About Us

Tamara is the leading fintech platform in Saudi Arabia and the wider GCC region with a mission to help people make their dreams come true by building the most customer-centric financial super-app on earth. The company serves millions of users in the region and partners with leading global and regional brands such as SHEIN, Jarir, noon, IKEA and Amazon, as well as small and medium businesses.

Tamara is Saudi Arabia’s first fintech unicorn and is backed by Sanabil Investments, a wholly owned company by the Public Investment Fund (PIF), SNB Capital, Checkout.com, amongst others. The company operates from its headquarters in Riyadh, with additional regional and global support offices.

Your Role

We are looking for a Senior Machine Learning Engineer (MLE) to join our Risk Data Science team. You will play a key role in designing, building, deploying, and scaling ML models that drive credit risk, fraud prevention, behavioral scoring, and other risk-related decision systems across our business.

You will work closely with data scientists, risk analysts, and engineering teams to transform research prototypes into high-performance, production-grade solutions that operate at scale in real-time decisioning environments.

Your Responsibilities

Model Deployment & Scaling

• Productionise risk and fraud models developed by the DS team using robust, efficient, and maintainable architectures
• Design low-latency, high-availability APIs and pipelines for real-time model inference.
• Implement batch scoring systems for periodic risk assessments.=

MLOps & Infrastructure

• Build and maintain CI/CD pipelines for model deployment and monitoring.
• Set up automated feature engineering pipelines, leveraging feature stores.
• Ensure model governance: reproducibility, versioning, auditability, and compliance with regulatory requirements.

Model Monitoring & Maintenance

• Implement real-time and batch monitoring for data drift, concept drift, and model performance.
• Build automated retraining workflows and model rollback mechanisms.

Collaboration with Risk DS

• Work closely with risk data scientists to translate experimental code (Python, notebooks) into production-grade services.
• Advise DS on efficient model architectures for operational environments.
• Optimize feature computation for speed and scalability.

System Design & Integration

• Integrate models with credit underwriting, fraud detection, collections, and merchant risk systems.
• Collaborate with backend engineering to align on API contracts and system interfaces.

Your Expertise

• 6+ years of experience as an MLE, ML Engineer, Mlops Developer.
• Strong Python skills (including Pandas, NumPy, scikit-learn, PySpark, FastAPI/Flask).
• Proficiency in distributed computing frameworks (Spark, Ray) and workflow orchestration tools (Airflow, Prefect).
• Experience with MLOps tools (MLflow, SageMaker, Vertex AI, or similar).
• Strong understanding of model deployment in cloud environments (AWS/GCP/Azure).
• Solid knowledge of microservice architecture, containerization (Docker), and orchestration (Kubernetes).
• Proven track record of deploying and maintaining ML models in production at scale.
• Experience in building and integrating with real-time streaming systems (Kafka, Kinesis, Pub/Sub).

All qualified individuals are encouraged to apply.

The role of a Data Scientist at Dataiku is quite unique. Our Data Scientists not only code up solutions to real-world problems, but also participate in client-facing endeavours throughout the customer journey. This includes supporting their discovery of the platform, helping integrate Dataiku with other tools and technologies, some user training, and co-developing data science projects from design to deployment.

Just as non-technical skills are important, so too are technical skills. Our Data Scientists work on the Dataiku platform every day. Aside from the visual tools, our team primarily uses Python and SQL, with occasional work in other languages (e.g., R, PySpark, JavaScript). An ideal candidate is excited to learn complex new technologies and modelling techniques while being able to explain their work to other data scientists and clients.

In this role, you'll help the team:

• Co-develop production-level data science projects with our customers

• Analyse and investigate various kinds of data and machine learning applications across industries and use cases

• Help users discover and master the Dataiku platform, via user trainings, office hours, and ongoing consultative support

• Provide data science expertise both to customers and internally to Dataiku’s sales and marketing teams

• Develop custom Python-based “plugins” in collaboration with Solutions, R&D, and Product teams, to enhance Dataiku’s functionality

You might be a good fit for the role if you have:

• English - fluent, Arabics - fluent/native will be a big plus

• Curiosity and a desire to learn new topics and skills

• Empathy for others and an eagerness to share your knowledge and expertise with your colleagues, Dataiku’s customers, and the general public

• The ability to clearly explain complex topics to technical as well as non-technical audiences

• 2 - 10 years of experience with Python and SQL

• 2 - 10 years of experience with building ML models and using ML tools (e.g., sklearn)

