, CI/CD.

Ability to debug software-level issues affecting ML workflows.

Nice-to-Haves

Experience with PETs, anonymization, synthetic data generation, or differential privacy.

Familiarity with frontend workflows (Next.js/React).

Prior experience in multi-disciplinary product teams.