Data Scientist II

Be at the Forefront of the Agentic AI Revolution At Skan AI, we are pioneering the context engine for human and agentic execution, bringing context from enterprise operators, systems, and processes to power how the world's largest organizations execute their most complex, mission-critical work. Why Skan AI We're in hyper-growth mode at exactly the right moment in history. As enterprises race to adopt agentic AI, we're uniquely positioned to deliver the clear signal they desperately need: a platform that trains and grounds AI Agents in trillions of real execution signals, enabling reliable, compliant automation of their most complex processes. Backed by Dell Technologies Capital and other leading investors, we're the only company that can bridge the gap between AI's promise and enterprise reality, making us perfectly positioned to define the agentic era for modern enterprises. Our diverse, collaborative team of 250+ innovators is solving category-defining challenges at the intersection of AI, process intelligence, and enterprise work. Diverse perspectives fuel breakthrough thinking, cross-functional collaboration is the norm, and our work directly transforms how Fortune 500 companies operate. We are shaping the future of work itself. The Role We are looking for an experienced Data Scientist who loves to get their hands dirty. This is a deeply hands-on role; you will build and own AI/ML models and data pipelines, work directly with large and messy real-world datasets, and be the go-to person when something breaks or doesn't make sense. What You'll Do Hands-On Modelling & Algorithm Development Build, train, evaluate, and improve ML models that power core Skan.ai features including but not limited to task detection, workflow segmentation, behavioral pattern recognition, process discovery, and variant analysis. Own the full modelling lifecycle: data exploration, feature engineering, model selection, hyperparameter tuning and validation. Run disciplined experiments: define clear hypotheses, design evaluation frameworks, and present findings with statistical rigour. Explore and apply techniques from deep learning, NLP, time-series analysis, and unsupervised learning to real process intelligence problems. Write clean, well-tested, production-ready Python code that your teammates can maintain and build on. Data Pipelines & Large-Scale Data Design and build reliable data pipelines that handle high volumes of multimodal enterprise data such as screen telemetry, event logs, clickstreams, and structured process data. Own data quality at every step: define validation checks, detect drift, monitor pipeline health, and fix issues proactively. Collaborate with engineering to optimise ingestion and feature generation workflows for speed, cost, and reliability. Customer Issue Diagnosis & Production Support Investigate and resolve data and model issues that surface in customer environments; trace root causes…