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.(ref : hirist.tech)