Job Description
**Chief Deep Learning Scientist** | Bhubaneswar | Leadership - Full-time | INR 20.3-64.4 LPA
**About the role:**
- Position within Deep Learning, under the Machine Learning vertical
- Level: senior level
- Reports to the functional head for Deep Learning
**What you'll do:**
- Handle core deep learning activities on a day-to-day basis
- Use Python regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Python
- Deep Learning
- Statistical Modelling
- TensorFlow/PyTorch
- Cloud Platforms (AWS/GCP)
- MLOps
- Computer Vision/NLP Basics
- Data Preprocessing
- Feature Engineering
- M.Sc (Data Science)
- 15+ years of relevant experience
**Good to know:**
- The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
- Internal job postings are open to employees who've completed a minimum tenure in their current role. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
- Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
- The package is structured with a fixed component, performance bonus, and standard health coverage. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
- Compensation includes standard statutory benefits along with performance-linked variable pay. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
- Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
- Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
- Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
- References may be requested closer to the offer stage rather than at the start of the process. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
- Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Interested candidates should be prepared to walk through relevant past work during the interview process.
- Learning and certification support is available for tools and frameworks directly relevant to the role. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
- There is a defined internal mobility path for people who want to specialise deeper or move into people management. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
**About the role:**
- Position within Deep Learning, under the Machine Learning vertical
- Level: senior level
- Reports to the functional head for Deep Learning
**What you'll do:**
- Handle core deep learning activities on a day-to-day basis
- Use Python regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Python
- Deep Learning
- Statistical Modelling
- TensorFlow/PyTorch
- Cloud Platforms (AWS/GCP)
- MLOps
- Computer Vision/NLP Basics
- Data Preprocessing
- Feature Engineering
- M.Sc (Data Science)
- 15+ years of relevant experience
**Good to know:**
- The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Most days include at least one review checkpoint where progress is discussed against the plan for the week. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
- Internal job postings are open to employees who've completed a minimum tenure in their current role. Benefits include health insurance, provident fund contributions, and paid leave in line with company policy.
- Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
- The package is structured with a fixed component, performance bonus, and standard health coverage. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
- Compensation includes standard statutory benefits along with performance-linked variable pay. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
- Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
- Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
- Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
- References may be requested closer to the offer stage rather than at the start of the process. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
- Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Interested candidates should be prepared to walk through relevant past work during the interview process.
- Learning and certification support is available for tools and frameworks directly relevant to the role. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
- There is a defined internal mobility path for people who want to specialise deeper or move into people management. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.