Job Description
## Principal Research Scientist – AI
**Department:** Artificial Intelligence & Machine Learning | **Industry:** Academic Research
**Location:** Gurugram | **Job Type:** Full-time | **CTC:** INR 27.8-49.8 LPA
### Role Overview
This position calls for a Principal Research Scientist – AI who can take ownership of core responsibilities within our Artificial Intelligence & Machine Learning team. The role is positioned at the senior level and reports into the broader Artificial Intelligence & Machine Learning charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to artificial intelligence & machine learning.
- Apply working knowledge of Machine Learning and Citation Management (EndNote/Zotero) to daily execution.
- Coordinate with cross-functional stakeholders within Academic Research.
- Maintain documentation and reporting as required by the team lead.
- Flag risks and blockers early rather than at review checkpoints.
### Requirements
- M.Phil or equivalent qualification.
- 16 years of relevant experience (senior level).
- Working proficiency in: Machine Learning, Citation Management (EndNote/Zotero), Research Ethics, Artificial Intelligence, Qualitative Analysis, Statistical Software (SPSS/R), Critical Thinking, Grant Proposal Writing.
### Additional Notes
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Compensation includes standard statutory benefits along with performance-linked variable pay. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. High performers in this role typically move into a broader ownership scope within 18-24 months. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. References may be requested closer to the offer stage rather than at the start of the process. Mornings are usually spent on planning and status reviews, while the rest of the day is given over to hands-on delivery and problem-solving. The organisation has historically promoted from within for a majority of its senior and lead openings. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Internal job postings are open to employees who've completed a minimum tenure in their current role. Relocation assistance may be extended for candidates moving from a different city, subject to approval. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. The package is structured with a fixed component, performance bonus, and standard health coverage.
**Department:** Artificial Intelligence & Machine Learning | **Industry:** Academic Research
**Location:** Gurugram | **Job Type:** Full-time | **CTC:** INR 27.8-49.8 LPA
### Role Overview
This position calls for a Principal Research Scientist – AI who can take ownership of core responsibilities within our Artificial Intelligence & Machine Learning team. The role is positioned at the senior level and reports into the broader Artificial Intelligence & Machine Learning charter.
### Key Responsibilities
- Own end-to-end delivery of tasks related to artificial intelligence & machine learning.
- Apply working knowledge of Machine Learning and Citation Management (EndNote/Zotero) to daily execution.
- Coordinate with cross-functional stakeholders within Academic Research.
- Maintain documentation and reporting as required by the team lead.
- Flag risks and blockers early rather than at review checkpoints.
### Requirements
- M.Phil or equivalent qualification.
- 16 years of relevant experience (senior level).
- Working proficiency in: Machine Learning, Citation Management (EndNote/Zotero), Research Ethics, Artificial Intelligence, Qualitative Analysis, Statistical Software (SPSS/R), Critical Thinking, Grant Proposal Writing.
### Additional Notes
The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. Compensation includes standard statutory benefits along with performance-linked variable pay. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. High performers in this role typically move into a broader ownership scope within 18-24 months. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. References may be requested closer to the offer stage rather than at the start of the process. Mornings are usually spent on planning and status reviews, while the rest of the day is given over to hands-on delivery and problem-solving. The organisation has historically promoted from within for a majority of its senior and lead openings. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. Internal job postings are open to employees who've completed a minimum tenure in their current role. Relocation assistance may be extended for candidates moving from a different city, subject to approval. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. The package is structured with a fixed component, performance bonus, and standard health coverage.