Research Scientist

Required Skills & Experience

Strong fundamentals in machine learning concepts\nHands-on experience with deep learning / neural networks and modern AI approaches\nSolid understanding of model evaluation, limitations, and trade-offs\nAbility to explain models and results in simple, intuitive terms\nExperience with Explainable AI (XAI) concepts and techniques\nStrong judgment on when Generative AI adds value—and when it does not\nUnderstanding of agentic AI concepts (design-level grasp is sufficient)\nData & Use Cases\nTime series and temporal data analysis\nNumerical and high-dimensional tabular datasets\nAnomaly detection, trend modeling, and outlier identification\nApplying ML outputs directly to business decision-making\nProgramming & Tools\nPython (practical proficiency; perfection not required)\nPyTorch (preferred over TensorFlow)\nscikit-learn, XGBoost, pandas\nExperience with Python ML libraries in time series and tabular contexts\nDeployment experience on Azure or a comparable cloud platform\nTo avoid: PySpark, Databricks

Job Description

We are seeking a business-oriented Machine Learning Engineer with strong fundamentals in classical ML and modern AI, including Generative AI. This role focuses on applying AI to real-world business problems, particularly in time series, tabular, and document-based data.\nThe ideal candidate is comfortable working autonomously, collaborating across international teams, and translating complex analytical findings into clear, actionable insights for stakeholders.\nKey Responsibilities\nAnalyze complex datasets (time series, tabular, numerical, and temporal data) to identify patterns, trends, anomalies, and outliers\nDesign, build, evaluate, and explain ML models aligned to real business use cases\nBreak down ambiguous or complex business problems into solvable ML tasks\nInterpret model results, analyze errors, and connect outcomes back to business impact\nCommunicate findings clearly to non-technical stakeholders and ask the right follow-up questions\nPropose and iterate on AI-driven solutions based on data insights\nCollaborate closely with cross-functional and international teams in English