Job Description
An opening exists for a Senior AI Risk Data Scientist to join the Artificial Intelligence (AI) vertical, focused on AI-driven Risk Management. This is a senior level opening based out of Remote (Pan India), offered on a full-time basis.
The ideal candidate holds Ph.D (AI) and brings roughly 13 years of hands-on exposure to ai-driven risk management. Day-to-day, the work draws heavily on Deep Learning, Machine Learning, driven Risk Management, Python, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 35.2-87.1 LPA, in line with market standards for senior level roles within artificial intelligence (ai).
The next logical step from this position is a senior or lead-track role within the same function. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. 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.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. References may be requested closer to the offer stage rather than at the start of the process.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Learning and certification support is available for tools and frameworks directly relevant to the role. High performers in this role typically move into a broader ownership scope within 18-24 months.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Compensation includes standard statutory benefits along with performance-linked variable pay. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
The organisation has historically promoted from within for a majority of its senior and lead openings. Interested candidates should be prepared to walk through relevant past work during the interview process.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.
The ideal candidate holds Ph.D (AI) and brings roughly 13 years of hands-on exposure to ai-driven risk management. Day-to-day, the work draws heavily on Deep Learning, Machine Learning, driven Risk Management, Python, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 35.2-87.1 LPA, in line with market standards for senior level roles within artificial intelligence (ai).
The next logical step from this position is a senior or lead-track role within the same function. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. 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.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. References may be requested closer to the offer stage rather than at the start of the process.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Learning and certification support is available for tools and frameworks directly relevant to the role. High performers in this role typically move into a broader ownership scope within 18-24 months.
Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Compensation includes standard statutory benefits along with performance-linked variable pay. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
The organisation has historically promoted from within for a majority of its senior and lead openings. Interested candidates should be prepared to walk through relevant past work during the interview process.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.