Machine Learning Scientist

Machine Learning Scientist / Engineer Financial Intelligence

Location: Al Khobar Saudi Arabia Position Type: 12-Month Contract (Initial term with high potential to extend)

Critical Requirements:

• Residency: Candidates must be currently resident in Saudi Arabia.
• Work Authorization: Must possess valid right to work in KSA and/or a transferable Iqama. Applications without this cannot be considered.

About the Client & Role

We are an elite recruitment agency partnering with a forward-thinking organization to find a versatile Machine Learning Scientist / Engineer to design build and productionize the algorithms powering their next-generation financial intelligence systems.

In this hybrid role you will sit at the perfect intersection of quantitative data science and robust software engineering. You will own the entire lifecycle of predictive modelsfrom mathematically formulating hypotheses and prototyping advanced models to deploying scalable production pipelines. Your primary focus will be applying ML and time series forecasting to automate Cost Variance Cost Forecasting Scenario & What-If Analysis and KPI Variance.

Key Responsibilities

• Advanced Predictive Modeling: Design train and validate sophisticated machine learning architectures and classical statistical models tailored for multi-horizon cost forecasting and KPI predictions.
• Time Series & Sequential Modeling: Leverage advanced time series techniques (e.g. Deep Learning State-Space models hierarchical forecasting) to capture complex seasonal patterns macroeconomic dependencies and trend shifts in high-dimensional financial data.
• Scenario & What-If Simulation: Develop simulation engines (such as Monte Carlo and stress-testing frameworks) that allow financial planners to run interactive What-If scenarios modeling the ripple effect of operational and market changes on cost structures.
• KPI & Cost Variance Analysis: Build automated anomaly detection and diagnostic models to pinpoint the root causes of variance between planned forecasted and actual financial KPIs.
• Production Pipeline & MLOps Engineering: Refactor prototype code into clean scalable production services. Deploy and containerize models orchestrate pipelines and build monitoring systems to detect feature and model drift over time.
• Financial Translation: Partner with corporate finance teams to translate complex statistical outputs into transparent interpretable insights and interactive strategic dashboards.

Required Qualifications & Skills Data Science & Modeling Expertise

• ML & Statistical Foundations: Strong theoretical and practical foundation in supervised/unsupervised learning probabilistic programming ensemble methods and non-linear regression.
• Deep Time Series Domain: Extensive experience with forecasting frameworks (e.g. Prophet ARIMA DeepAR Temporal Fusion Transformers or N-BEATS) and handling sparse noisy or irregular financial datasets.
• Simulation & Decision Science: Proven ability to build simulation frameworks sensitivity analyses or Bayesian networks for risk and scenario modeling.

Software & MLOps Engineering

• Core Tech Stack: Mastery of Python and its scientific/ML stack (Pandas NumPy Scikit-Learn PyTorch/TensorFlow or JAX).
• Engineering & Scale: Strong software engineering practices (Git unit testing APIs) with experience scaling computations using distributed frameworks (e.g. Spark Ray) for heavy simulation workloads.
• Data & Cloud Systems: Proficiency in SQL and cloud data warehouses (e.g. Snowflake BigQuery) alongside MLOps orchestration tools (e.g. Docker MLflow Airflow or Kubernetes).

Experience & Education

• Education: Masters or Ph.D. in Data Science Computer Science Statistics Quantitative Finance or a highly quantitative field.
• Experience: 5 years of professional experience as a Data Scientist or Machine Learning Engineer.
• Preferred Experience: A clear history of applying machine learning directly to financial economic or operational planning data.
• Domain Knowledge: A solid grasp of corporate finance principles (budgeting cycles driver-based planning cost allocation and variance attribution) is highly advantageous.

To Apply

If you meet the residency and Iqama requirements and are ready to take on this cutting-edge challenge in Al Khobar please submit your CV and a brief summary of your experience with time-series forecasting frameworks.