Machine Learning Engineer

On behalf of our client, we are seeking a highly skilled Machine Learning Engineer to join their team in Al Khobar.

The role is responsible for designing, developing, and productionizing machine learning algorithms for financial intelligence systems, with a focus on Cost Variance, Cost Forecasting, Scenario and What-If Analysis, and KPI Variance.

The position combines advanced quantitative data science with robust software engineering, covering the full lifecycle of predictive models from mathematical formulation and prototyping to scalable production pipelines.

The role requires strong expertise in machine learning, statistical modelling, time-series forecasting, Python, and MLOps, with experience in financial, economic, or operational planning data considered highly relevant.

Responsibilities:

• Design, train, and validate advanced machine learning and statistical models for multi-horizon cost forecasting and KPI predictions.
  
• Apply advanced time-series and sequential modelling techniques to capture seasonal patterns, macroeconomic dependencies, and trend shifts in financial data.
  
• Develop simulation engines, including Monte Carlo and stress-testing frameworks, for interactive What-If scenarios.
  
• Build automated anomaly detection and diagnostic models to identify the root causes of variance between planned, forecasted, and actual financial KPIs.
  
• Refactor prototype code into clean, scalable production services.
  
• Deploy and containerize models, orchestrate pipelines, and build monitoring systems to detect feature and model drift.
  
• Partner with corporate finance teams to translate complex statistical outputs into transparent, interpretable insights and interactive strategic dashboards.
  

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.