• Familiarity with data visualisation in Python and R

• Understanding of underlying data systems such as Cloud architectures, Hadoop, or SQL

Technical skills that may help you in the role:

• Experience with Consulting and/or Customer-facing Data Science roles

• Experience with Data Engineering or MLOps

• Experience developing WebApps in JavaScript, RShiny, or Dash

• Experience building APIs

• Experience using enterprise data science tools

• Passion for teaching or public speaking

#LI-AS2

About the team

The Careem Data Science team’s mission is to drive competitive value from data at scale through building AI models to optimize user experiences, decision-making, operational efficiencies, and lead the region’s AI ecosystem. As one of the tech leaders in this team, you will be at the forefront of fulfilling this mission. You will be working with the top data science talent of the region, while innovating on our user experience using GenAI.

What you'll do

• Be part of a 0-1 AI transformation for the Careem app from a personalization perspective.
• Build a long-term vision on how we can rethink our customer acquisition and engagement strategies, leveraging data in our decision-making.
• Drive exploratory analysis to understand the ecosystem, user behavior, and identify new levers to help move metrics and build models of user behaviors for analysis and product enhancements.
• Shape and influence data/ML models and instrumentation to optimize the product experience and generate insights on new areas of opportunity and new products.
• Provide product leadership by sharing data-based recommendations to communicate the state of the business, the root cause of change in metrics, and experimentation results, influencing product and business decisions
• Implement scalable machine learning algorithms that will be used in production on big data.
• Embark on exploratory data analysis projects to achieve a better understanding of phenomena as well as to discover untapped areas of growth and optimization. 
• Answer complex analytic questions from big data sets to help Careem shape its products and services in a better way.
• Help define and track the appropriate key metrics for specific projects. 
• Design and run randomized controlled experiments, analyze the resulting data, and communicate results with other teams.
• You will always challenge the status quo and continually investigate new data processing technologies and seek to ensure that we follow the industry best practices.
• Build and deploy retrieval augmented generation systems and other applications of large language models.

What you'll need

• 7-9 years of experience in data mining, predictive modeling, time series analysis, machine learning, Big Data methodologies, and transformation and cleaning of both structured and unstructured data.
• Advanced degree in a quantitative discipline such as Physics, Statistics, Mathematics, Engineering or Computer Science.
• Solid experience with Deep Learning Techniques (e.g, attention, retrieval models, etc)
• 1-2 years of industrial experience in personalization, recommendation or search is a MUST. Preferably working in a product-driven company.
• Strong problem-solving and coding skills.
• Solid knowledge of AB testing, classic ML, and DL
• Solid understanding of recommendations, ranking, and retrieval. 
• Proficiency and demonstrated experience in: Python, SQL, Spark, Hive.
• Demonstrated experience with database technologies (e.g. , Hadoop, BigQuery, Amazon EMR, Hive, Oracle, SAP, DB2, Teradata, MS SQL Server, MySQL).
• Demonstrated experience with business intelligence and visualization tools (Tableau, MicroStrategy, ChartIO, Qlik) along with geospatial data processing skill, is a plus.

About the Role

We’re looking for senior technical leaders who can architect and scale intelligent systems that architect the "brain" of our dark store operations. As a senior data scientist you won’t just be building models, you’ll be optimizing the lifecycle of millions of items from the moment they leave a supplier to the moment they reach a customer’s doorstep in under 20 minutes. You will tackle the classic "perishable trade-off" ensuring our shelves are always stocked with what customers want, while ruthlessly minimizing wastage and maximizing every square centimetre of our dark store floor space.

What You'll Do

• Demand Forecasting: Build and deploy high-precision forecasting models to predict hyper-local demand at the SKU-store level.
• Inventory & Availability: Develop replenishment algorithms that balance In-Stock Availability with the risk of expiry, specifically for high-turnover perishables.
• Wastage Mitigation: Use ML to identify items at risk of expiring and trigger automated interventions including reduced-to-clear discounting, intra-store replenishment etc.
• Dynamic Pricing & Discounts: Design and A/B test pricing strategies that move inventory efficiently without eroding margins.
• Space Management: Optimize dark store space determining which items should be stored where within the dark store to minimize picker travel time and maximize storage density.
• End-to-End Ownership: Work closely with Product Managers and Supply Chain Operations to turn your models into production-level code that dictates real-world logistics.

