Job Description
The organisation is seeking a qualified AI Risk Data Scientist to support ongoing initiatives in AI-driven Risk Management. This is a mid level opening based out of Hybrid - Bengaluru, offered on a hybrid basis.
The ideal candidate holds M.Sc (Data Science) and brings roughly 4 years of hands-on exposure to ai-driven risk management. Day-to-day, the work draws heavily on Python, driven Risk Management, Neural Network Architectures, TensorFlow/PyTorch, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 20.0-35.1 LPA, in line with market standards for mid level roles within artificial intelligence (ai).
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. 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.
Learning and certification support is available for tools and frameworks directly relevant to the role. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
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. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
The organisation has historically promoted from within for a majority of its senior and lead openings. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The next logical step from this position is a senior or lead-track role within the same function. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Compensation includes standard statutory benefits along with performance-linked variable pay. 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. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The package is structured with a fixed component, performance bonus, and standard health coverage.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
This role offers a genuine opportunity to build depth in AI-driven Risk Management while working alongside an experienced team.
The ideal candidate holds M.Sc (Data Science) and brings roughly 4 years of hands-on exposure to ai-driven risk management. Day-to-day, the work draws heavily on Python, driven Risk Management, Neural Network Architectures, TensorFlow/PyTorch, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 20.0-35.1 LPA, in line with market standards for mid level roles within artificial intelligence (ai).
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. 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.
Learning and certification support is available for tools and frameworks directly relevant to the role. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
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. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
The organisation has historically promoted from within for a majority of its senior and lead openings. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
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. In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The next logical step from this position is a senior or lead-track role within the same function. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions.
Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Compensation includes standard statutory benefits along with performance-linked variable pay. 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. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. The package is structured with a fixed component, performance bonus, and standard health coverage.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
This role offers a genuine opportunity to build depth in AI-driven Risk Management while working alongside an experienced team.