Research Scientist

Machine Learning Engineer

Strong fundamentals in machine learning concepts

Hands-on experience with deep learning / neural networks and modern AI approaches

Solid understanding of model evaluation, limitations, and trade-offs

Ability to explain models and results in simple, intuitive terms

Experience with Explainable AI (XAI) concepts and techniques

Strong judgment on when Generative AI adds value—and when it does not

Understanding of agentic AI concepts (design-level grasp is sufficient)

Data & Use Cases

Time series and temporal data analysis

Numerical and high-dimensional tabular datasets

Anomaly detection, trend modeling, and outlier identification

Applying ML outputs directly to business decision-making

Programming & Tools

Python (practical proficiency; perfection not required)

PyTorch (preferred over TensorFlow)

scikit-learn, XGBoost, pandas

Experience with Python ML libraries in time series and tabular contexts

Deployment experience on Azure or a comparable cloud platform

To 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. The ideal candidate is comfortable working autonomously, collaborating across international teams, and translating complex analytical findings into clear, actionable insights for stakeholders.

Key Responsibilities

Analyze complex datasets (time series, tabular, numerical, and temporal data) to identify patterns, trends, anomalies, and outliers

Design, build, evaluate, and explain ML models aligned to real business use cases

Break down ambiguous or complex business problems into solvable ML tasks

Interpret model results, analyze errors, and connect outcomes back to business impact

Communicate findings clearly to non-technical stakeholders and ask the right follow-up questions

Propose and iterate on AI-driven solutions based on data insights

Collaborate closely with cross-functional and international teams in English