Senior ML Scientist - Financial Forecasting & MLOps

Silver Edge Arabia

Al Khobar

Hybrid

SAR 240,000 - 360,000

Full time

14 days+
Application generator

Stand out for this role — generate a tailored resume and cover letter in about a minute.

Get past ATS filters

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

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.

Get your free, confidential resume review.

or drag and drop your file here.

Similar jobs

Similar jobs worth comparing

ML Engineer, Financial Forecasting & MLOps
ML Engineer, Financial Forecasting & MLOps

Client of Ethra Hr • Saudi Arabia

On-site
SAR 380,000 - 660,000
Machine Learning Scientist
Machine Learning Scientist

Silver Edge Arabia • Al Khobar

On-site
SAR 240,000 - 360,000
Machine Learning Engineer
Machine Learning Engineer

Client of Ethra Hr • Saudi Arabia

On-site
SAR 380,000 - 660,000
Senior MLOps Engineer: Build Self-Serve AI Platform
Senior MLOps Engineer: Build Self-Serve AI Platform

Fathom.io • Dhahran Compound

On-site
SAR 260,000 - 420,000
Senior Data Scientist
Senior Data Scientist

Scarab Dev • Saudi Arabia

Remote
SAR 300,000 - 500,000
Senior ML Platform Engineer, Inference & MLOps
Senior ML Platform Engineer, Inference & MLOps

Mollkom • Riyadh

Hybrid
SAR 150,000 - 210,000
Senior ML Platform Engineer - Inference & MLOps (Hybrid)
Senior ML Platform Engineer - Inference & MLOps (Hybrid)

Mollkom • Riyadh

Hybrid
SAR 150,000 - 210,000
Senior Multimodal ML Engineer - Vision, Text & Data
Senior Multimodal ML Engineer - Vision, Text & Data

Mollkom • Riyadh

Hybrid
SAR 260,000 - 360,000
Applied AI Engineer: End-to-End ML & MLOps
Applied AI Engineer: End-to-End ML & MLOps

Remal Ventures • Saudi Arabia

On-site
SAR 224,971 - 299,962
Lead AI Engineer: Fintech ML & Data Science
Lead AI Engineer: Fintech ML & Data Science

Client of Hire Lebanese • Saudi Arabia

On-site
SAR 120,000 - 180,000