Job Description
Artificial Intelligence (AI) is looking for an experienced Director – AI Risk Management to take ownership of the AI-driven Risk Management charter at a senior level. This is a role for someone who has already operated at scale and is comfortable setting direction rather than simply following one.
The successful candidate will bring approximately 9 years of progressively senior experience, holding M.Sc (Data Science), with a demonstrated track record across Statistical Modelling, Model Deployment (MLOps), TensorFlow/PyTorch, Neural Network Architectures, Data Preprocessing. Beyond individual capability, this role expects the person to mentor junior team members and represent the function in cross-departmental discussions.
Reporting relationships will be defined based on the final scope agreed at offer stage, and the role is based out of Mumbai on a hybrid arrangement. Compensation is benchmarked at INR 29.2-95.8 LPA, reflecting the seniority and scope of the position.
Success in this role will be measured less by task completion and more by the quality of judgement applied to ambiguous, high-stakes situations within ai-driven risk management.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
The next logical step from this position is a senior or lead-track role within the same function. Interested candidates should be prepared to walk through relevant past work during the interview process.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. 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.
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. Relocation assistance may be extended for candidates moving from a different city, subject to approval. 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. 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.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
The successful candidate will bring approximately 9 years of progressively senior experience, holding M.Sc (Data Science), with a demonstrated track record across Statistical Modelling, Model Deployment (MLOps), TensorFlow/PyTorch, Neural Network Architectures, Data Preprocessing. Beyond individual capability, this role expects the person to mentor junior team members and represent the function in cross-departmental discussions.
Reporting relationships will be defined based on the final scope agreed at offer stage, and the role is based out of Mumbai on a hybrid arrangement. Compensation is benchmarked at INR 29.2-95.8 LPA, reflecting the seniority and scope of the position.
Success in this role will be measured less by task completion and more by the quality of judgement applied to ambiguous, high-stakes situations within ai-driven risk management.
The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
The next logical step from this position is a senior or lead-track role within the same function. Interested candidates should be prepared to walk through relevant past work during the interview process.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. 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.
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. Relocation assistance may be extended for candidates moving from a different city, subject to approval. 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. 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.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.