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JUNIOR DATA SCIENTIST - Dubai/ UAE
Employment type: Full-time & Permanent
Starting salary of USD 65,000 per annum + performance based bonus - 0% tax environment
Job Location: Dubai, UAE (We provide visa sponsorship & relocation assistance)
About Cobblestone Energy
Cobblestone Energy is an energy trading company specialising in the European Gas, Power and Emissions markets. Established in 2017, we combine deep analytics, advanced data science and agile technology to drive profitable trading decisions. Our flat organisational structure empowers every team member with autonomy and ownership. Our diverse global team of extraordinary talent is united by a relentless pursuit of excellence.
Cobblestone Values
- Lifelong learning with continuous reflection
- Independent thinking through a meritocracy of ideas
- The team is more important than the individual.
- Being the best in any market we enter
- Hiring and keeping only the most effective people
- Others must benefit from our existence.
What we offer in this role
- A full-time position on our Commercial team
- Committed and remarkably talented colleagues.
- Highly competitive compensation dependent on performance
- An exciting, challenging and fulfilling career.
- Investment in your development to ensure that you always remain the best in business.
- Equity participation for strong contributors, ensuring our interests are all aligned long term.
Key Responsibilities:
- Data Preparation: Collect, clean, and structure large datasets from market, fundamental, and operational sources
- Forecasting & Modeling: Develop and validate predictive models (e.g. wind generation, demand) to support trading strategies
- Strategy Research: Prototype, back-test, and refine data-driven trading ideas; collaborate with analytics and development teams to productionise solutions
- Infrastructure: Build and maintain data pipelines, dashboards, and automated tooling to streamline workflows
- AI-Accelerated Workflows: Utilise LLMs and agentic frameworks to accelerate exploratory data analysis (EDA), automate data cleaning pipelines, and generate synthetic data for model robustification.
- Stay current with the rapidly evolving AI landscape (e.g., RAG, Agents, Multimodal models) and proactively propose new ways to apply these to trading data.
- Collaboration: Partner with traders, risk managers, and software engineers to integrate insights into real-time decision-making
- Documentation: Maintain clear records of methodologies, data definitions, and model specifications
- Culture: Champion our values through proactive communication and teamwork
Your background:
- Bachelor's or master’s degree in Computer Science, Mathematics, Statistics, or a related field
- Strong proficiency in programming languages such as Python and R
- Experience with data analysis and manipulation tools such as Pandas, NumPy, and SQL
- Knowledge of data visualization tools such as Matplotlib and Seaborn
- Experience programmatically integrating LLMs (via APIs, LangChain, or Python scripts) into data pipelines rather than just using chat interfaces.
- Proven ability to build custom \"Agents\" or automated workflows that interpret unstructured data or reason through multi-step problems.
- Pro-active and flexible work mentality
- Interest in data-driven trading
- Fluency in English, both written and spoken.
Preferred Background:
- Proven track record of excellence, academic or professional, in quantitative or analytical roles
- Experience working with time-series data, machine learning libraries, or cloud-based data platforms
- Demonstrated ability to innovate and drive end-to-end solutions
The Hiring Process
- Application Review
- Online Assessments: Psychometric and logical reasoning tests
- Case Study: Research-oriented project to showcase your analytical approach
- Remote Interviews: Three rounds with members of our Analytics and Trading teams
- Offer & Onboarding: Join our Commercial Development Program, then step directly into your role as Junior Data Scientist
Important Note: This role is advertised globally; however, the job location is as specified in the job description.
