Job Description
AI Analytics Engineer | Pune | INR 21.9-50.8 LPA
Data Science role, mid level, full-time.
Need: M.Tech (AI/ML), 7+ yrs.
Core skills: Machine Learning, Cloud Platforms (AWS/GCP/Azure), Python, Data Science, Data Preprocessing.
If this fits your profile in data science, apply now.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The next logical step from this position is a senior or lead-track role within the same function.
The package is structured with a fixed component, performance bonus, and standard health coverage. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. High performers in this role typically move into a broader ownership scope within 18-24 months.
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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. References may be requested closer to the offer stage rather than at the start of the process.
Data Science role, mid level, full-time.
Need: M.Tech (AI/ML), 7+ yrs.
Core skills: Machine Learning, Cloud Platforms (AWS/GCP/Azure), Python, Data Science, Data Preprocessing.
If this fits your profile in data science, apply now.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. The next logical step from this position is a senior or lead-track role within the same function.
The package is structured with a fixed component, performance bonus, and standard health coverage. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. High performers in this role typically move into a broader ownership scope within 18-24 months.
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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. References may be requested closer to the offer stage rather than at the start of the process.