business analyst

data analyst

financial analyst

treasury analyst

senior data analyst

quantitative analyst

BtcDana Global

<div>
 
 
 Company Description
 
 
 
 BTC Dana is a leading next-generation financial brokerage firm based in Dubai, dedicated to revolutionizing the investment landscape. We offer a diverse range of over 500 CFD trading instruments, including FX, Cryptocurrencies, Commodities, and Stocks. Our user-friendly FinTech-designed platform and dedicated customer support make trading easy and enjoyable for everyone, ensuring transparency and fund safety.
 
 
 
 
 
 Role Description
 
 
 This is a full-time on-site role for a Quantitative Analyst (Risk), located in Dubai. The Quantitative Analyst will be responsible for developing and implementing quantitative models to analyze market risks, conducting statistical analysis, and supporting the trading platform's risk management strategies. Daily tasks will include data analysis, designing risk metrics, collaborating with various teams to optimize trading strategies.
 
 
 
 
 
 Key Responsibilities:
 
 <ul>
 <li>
 Factor &amp; Indicator Mining: Build risk/behavioural factors from historical data (e.g., markout, VPIN), integrated with risk management.
 </li>
 <li>
 User Segmentation &amp; Profiling: Classification models for retail/professional/arbitrage/HFT clients, with risk grading to drive strategy and routing optimization.
 </li>
 <li>
 PnL Attribution: Daily profit and return attribution (fees, spread, funding, basis, slippage) pipelines and anomaly notifications.
 </li>
 <li>
 Data Pipelines (Historical/Real-time): ETL of orders, trades, positions, and funds; unified definitions of metrics and dimensions.
 </li>
 <li>
 Real-time Risk Control &amp; Alerts: Dashboards (by platform/product/account), risk order detection, anomaly detection, automated risk parameter adjustment, and circuit-breaker recommendations.
 </li>
 </ul>
 
 
 
 
 
 Core Skills:
 
 
 <ul>
 <li>
 Risk Metrics: Exposure, leverage, Greeks, hedge deviation, latency/failure rate, VaR/ES, TCA, risk factor backtesting metrics (IR, Sharpe, Max Drawdown).
 </li>
 <li>
 Machine Learning: Scikit-learn; classification/regression, anomaly &amp; change-point detection, feature engineering, model drift/calibration &amp; monitoring; familiarity with PyTorch/TF.
 </li>
 <li>
 Databases &amp; Tools: SQL/Spark/Hive/ClickHouse, data modeling/partitioning/materialized views, BI tools (FineBI/PowerBI/Tableau).
 </li>
 <li>
 Data Stream Processing: Spark/Flink, Kafka/Redpanda, Airflow/Dagster; data quality frameworks (Great Expectations).
 </li>
 <li>
 Programming: Python/Java, engineering practices (testing, logging, version control).
 </li>
 <li>
 Cloud platform: Google Cloud Platform is preferred
 </li>
 </ul>
 
 
 
 
 
 Preferred Qualifications:
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree or above in Computer Science, Financial Engineering, Data Science, or related fields.
 </li>
 <li>
 Familiarity with common risk control and anomaly detection algorithms (supervised/unsupervised learning, clustering, time series analysis).
 </li>
 <li>
 Prior experience in financial derivatives, leveraged trading, or FX/CFD risk management preferred.
 </li>
 <li>
 Strong risk awareness, with ability to connect models to business use cases.
 </li>
 <li>
 Strong cross-functional communication skills (collaborating with risk, trading, and development teams).
 </li>
 </ul>
</div>

Quantitative Analyst

GitMax

Office We are looking for a Quantitative Trader to develop and test original trading models from the ground up. This role focuses on designing fundamentally new strategies using statistical analysis, probabilistic reasoning, and machine learning. This is not a template-based or traditional derivatives trading position we value creativity, initiative, and independence. Key Responsibilities: <ul> <li>Research and build quantitative trading models and signal-generation algorithms;</li> <li>Invent and validate new market factors and features;</li> <li>Work with large historical datasets using a well-established backtesting infrastructure;</li> <li>Contribute to cross-market and cross-asset models with real capital exposure;</li> <li>Earn performance-based bonuses tied to the profitability of your models (top performers can earn multiples of their base compensation).</li> </ul> Requirements: <ul> <li>Proficient in Python (numpy, pandas, sklearn, etc.);</li> <li>Solid knowledge of probability theory, statistics, and econometrics;</li> <li>Experience with ML frameworks is a plus;</li> <li>Ability to design and implement models independently, not just fine-tune existing ones;</li> <li>English proficiency required;</li> </ul> Why Join Us: <ul> <li>Work on real models deployed across diverse global markets;</li> <li>Lean team structure with high impact and visibility;</li> <li>Tight-knit, high-caliber quant team (3 5 people max);</li> <li>Clear path from intern to core trader with strategic ownership opportunities</li> </ul>

