Job Title : Data Science Consultant - Data Management & Applied AI

Location : Bangalore

Full time role

Roles and Responsibilities :

- Lead or support data annotation projects, focusing on imbalanced datasets and classification quality.

- Design data labeling workflows that support prompt fine-tuning and intent detection improvements.

- Support data augmentation and synthesis to expand training sets for underrepresented categories.

- Perform embedding generation, vectorization, and feature extraction for downstream model use.

- Apply dimensionality reduction and clustering to support cohort modeling and personalization efforts.

- Contribute to tools and processes that generate reusable feature sets for prompt APIs and Copilot skills.

- Fine-tune small/medium scale custom models using annotated datasets and embedding-based features.

- Build unit/offline test frameworks for model behavior validation, especially around intent detection and prompt understanding.

- Conduct prompt-level accuracy evaluations and maintain version-controlled test scripts.

- Develop lightweight custom models tailored to specific use cases such as Live Notes, Meeting Summarization, and Skill Invocation.

- Work with engineering teams to flight models across controlled releases, enabling A/B comparison and performance measurement.

- Document findings, test protocols, and improvement paths across releases.

Required Experience :

- 3+ years of experience in applied data science, ML/NLP workflows, or model operations.

- Proven expertise in Python, Pandas, scikit-learn, PyTorch/TensorFlow, and embedding frameworks.

- Hands-on experience with data labeling platforms, annotation QA, or prompt training pipelines.

- Deep understanding of feature engineering for NLP or structured intent-based models.

- Prior work in unit testing models, offline evaluation, or custom model deployments preferred.

Soft Expectations :

- Strong communication and documentation skills.

- Ability to work independently with clear weekly deliverables.

- Comfort in engaging with cross-functional teams including engineers, product managers, and peer data scientists.

- Time zone alignment with Europe is preferred for coordination with the Prague team.