Analyze complex datasets (time series, tabular, numerical, temporal) to identify patterns, trends, anomalies, and outliers
Design, build, evaluate, and explain ML models aligned to business use cases
Break down ambiguous business problems into solvable ML tasks
Interpret results, analyze errors, and tie outcomes to business impact
Communicate clearly to non-technical stakeholders and ask strong follow-up questions
Propose and iterate on AI-driven solutions based on data insights
Collaborate with cross-functional, international partners in English
Required skills and experience
Strong ML fundamentals; hands-on deep learning/neural networks
Solid model evaluation knowledge (limitations and trade-offs) and ability to explain results simply
Experience with Explainable AI (XAI)
Good judgment on when GenAI adds value; design-level understanding of agentic AI
Programming and tools
Python; PyTorch preferred; scikit-learn, XGBoost, pandas
Experience with ML libraries for time series/tabular use cases
Deployment on Azure or comparable cloud
Please avoid: PySpark, Databricks
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