Senior Data Scientist / Senior ML Engineer

Who Are We❓ We Are Foodics! a leading restaurant management ecosystem and payment tech provider. Founded in 2014 with headquarter in Riyadh and offices across 5 countries, including UAE, Egypt, Jordan and Kuwait. We are currently serving customers and partners in over 35 different countries worldwide. Our innovative products have successfully processed over 6 billion (yes, billion with a B) orders so far! making Foodics one of the most rapidly evolving SaaS companies to ever emerge from the MENA region. Also Foodics has achieved three rounds of funding, with the latest raising $170 million in the largest SaaS funding round in MENA, boosting its innovation capabilities to better serve business owners. The Job in a Nutshell💡 You will lead the design, development, and deployment of ML/AI/GenAI models that power core Foodics products (e.g., pricing, personalization, fraud detection). You’ll collaborate with Data Engineers, Product Managers, and Platform teams to deliver production-grade models with real impact. What Will You Do❓ - Own end-to-end ML model lifecycle: problem framing, data exploration, training, deployment, monitoring. - Design and develop scalable solutions using classical ML and GenAI techniques. - Implement MLOps best practices: versioning, reproducibility, monitoring, CI/CD for models. - 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. - Integrate models with APIs and backend services as needed. - 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. What Are We Looking For❓ - 5+ years’ experience in applied ML, AI, or data science. - Strong proficiency in Python and ML/AI libraries (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, HuggingFace Transformers). - Experience with MLOps tools (e.g., MLflow, SageMaker) and managing versioning, testing, and observability. - Deep understanding of model development workflows including feature engineering, hyperparameter tuning, model evaluation, and A/B testing. - Deep understanding of statistical modeling, statistical inference, and the appropriate application of statistical tests (e.g., t-test, chi-square, ANOVA, regression analysis); ability to interpret results and communicate implications to both technical and non-technical audiences. - Proven track record of deploying ML models in production at scale. - Knowledge of ML best practices including bias mitigation, explainability (e.g., SHAP, LIME), and model monitoring for drift and fairness. - Strong understanding of data pipelines, experimentation, and model evaluation. - Familiarity working in a cloud-native environment (AWS preferred) with CI/CD, GitOps, and IaC tools (e.g., Terraform, CDK). - Hands-on experience with GenAI / LLM integration (e.g., RAG, fine-tuning, embeddings, prompt engineering) and tools such as LangChain, LangGraph, or LlamaIndex. What We Offer You❗ We believe you will love working at Foodics! - We have an inclusive and diverse culture that encourages innovation. - We offer highly competitive compensation packages, including bonuses and the potential for shares. - We prioritize personal development and offer regular training and an annual learning stipend to tackle new challenges and grow your career in a hyper-growth environment. - Join a talented team of over 30 nationalities working in 14 countries, and gain valuable experience in an exciting industry. - We offer autonomy, mentoring, and challenging goals that create incredible opportunities for both you and the company.

Own end-to-end ML model lifecycle: problem framing, data exploration, training, deployment, monitoring. Design and develop scalable solutions using classical ML and GenAI techniques. Implement MLOps best practices: versioning, reproducibility, monitoring, CI/CD for models. 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. Integrate models with APIs and backend services as needed. 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.