What You'll Need

• Academic Background & Experience: 8+ years of experience in Data Science, ideally in E-commerce, Q-commerce, or Supply Chain. A background in Operations Research or Statistics. Hands-on proficiency in Python, Spark and ML frameworks. Experience with real-time inference systems and model monitoring in production. Strong understanding of experimentation, A/B testing, and causal inference. Demonstrated ability to drive architectural decisions across teams.
• Technical Skills: Professional-grade Python and SQL. Experience with optimization libraries (like Gurobi, CPLEX, or PuLP) and ML frameworks (XGBoost, LightGBM, or PyTorch).
• Problem Solving: Experience with Stochastic Optimization or Reinforcement Learning for inventory management is highly preferred.
• Business Intuition: You understand that a 1% reduction in wastage across 100+ stores translates to millions in saved EBITDA.
• Communication Skills: Excellent communication skills and ability to translate complex modelling trade-offs into business impact.

What We’ll Provide You

We offer colleagues the opportunity to drive impact in the region while they learn and grow. As a full time Careem colleague, you will be able to:

• Work and learn from great minds by joining a community of inspiring colleagues.
• Put your passion to work in a purposeful organisation dedicated to creating impact in a region with a lot of untapped potential.
• Explore new opportunities to learn and grow every day.
• Work 4 days a week in office & 1 day from home, and remotely from any country in the world for 30 days a year with unlimited vacation days per year. (If you are in an individual contributor role in tech, you will have 2 office days a week and 3 to work from home.)
• Access to healthcare benefits and fitness reimbursements for health activities including gym, health club, and training classes.

About the Role

As a Staff Data Scientist I – ETA, you will own the end-to-end ETA prediction systems for Careem’s Food and Groceries verticals. This is a highly technical Individual Contributor role with full domain ownership across architecture, modeling strategy, experimentation, production deployment, and operational excellence.

You will define the long-term vision for ETA systems in high-load, latency-sensitive marketplace environments. You will design and implement multi-stage stochastic pipelines that model preparation time, assignment delay, pickup time, travel time, batching, and pooling effects delivering reliable, calibrated predictions at scale. This role requires deep expertise in time-series forecasting, deep learning, and operations research combined with strong production experience in distributed and real-time systems. You will collaborate closely with Product, Engineering, Marketplace, and Operations leaders, ensuring ETA becomes a core competitive advantage in Careem’s Everything App.

What You'll Do

1. ETA Vision & Architecture Ownership

• Define and own the long-term technical vision for ETA systems across Food and Groceries.
• Architect scalable, multi-stage pipelines.
• Design probabilistic and stochastic modeling approaches with uncertainty calibration and reliability guarantees.
• Establish modeling standards and best practices for ETA across the organization.

2. Advanced Modeling & Algorithm Development

• Develop and deploy production-grade ML systems leveraging:
• Deep learning architectures
• Time-series forecasting
• Graph-based and routing-aware models
• Operations research techniques
• Build models robust to marketplace volatility and supply-demand shifts.
• Optimize for both point accuracy and distributional correctness (confidence intervals, tail control).
• Continuously improve system performance under high traffic and low-latency constraints.

3. Production Systems & Real-Time Inference

• Design and deploy scalable real-time inference pipelines.
• Ensure model reliability, monitoring, alerting, and graceful degradation under load.
• Collaborate with Data Platform and ML Ops teams to productionize models using Spark, Trino, Python, and distributed frameworks.
• Lead model lifecycle management, retraining strategies, and performance tracking in live environments.

4. Experimentation & Marketplace Impact

• Define clear evaluation frameworks aligned with business metrics (conversion, cancellations, fulfillment efficiency, customer trust).
• Design and run controlled experiments to measure ETA improvements and marketplace impact.
• Drive measurable improvements in operational efficiency and user experience through data-driven insights.

5. Technical Leadership & Cross-Team Influence

• Lead cross-team architectural discussions.
• Conduct design reviews and raise the technical bar for modeling quality and system robustness.
• Mentor senior data scientists and engineers in advanced ML and modeling techniques.
• Contribute to Careem’s applied AI community through technical talks, documentation, and research initiatives.
  
  

What You'll Need

• 8+ years of experience in Applied Machine Learning or Data Science, with significant experience building large-scale production systems.
• Advanced degree in Computer Science, Statistics, Engineering, Operations Research, or a related quantitative field.
• Proven expertise in:
• Time-series forecasting
• Deep learning
• Stochastic modeling
• Operations research and optimization
• Strong experience building and deploying high-load, low-latency ML systems.
• Hands-on proficiency in Python, Spark and ML frameworks.
• Experience with real-time inference systems and model monitoring in production.
• Strong understanding of experimentation, A/B testing, and causal inference.
• Demonstrated ability to drive architectural decisions across teams.
• Excellent communication skills and ability to translate complex modeling trade-offs into business impact.
  
