Machine Learning Engineer

Requirements

• Master’s or Ph.D. in Data Science, Computer Science, Statistics, Quantitative Finance, or a highly quantitative field.
• Minimum of 5 years of professional experience as a Data Scientist or Machine Learning Engineer.
• Strong theoretical and practical foundation in supervised and unsupervised learning, probabilistic programming, ensemble methods, and non-linear regression.
• Extensive experience with forecasting frameworks such as Prophet, ARIMA, DeepAR, Temporal Fusion Transformers, or N-BEATS.
• Experience handling sparse, noisy, or irregular financial datasets.
• Proven experience building simulation frameworks, sensitivity analyses, or Bayesian networks for risk and scenario modelling.
• Mastery of Python and its scientific/ML stack, including Pandas, NumPy, Scikit-Learn, PyTorch/TensorFlow, or JAX.
• Strong software engineering practices, including Git, unit testing, and APIs.
• Experience scaling computations using distributed frameworks such as Spark or Ray.
• Proficiency in SQL and cloud data warehouses such as Snowflake or BigQuery.
• Experience with MLOps orchestration tools such as Docker, MLflow, Airflow, or Kubernetes.
• Experience applying machine learning directly to financial, economic, or operational planning data is preferred.
• Good understanding of corporate finance principles, including budgeting cycles, driver-based planning, cost allocation, and variance attribution, is preferred.