Data Scientist – FinTech Software House – Dubai Based

Data Scientist – FinTech Software House – Dubai Based

Global leading FinTech software house seeks an experienced Data Scientist to join their Global HQ in JLT, Dubai (UAE), leading the tactical execution of the company’s AI strategy.

It is essential that you are already based in Dubai as they will not pay for relocation.

Reporting directly into their Chief Technology Officer (CTO), you’ll join a truly global business with offices and clients based across North America, Europe and Asia, providing trading software across trading, logistics and risk management. You’ll work closely with some of the most experienced software designers, developers and business analysts in the industry, providing the trading community with the most robust, user-friendly, enterprise wide software package. You’ll join a truly world class trading software business, who continue to launch new products that evolve with the changing global technological and trading landscapes, and who have made AI the cornerstone of their business.

The company is executing an AI strategy which will add new capabilities and transform the operating model in addition to delivering benefits that enhance existing and emerging AI related products and services to Customers.

The successful Data Scientist will lead the tactical execution of the company’s AI strategy specifically in the design and implementation of AI/ML use cases built on Large Language Models (LLMs/RAG) and Natural Language Processing (NLP), Machine Learning (ML), Generative AI (GenAI), Robotic Process Automation (RPA) and Decision Making (AgenticAI).

The role will deliver a broad range of prioritised use cases for internal optimisation and external product benefit therefore candidates will need skills and experience across the key AI/ML types with an estimated split of skills as follows:

• 10% AI skills focused on AI/ML models [LLMs/NLP/ML]
• 40% AI skills to automate, assist & augment [GenAI, RPA, AgenticAI]
• 50% AI skills for developing & operationalising AI/ML). SFIA alignments assume all skills are present from lower levels

This is a hands-on role for someone who can move smoothly between experimentation and production, and who has demonstrable experience in converting emerging AI/ML capabilities into measurable business outcomes.

Technical Roles and Responsibilities:

• Design retrieval pipelines using structured and unstructured enterprise data sources.
• Partner with the Data Engineering team to transform data into usable formats, ensuring data is transported (e.g.: event streamed, virtualised) into an AI accessible unified data platform.
• Apply exploratory and inferential statistical data analysis techniques (e.g.: Range Analysis [interpolation, extrapolation, gaussian distribution, confidence intervals], Time Series Analysis [ARIMA], Hypothesis Testing [ANOVA, Monte Carlo], Bayesian Statistics and Dimensionality Reduction [Principal Component Analysis].
• Prepare data models for AI/ML model consumption including but limited to data cleaning and QA, semantic structuring and vectorization and feature engineering.
• Automate data preparation for ML.
• Establish guardrails for compliance, privacy, security, and responsible AI deployment.
• Partner with product, design, data, security, and go-to-market teams to define use cases and deliver production features.
• Create benchmarks and success metrics tied to customer adoption, efficiency gains, and revenue impact.
• Stay current on advances in LLMs, agent frameworks, vector search, model serving, and AI infrastructure.

Other Roles and Responsibilities:

• Strong SQL and Python skills and solid experience with GraphQL based service APIs.
• Demonstrable experience in creating algorithms and models to forecast trends, classify data and automate data driven insight.
• Detailed knowledge of libraries like Scikit-Learn, Tensorflow and PyTorch.
• Experience with modern LLM stacks, including prompt engineering, RAG, embeddings, vector databases, function calling, and evaluation pipelines.
• Experience integrating LLM systems with enterprise product workflow and internal knowledge systems.
• Strong understanding of transformer models, model limitations, inference tradeoffs, and quality evaluation.
• Experience with the platforms of Azure cloud compute.
• Familiarity with Docker, Kubernetes, CI/CD, Lakehouse DBs, source data stores and production monitoring.
• Experience fine-tuning open-source models and working with model serving frameworks.
• Experience with LangChain, LlamaIndex, DSPy, semantic search, reranking, agent orchestration frameworks.
• Experience with data pipelines, search infrastructure, knowledge graphs, enterprise data integration.
• Experience with security, governance, and compliance requirements in enterprise environments.
• Used to building copilots, internal assistants, customer support bots, or task automation agents.

Technical Experience and Expertise:

A broad range of technology knowledge to appropriate level of competency/qualification including but not limited to:

• Languages: Python, SQL, TypeScript or Go, PowerShell, Git and ideally Java/C#
• Data ETL/Analysis: Pandas, Numpy, Apache Spark (PySpark), Hadoop
• ML/Deep Learning: Scikit-learn, TensorFlow or PyTorch
• LLM/AI: OpenAI APIs, Anthropic, open-source LLMs, embeddings, fine-tuning, evaluation
• Frameworks: LangChain, LlamaIndex, DSPy, FastAPI, MLflow, Hugging Face
• Data & Retrieval: Vector databases, Elasticsearch/OpenSearch, metadata filtering, reranking
• Infra: Azure, AKS, Docker, Redis, Postgres, SQL
• Engineering: GraphQL service integration, distributed systems, observability, testing, CI/CD
• Responsible AI: Guardrails, red teaming, privacy, model risk management

Salary: Flexible but circa AED 20-30k PCM + package

Experience:

• 5+ years of data engineering, big data analytics with complex datasets, data visualisation, machine learning and/or applied AI experience.
• 2+ years of hands-on experience building ML, models for agentic dataset retrieval/agentic service integration or generative AI systems in production.
• Masters in Data Science, Machine Learning or a related field.

For more information, please e-mail an up-to-date copy of your CV to Ben at [email protected]