  

What we’ll provide you

We offer colleagues the opportunity to drive impact in the region while they learn and grow. As a full time Careem colleague, you will be able to:

• Work and learn from great minds by joining a community of inspiring colleagues.
• Put your passion to work in a purposeful organisation dedicated to creating impact in a region with a lot of untapped potential.
• Explore new opportunities to learn and grow every day.
• Work 4 days a week in office & 1 day from home, and remotely from any country in the world for 30 days a year with unlimited vacation days per year. (If you are in an individual contributor role in tech, you will have 2 office days a week and 3 to work from home.)
• Access to healthcare benefits and fitness reimbursements for health activities including gym, health club, and training classes.

About the Role

As a Staff Data Scientist I – Matching, you will own the end-to-end matching and dispatch architecture for Careem’s Food and Groceries verticals. You will design and implement real-time assignment systems that optimize cost, SLA compliance, and marketplace efficiency at scale.

This role sits at the core of the marketplace engine solving complex, multi-objective optimization problems under uncertainty. You will build systems that dynamically match orders to captains, optimize batching and pooling decisions, and continuously re-optimize as new information arrives. You will operate as a senior technical IC, leading architecture decisions, mentoring other data scientists, and setting optimization standards across the domain.

What You'll Do

1. Matching & Dispatch Architecture Ownership

• Design and own the end-to-end matching and dispatch systems for Food & Groceries.
• Architect scalable assignment frameworks incorporating:
• Static and dynamic assignment
• Online matching under uncertainty
• Batch and pooling optimization
• Continuous re-optimization with dynamic events
• Establish robust optimization standards and best practices across the organization.

2. Multi-Objective Optimization & Algorithm Development

• Formulate and implement multi-objective optimization models balancing:
• Cost minimization
• SLA compliance
• Marketplace efficiency
• Develop and deploy:
• Min-cost flow and assignment models
• Graph-based optimization frameworks
• Custom solvers and OR-Tools based solutions
• Incorporate stochastic elements and uncertainty-aware decision-making into assignment policies.
• Continuously refine matching quality under high-scale marketplace conditions.

3. Real-Time Systems & Production Delivery

• Build production-grade real-time matching systems using Python, Spark, and Trino.
• Design scalable pipelines capable of handling large-scale, high-throughput marketplace events.
• Collaborate with Engineering and Platform teams to ensure reliable model serving and system performance.
• Lead performance monitoring, diagnostics, and system optimization.

4. Experimentation & Impact Measurement

• Define evaluation metrics aligned with marketplace and operational objectives.
• Design and run controlled experiments to quantify matching improvements.
• Analyze system trade-offs and communicate clear, data-driven recommendations to Product and Operations stakeholders.
• Ensure decisions translate into measurable improvements in efficiency and SLA performance.

What You'll Need

• 6–8+ years of experience in Applied Machine Learning, Optimization, or Data Science.
• Advanced degree in Computer Science, Engineering, Operations Research, Mathematics, or a related quantitative field.
• Strong expertise in:
• Optimization and assignment algorithms
• Graph modeling and min-cost flow problems
• Multi-objective optimization
• Dynamic and online decision systems
• Hands-on experience with OR-Tools or custom optimization solvers.
• Proficiency in Python, SQL, Spark, and distributed data systems.
• Experience building real-time or near-real-time decision systems.
• Strong understanding of experimentation and performance evaluation in marketplace systems.
• Excellent communication skills with the ability to explain algorithmic trade-offs in business terms.

What we’ll provide you

We offer colleagues the opportunity to drive impact in the region while they learn and grow. As a full time Careem colleague, you will be able to:

• Work and learn from great minds by joining a community of inspiring colleagues.
• Put your passion to work in a purposeful organisation dedicated to creating impact in a region with a lot of untapped potential.
• Explore new opportunities to learn and grow every day.
• Work 4 days a week in office & 1 day from home, and remotely from any country in the world for 30 days a year with unlimited vacation days per year. (If you are in an individual contributor role in tech, you will have 2 office days a week and 3 to work from home.)
• Access to healthcare benefits and fitness reimbursements for health activities including gym, health club, and training classes.