وصف الوظيفة
Role Overview
Senior Data Scientist / Senior ML Engineer at Foodics, a leading restaurant management ecosystem and payment tech provider headquartered in Riyadh with offices across 5 countries.
Company Overview
Foodics is a restaurant management ecosystem and payment tech provider founded in 2014. Headquartered in Riyadh with offices in UAE, Egypt, Jordan, and Kuwait, the company serves customers and partners in over 35 countries. Foodics has processed over 6 billion orders and has completed three rounds of funding, raising $170 million in the largest SaaS funding round in MENA. The company employs over 30 nationalities working across 14 countries.
Role Purpose
You will lead the design, development, and deployment of ML/AI/GenAI models that power core Foodics products including pricing, personalization, and fraud detection. You will collaborate with Data Engineers, Product Managers, and Platform teams to deliver production-grade models with real business impact.
Key Responsibilities
Model Lifecycle & Development
- Own end-to-end ML model lifecycle from problem framing through data exploration, training, deployment, and monitoring.
- Design and develop scalable solutions using classical ML and GenAI techniques.
- Implement deep understanding of model development workflows including feature engineering, hyperparameter tuning, model evaluation, and A/B testing.
MLOps & Infrastructure
- Implement MLOps best practices including versioning, reproducibility, monitoring, and CI/CD for models.
- Ensure familiarity with cloud-native environments, CI/CD, GitOps, and Infrastructure as Code tools.
- Integrate models with APIs and backend services as needed.
Collaboration & Standards
- Collaborate with squads and platform teams to ensure reusability and adherence to standards.
- Mentor junior ML engineers and contribute to the internal ML knowledge base.
Production Ownership
- Embrace and enforce a "you build it, you run it" approach, owning the full lifecycle of ML models from development through monitoring and continuous improvement.
Qualifications & Experience
- 5+ years of experience in applied ML, AI, or data science.
- Proven track record of deploying ML models in production at scale.
Skills & Competencies
Programming & ML Libraries
- Strong proficiency in Python and ML/AI libraries including scikit-learn, PyTorch, TensorFlow, XGBoost, and HuggingFace Transformers.
MLOps & Tools
- Experience with MLOps tools such as MLflow and SageMaker for managing versioning, testing, and observability.
- Familiarity with cloud-native environments (AWS preferred) with CI/CD, GitOps, and Infrastructure as Code tools such as Terraform and CDK.
Statistical & Analytical
- Deep understanding of statistical modeling, statistical inference, and appropriate application of statistical tests including t-test, chi-square, ANOVA, and regression analysis.
- Ability to interpret results and communicate implications to both technical and non-technical audiences.
- Strong understanding of data pipelines, experimentation, and model evaluation.
ML Best Practices
- Knowledge of ML best practices including bias mitigation, explainability tools such as SHAP and LIME, and model monitoring for drift and fairness.
GenAI & LLMs
- Hands-on experience with GenAI and LLM integration including RAG, fine-tuning, embeddings, and prompt engineering.
- Experience with tools such as LangChain, LangGraph, or LlamaIndex.
Additional Information
- Highly competitive compensation packages including bonuses and the potential for shares.
- Regular training and an annual learning stipend to support career development in a hyper-growth environment.
- Inclusive and diverse culture that encourages innovation.
- Autonomy, mentoring, and challenging goals that create opportunities for growth.