Data Scientist I

AI in this role

runway

prompt-engineeringragai-evaluation

About Mozn

MOZN is a leading Enterprise AI company enabling organizations to make informed decisions in two critical domains: Financial Crime Prevention and Enterprise Knowledge Intelligence.

We’re a diverse, collaborative team of innovators united by a shared purpose: to build AI that delivers tangible business value, builds trust, and empowers people and organizations with augmented intelligence. Our culture is built on the relentless pursuit of excellence and meaningful impact.

If you’re passionate about working alongside exceptional talent on world-class AI, and you want the autonomy and runway to do the best work of your career, join us in shaping the future of intelligent enterprises.

About the role

The Mozn brand is alive within a plethora of stakeholder touchpoints and therefore the most suitable candidate for the Data Scientist I position would have an interest in Financial Fraud Detection, Sanction Screening, Know Your Customer (KYC) procedures, Anti-Money Laundering (AML) initiatives, and emerging Generative AI technologies. As a Data Scientist I, you will support the development of analytical and AI-driven solutions to help detect and prevent fraudulent activities and mitigate AML risks under close guidance.

What you'll do

Support the development of fraud detection, AML, and Generative AI use cases.
Assist in building and testing ML and GenAI models (e.g., prompt engineering, RAG pipelines).
Prepare, clean, and analyze structured and unstructured data.
Support the training, evaluation, and deployment of ML models.
Assist with experimentation using LLMs for tasks such as document analysis, alert summarization, or data enrichment.
Execute assigned tasks within project plans under supervision.
Stay informed about industry trends related to fraud, AML, and Generative AI.

Requirements

Bachelor’s degree in Data Science, AI, Machine Learning, Mathematics, Statistics, or a related field.
0–2 years of experience in data science or analytics.
Basic understanding of ML concepts and exposure to Generative AI or LLMs.
Proficiency in SQL and Python.
Experience with data extraction, cleaning, and basic visualization.
Familiarity with model evaluation and feature engineering concepts.
Proficiency in English; Arabic is a plus.
Strong problem-solving skills, attention to detail, and eagerness to learn.

Benefits

You will be at the forefront of an exciting time for the Middle East, joining a high-growth rocket-ship in an exciting space.
You will be given a lot of responsibility and trust. We believe that the best results come when the people responsible for a function are given the freedom to do what they think is best.
The fundamentals will be taken care of: competitive compensation, top-tier health insurance, and an enabling culture so that you can focus on what you do best
You will enjoy a fun and dynamic workplace working alongside some of the greatest minds in AI.
We believe strength lies in difference, embracing all for who they are and empowered to be the best version of themselves.

How we rate this

Data Scientist I at Mozn rates 64 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringRAGAI EvaluationRunway

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide that one model's output is better than another's for a given task?
What's a project where you used Runway hands-on?
Describe a typical day in a role like this one: which parts run through AI directly?

Adapt your resume

List these exact terms on your resume: Prompt Engineering, RAG, AI Evaluation, and Runway. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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