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 models from 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

Master's 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. Desired Candidate Profile

Education

Experience

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. 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. 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). Residency: Candidates must be currently resident in Saudi Arabia. Work Authorization: Must possess valid right to work in KSA and/or a transferable Iqama .