Machine Learning Scientist

Silver Edge Arabia; Al Khobar; On-site; SAR 240,000 - 360,000; Full time

Job summary

Silver Edge Arabia seeks a Machine Learning Scientist/Engineer for a hybrid Al Khobar-based 12-month contract. You will design, build, and productionize algorithms powering next-generation financial intelligence systems, guiding models from prototyping to scalable deployment.

You will apply time-series forecasting, ML modeling, and MLOps practices to automate cost variance and KPI forecasts, partnering with finance to deliver transparent insights.

Qualifications

• Strong theoretical and practical foundation in supervised/unsupervised learning, probabilistic programming, ensemble methods, and non-linear regression.
• Extensive experience with forecasting frameworks (Prophet, ARIMA, DeepAR, TFT, or N-BEATS) and financial data handling.
• Proven ability to build simulation frameworks, sensitivity analyses, or Bayesian models for risk and scenario modeling.

Responsibilities

• Advanced Predictive Modeling: Design, train, and validate ML architectures and statistical models for multi-horizon forecasts.
• Time Series & Sequential Modeling: Leverage deep time series techniques to capture seasonal patterns and macro dependencies in financial data.
• Scenario & What-If Simulation: Develop Monte Carlo and stress-testing engines for interactive scenario analysis.
• KPI & Cost Variance Analysis: Build automated anomaly detection and variance attribution models.
• Production Pipeline & MLOps Engineering: Refactor code for production, containerize models, orchestrate pipelines and monitor drift.
• Financial Translation: Partner with finance to translate outputs into interpretable dashboards.

Skills

ML Foundations

Time Series Forecasting

Simulation & Bayesian

Education

Master's or PhD in Data Science/CS/Quantitative Finance

Tools

Snowflake

BigQuery

Docker

Kubernetes

Job description

Machine Learning Scientist / Engineer – Financial Intelligence

Location: Al Khobar, Saudi ArabiaPosition 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

• 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.