Quantitative Trader

BBGC

<div>Requirements</div><div>Previous work experience working as a quant in an energy commodity trading organisation, preferably in LNG, gas, power or oil.</div><div>Experience in modelling spot/forward price processes, building Monte Carlo simulation tools, multifactor models, gas storage models, and commodity option pricing (spread and exotic), pricing complex commodity structures, modelling stochastic volatility and correlation in commodity prices.</div><div>Good knowledge of energy commodity derivatives and hedging exposure management</div><div>Expert-level coding skills in a language such as Python, C#, or C++</div><div>Education: PhD or MS (PhD preferred) in a quantitative subject such as Physics, Mathematics, Statistics, Computer Science, Engineering, or related.</div>

Energy Trading Quant Analyst

Qube Research & Technologies

Your future role within QRT: 
As a Quantitative Developer, you'll work in a close-knit global team of Multi-Asset Quant Developers and Researchers. Working directly with Researchers and Portfolio Managers, you'll enhance our research platform to meet the needs of a constantly evolving environment. You will have a large impact on the success of the team by recommending innovative software solutions - solving problems by building new tools and systems.
 Responsibilities 
<ul>
<li>Automate and streamline data preparation and cleaning</li>
<li>Build and support new quantitative trading framework</li>
<li>Industrialize trading strategies developed by quant researchers</li>
<li>Drive "best practice" amongst the researchers and portfolio managers</li>
<li>Continually expand and upgrade the team s software platform to accommodate the changing business needs</li>
</ul>
 Your present skillset: 
<ul>
<li>3-8 years of experience as a software engineer</li>
<li>Python language experience is a must</li>
<li>Ability to multitask, set project priorities and work in a team</li>
<li>Strong communication skills is a must</li>
<li>Ability to work in a fast-paced environment</li>
<li>Very high standards in code quality and good development practices</li>
<li>Experience in finance and/or machine learning would be beneficial</li>
</ul>

Quantitative Developer - Python

Your future role within QRT
This team is integral to the firm s success. As such, your responsibilities will include:
<ul>
<li>Collaborating with the trading desk to design and implement their data pipelines</li>
<li>Collaborating with data engineers to onboard relevant datasets for the desk within QRT</li>
<li>Implementing data transformations to produce high added-value datasets used for investment decisions</li>
<li>Prototyping and designing code to extract, clean, and aggregate data from a wide range of raw sources and formats</li>
<li>Managing the end-to-end process of onboarding new datasets</li>
<li>Proactively solving data related problems to minimise time to production</li>
<li>Innovating and experimenting with novel data extraction methods to enhance the firm s data onboarding toolkit</li>
</ul>
 Your present skillset 
<ul>
<li>3+ years of experience as a Data Scientist (or similar position); experience in a buy-side quantitative finance role is advantageous</li>
<li>Postgraduate degree in a quantitative discipline such as Mathematics, Physics or Engineering.</li>
<li>Advanced programming experience in Python, including proficiency with data handling libraries such as Pandas and NumPy</li>
<li>Demonstrable interest in financial markets and the application of data in its analysis and understanding</li>
<li>Experience working with both traditional and alternative financial datasets</li>
<li>Excellent communication skills, with the ability to effectively collaborate with all stakeholders, including researchers, traders, engineers, management, and external vendors</li>
<li>Ability to work in a high-performance, high-velocity environment</li></ul>

Data Scientist - Commodities

OneBullEx

<div>OneBullEx is a crypto futures exchange startup built for speed, reliability, and innovation. Our platform delivers advanced futures trading capabilities powered by AI-driven 300 SPARTANS &mdash; automated, high-precision trading strategies designed to thrive in market chaos.

As The Innovative Market Handler and The Alchemist of Order, OneBullEx is forging structure from the volatility of the crypto markets. Our elite 300 SPARTANS stand at the frontier of algorithmic warfare &mdash; self-evolving strategies engineered to dominate market turbulence.

The market never sleeps &mdash; and neither do we.

