Data Scientist

Responsibilities

• Design and run experiments to evaluate emerging generative and statistical models (e.g., diffusion-based, autoregressive, probabilistic models).
• Analyze model performance, identify failure cases, and improve robustness across different data types and structures.
• Tune hyperparameters, refine architectures, and propose new modeling approaches.
• Collaborate with engineers to build ML-powered product features (e.g., data imputation, constraint handling, preprocessing pipelines).
• Develop and recommend approaches for features such as multi-column constraints, data quality handling, and evaluation metrics (e.g., utility, privacy).
• Diagnose and resolve issues related to model training, output quality, and system integration.
• Translate technical findings into clear, user-friendly insights and documentation.
• Work closely with cross-functional teams to integrate ML components into production systems.
• Participate in Agile development processes, including design discussions and code reviews.

Requirements

• Minimum 2 years of hands-on experience in data science or machine learning roles.
• Strong foundation in machine learning, with experience in model development and experimentation.
• Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Ability to analyze model behavior, troubleshoot issues, and improve performance through experimentation.
• Experience translating research or new techniques into practical implementations.
• Working knowledge of backend development (e.g., REST APIs, FastAPI, Flask).
• Familiarity with cloud platforms (AWS preferred) and software development practices (Git, CI/CD).
• Strong communication skills and ability to work with both technical and non-technical stakeholders.
• Comfortable working in a fast-paced, collaborative Agile environment.