Sr. Applied Scientist
SkanBengaluruOn-siteDirect career pageEst. 35–65 LPA
Posted 1d ago
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Job description
Structured overview of role & requirements
About This Role
- Lead development of scalable systems that discover and represent end-to-end business processes from noisy, heterogeneous data sources such as event logs, UI traces, and unstructured documents.
- Own architecture and evaluation standards for process representations used by downstream autonomous agents ensuring balance between abstraction, detail, and tractability at scale.
- Drive investigations into system failures, turning complex real-world problems into viable research directions, mentor scientists/engineers, and collaborate cross-functionally to transition methods from prototype to production.
Minimum Requirements
- Strong fundamentals in problem decomposition, algorithmic thinking, and scalable data structures capable of operating over millions of process executions.
- Deep expertise in machine learning and deep learning, including representation learning, sequence modeling, clustering, structured prediction, information retrieval, and generative modeling.
- Experience leading technical research on process discovery, variant detection, noise reduction, and evaluation for complex, noisy data; work experience duration not explicitly mentioned.
- Ability to mentor technical staff and collaborate effectively across research, engineering, and product teams; specific educational qualifications or location requirements not explicitly mentioned.
Ideal Candidate Profile
- Experienced in bridging research and production, capable of making principled architecture and algorithmic tradeoffs under ambiguous, large-scale, noisy problem settings.
- Demonstrates strategic scientific leadership with a strong focus on principled evaluation, system reliability, and maintaining practical scalability across millions of data traces.
- Comfortable working with interdisciplinary teams and mentoring scientists/engineers while driving complex technical problem-solving and critical decision-making processes.