Now, we are seeking a master strategist &mdash; a Quantitative Researcher &mdash; to join our ranks and lead the charge in building the next generation of data-driven trading intelligence.

Your Mission
<ul>
<li>We are looking for a Quantitative Researcher to explore the uncharted territories of global financial markets in search of Alpha.</li>
<li>This is where your ideas become code, and your code becomes profit.</li>
<li>You will collaborate with top-tier engineers, data scientists, and researchers to design, backtest, and deploy scalable, profitable trading systems.</li>
<li>Your genius will help us find order in chaos &mdash; uncovering signals hidden in oceans of data to fuel our relentless pursuit of performance.</li>
</ul>
What You Will Do (Your Arsenal of Impact) 
<ul>
<li>Create &amp; Destroy: Design, code, and backtest strategies that challenge the status quo &mdash; destroy the weak, refine the strong.</li>
<li>Teach the Machine: Apply cutting-edge machine learning and deep learning to extract alpha and build autonomous, evolving trading intelligence.</li>
<li>Play with Data: Dive into massive datasets, visualise unseen patterns, and turn raw chaos into structured opportunity.</li>
<li>Level Up: Continuously monitor, refine, and evolve strategies to maintain dominance in dynamic markets.</li>
<li>Collaborate &amp; Conquer: Partner with the AI Engine Development team to push the boundaries of trading research and infrastructure.</li>
</ul>
We are Looking for Someone Who
<ul>
<li>Has 3+ years of proven experience in prop trading, systematic trading, or quantitative strategy research (crypto, futures, or FX).</li>
<li>Holds a Master's degree or PhD in Computer Science, Financial Engineering, Applied Mathematics, Physics, or Economics &mdash; or equivalent real-world expertise.</li>
<li>Is obsessed with machine learning, deep learning, or reinforcement learning, and has a track record of applying them to live markets.</li>
<li>Writes code that thinks &mdash; fluent in Python, C++, Rust, or another high-performance language.</li>
<li>Possesses sharp analytical instincts and a relentless drive to uncover hidden truths in data.</li>
<li>Communicates clearly and effectively in English &mdash; because clarity is power.</li>
</ul>
This Role Is for You If
<ul>
<li>You are driven by curiosity and never stop asking &quot;Why?&quot;.</li>
<li>You thrive in the unknown, building answers where none exist.</li>
<li>You believe in team synergy over ego &mdash; and understand that genius scales through collaboration.</li>
<li>You live for the thrill of seeing your ideas move real markets.</li>
</ul>
What We Offer
<ul>
<li>Limitless Growth: Industry-leading compensation directly tied to your performance. No ceiling, only sky.</li>
<li>Total Freedom: A research-first environment &mdash; no bureaucracy, just results.</li>
<li>Elite Tools: Access to state-of-the-art infrastructure and data resources to turn your vision into reality.</li>
<li>A-Team Energy: Collaborate with world-class quants, engineers, and innovators who will challenge and elevate you.</li></ul></div>

Quantitative Researcher

Trade W

<div>
 
 
 Company Description
 
 
 
 Trade W is a leading multi-asset trading platform with over seven years of industry experience, providing global users with secure, convenient, and efficient access to the financial markets.
 
 
 
 
 
 Role Description
 
 
 This is a full-time on-site role for a Quantitative
 
 Developer
 
 , located in Dubai.
 
 
 
 
 
 Responsibilities
 
 
 Market-Making Optimization &amp; Execution
 
 <ul>
 <li>
 Implement quoting logic, inventory targets, dynamic spreads, passive/active switching, and hedge intensity.
 </li>
 <li>
 Link toxicity/mark-out (e.g., VPIN/Kyle &lambda;) to spreads and hedging.
 </li>
 <li>
 Build multi-LP / cross-exchange routing and cost-aware execution
 </li>
 </ul>
 
 Strategy Research &amp; Signal Development
 
 <ul>
 <li>
 Formulate testable hypotheses for MM/stat-arb/funding-basis/order-book/flow-toxicity signals.
 </li>
 <li>
 Engineer features (L1/L2, imbalance, mark-out, volatility, liquidity/impact) and produce evidence-based research notes.
 </li>
 <li>
 Translate findings into parameterized rules and production-ready configs.
 </li>
 </ul>
 
