Why Tamara?

We’re proud to be Saudi’s first FinTech unicorn.

Our mission is to help people own their dreams by building the most customer-centric financial super app in the world. & There is no playbook for that; our Tamarians are writing it. Our teams are made up of innovators, problem-solvers, and learners we thrive on curiosity and collaboration.

If this sounds like you: curious, driven, and ready to build, we’d love to meet you

Apply now and join the next generation of Builders!

About Tamara's Builders Program 

At Tamara, we believe exceptional talent deserves an exceptional launchpad.

Our Flagship Builders Program is designed for ambitious graduates ready to step into real responsibility from day one. This isn’t a rotational “observer” program, it’s a career accelerator built for those who want to build, own, and raise the bar early.
Designed for recent graduates and early-career talent with up to two years of experience, the program places you directly into high-impact roles across Product, Engineering, Design, and beyond. You’ll contribute immediately and grow at an accelerated pace.
From Product to Engineering, Design to Commercial, you’ll tackle meaningful challenges that shape how millions experience fintech across the region. You’ll be trusted with ownership, surrounded by high-caliber peers, and mentored by leaders who expect excellence.

Our January and June cohorts are your opportunity to move fast, think big, and start building what’s next - not someday, but now.

About the role

We’re looking for a fresh graduate or early-career Associate Data Scientist on a builder path.

This role blends product thinking, applied statistics, and production-minded analytics. You will:

• turn ambiguous problems into measurable questions
• build reliable measurement (metrics and experimentation)
• create lightweight models and decision tools that teams can use in real workflows
• partner closely with engineering, product, and risk to improve customer outcomes and business performance
• build AI-assisted decision workflows (for example, LLM-powered analysis copilots backed by a curated metrics layer, with clear guardrails and validation)

With the advancement of AI, we value people who have strong fundamentals and clear thinking. Understanding data generation, measurement, tradeoffs, and how to validate results matters more than memorizing tools. You'll learn how to use AI responsibly to move faster, while still owning correctness, robustness, and interpretation.

Your responsibilities

• Define problems and measurement
  • Translate product or business questions into testable hypotheses and clear success metrics.
  • Build and maintain metric definitions and analysis templates so decisions are consistent and repeatable.
• Experimentation and causal thinking
  • Design, analyze, and interpret A/B tests (and quasi-experiments when randomization is not possible).
  • Partner with product and engineering to ensure correct tracking, guardrails, and experiment quality.
• Modeling for decisions (practical ML)
  • Build baseline predictive and segmentation models (for example, churn propensity, risk signals, customer clustering) with clear evaluation and limitations.
  • Focus on models that are actionable: who will use them, when, and what decision they change.
• Applied AI (LLMs) for analytics and decisioning
  • Prototype small, safe LLM use cases that improve how teams explore data (for example, natural-language Q&A over a curated dataset, summarizing experiment results, or generating investigation checklists).
  • Help define evaluation and validation approaches for AI-assisted outputs (sanity checks, golden queries, offline test sets, and human review loops).
  • Partner with platform and security stakeholders to ensure AI workflows are permissioned correctly and avoid leaking sensitive data.
• Analytics that ships
  • Create reusable, well-documented analysis assets: datasets, feature tables, notebooks, dashboards, and simple services or jobs.
  • Collaborate with data engineering to productionize reliable pipelines where needed.
• Data quality and reliability
  • Validate data inputs, monitor key metrics, and investigate anomalies.
  • Document assumptions and build sanity checks so analyses and models are trustworthy.
• Responsible use of AI tools
  • Use AI to accelerate coding, documentation, and exploration.
  • Validate AI outputs, protect sensitive data, and follow safe data handling practices.

Your expertise (must have)

• Fresh graduate or < 1 year of relevant experience (internships, capstone projects, or part-time roles count).
• Strong SQL fundamentals (joins, aggregations, window functions).
• One programming language for data (preferably Python) with basic skills in:
  • Data manipulation (tables/dataframes)
  • Statistics fundamentals (distributions, sampling intuition, confidence basics)
  • Basic modeling (regression/classification basics and evaluation intuition)
• Strong analytical thinking:
  • Ability to define a problem, validate data, and explain results clearly.
• Strong attention to detail and commitment to accurate, reliable outputs.
• Ability to work effectively in a team-oriented environment.

Nice to have

• Exposure to experimentation platforms or frameworks (for example Statsig, Optimizely, internal experimentation tooling).
• Familiarity with modern analytics stacks (dbt, BigQuery, Snowflake, Looker, PowerBI, Tableau) through coursework or projects.
• Exposure to ML tooling (scikit-learn, notebooks, basic MLOps concepts) and version control (Git).
• Familiarity with LLM concepts (prompting basics, retrieval/RAG intuition, and evaluation approaches) through coursework or side projects.
• Understanding of product analytics concepts (funnels, cohorts, retention) and causal pitfalls (selection bias, confounding).
• Experience creating AI-ready data assets (clean semantic layers, metric definitions, data contracts, documentation, and sanity-check checklists).
• Knowledge of responsible data handling (PII basics, access controls, safe sharing).

What success looks like

• You can independently deliver an end-to-end analysis with clear assumptions, validation steps, and a concrete recommendation.
• You help ship at least one decision tool (experiment, metric framework, or lightweight model) that changes a product or business decision.
• Stakeholders can run repeatable analyses with less back-and-forth, and your work reduces ambiguity in key metrics.
• You can spot when results look off, debug quickly, and explain the root cause.

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