 Backtesting &amp; Performance Evaluation
 
 <ul>
 <li>
 Build trustworthy back tests with cost/slippage/latency/partial-fill modeling and venue-specific constraints.
 </li>
 <li>
 Run walk-forward/rolling validation, capacity &amp; turnover analysis, and P&amp;L attribution (spread, fees, carry/basis, slippage).
 </li>
 <li>
 Perform stress/scenario tests; track Sharpe/IR, max DD, VaR/ES; run ablations to verify signal credibility.
 </li>
 </ul>
 
 Trading Data Analysis &amp; Live Iteration
 
 <ul>
 <li>
 Analyze orders/trades/positions for mark-out, adverse selection, TCA, and market-quality KPIs.
 </li>
 <li>
 Segment users (retail/pro/arb/HFT) to inform MM parameters and routing.
 </li>
 <li>
 Maintain daily dashboards &amp; alerts; monitor backtest&harr;live drift and close gaps quickly.
 </li>
 <li>
 Run parameter/signal A/B experiments; publish concise weekly updates and quarterly roadmaps.
 </li>
 <li>
 Keep runbooks for events/new listings (risk bands, spreads, leverage) and coordinate rollouts with Dev/Risk.
 </li>
 </ul>
 
 Framework &amp; Systems
 
 <ul>
 <li>
 Design an end-to-end framework where research = backtest = simulation = production (unified data, cost/latency models, parameterized configs, experiment tracking, auto-reports).
 </li>
 <li>
 Real-time risk monitoring: inventory/exposure/leverage/hedge deviation/latency/failure-rate with anomaly detection, circuit-breakers, grade-down, and auto-recovery.
 </li>
 </ul>
 
 
 
 
 Must-have
 
 <ul>
 <li>
 Languages:
 
 C++
 
 </li>
 <li>
 Financial modeling &amp; statistics (probability/time-series, microstructure awareness); comfort with perps/futures, funding &amp; basis.
 </li>
 <li>
 Data analysis: Python (pandas/numpy/numba/asyncio/statsmodels/scikit-learn) and strong SQL; careful EDA and reproducible notebooks.
 </li>
 <li>
 Backtesting discipline: cost/impact modeling, leakage controls, validation hygiene.
 </li>
 <li>
 Engineering mindset: clean code, testing, ability to convert research into robust production rules/services.
 </li>
 <li>
 Clear communication: crisp writing, explicit assumptions, defensible conclusions for business/risk stakeholders.
 </li>
 <li>
 2-8 years of experience
 </li>
 </ul>
 
 
 
 
 Tech Stack (reference)
 
 <ul>
 <li>
 Languages: C++, Java, Python, SQL
 </li>
 <li>
 Data: PostgreSQL, ClickHouse, kdb+
 </li>
 <li>
 Analysis/Modeling: NumPy, pandas, statsmodels, scikit-learn, PyTorch/TensorFlow, cvxpy, QuantLib, arch
 </li>
 <li>
 Backtesting: vectorbt, backtrader, zipline
 </li>
 <li>
 Visualization: Matplotlib, Seaborn, Plotly, Tableau/PowerBI
 </li>
 <li>
 Workflow: Jupyter, Airflow/Dagste, Git
 </li>
 <li>
 Crypto(Optional): web3.py/ethers/solana-py, Dune/Flipside, Safe/Fireblocks
 </li>
 </ul>
 
 
 
 
 Nice-to-have
 
 <ul>
 <li>
 Order-book microstructure, MM/inventory control; toxicity metrics (mark-out/VPIN/Kyle &lambda;).
 </li>
 <li>
 ClickHouse/PostgreSQL, materialized views, high-throughput writes/queries; dashboards (Metabase/Superset/Plotly); experiment/version tracking.
 </li>
 <li>
 Streaming &amp; orchestration: Kafka/Redpanda, Flink or Spark Streaming, Airflow/Dagster; data quality (e.g., Great Expectations).
 </li>
 <li>
 Systems &amp; deployment: Linux, Docker/K8s, Prometheus/Grafana, GitHub Actions, observability/traceability.
 </li>
 <li>
 Production MM or cross-exchange arb track record; FIX/REST/WebSocket integrations (Binance/OKX/Bybit, etc.).
 </li>
 <li>
 Low-latency optimization (zero-copy, lock-free queues, batch I/O, kernel/network tuning).
 </li>
 <li>
 DEX/on-chain analytics (Dune/The Graph; web3.py/ethers/solana-py)
 </li>
 </ul>
</div>

Quantitative Developer (C++)

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