BRAIN Data Scientist

WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform.

WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement.

Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it.

About Worldquant Braintm
BRAIN is a group within WorldQuant similar to a traditional B2C fintech setup. Our mission is to simplify quant finance and provide global remote-work opportunities to participants, while they learn quant finance, AI and ML concepts. WorldQuant Brain provides an advanced crowdsourcing platform for external participants to contribute signals, data, and more.

WorldQuant Is Seeking a Data Scientist To Join The BRAIN Core Team And Turn Our Ever-growing Data Universe Into Actionable Intelligence That Moves The Business Forward. You Will

• Organize and enrich large, diverse datasets.
• Build and productionize decision-support tools used daily by BRAIN consultants.
• Fast-track the adoption of new technologies—especially large-language-model (LLM) capabilities—to keep the platform up to date.
• Spend a portion of your time on exploratory research and prototyping of next-generation analytics so the team keeps pushing boundaries.

Key Responsibilities
Data → Intelligence

• Curate, combine and transform structured, semi-structured and unstructured data into clean, well documented intelligence layers.
• Develop metrics, features and knowledge graphs that reveal business-relevant signals.
• Maintain data-quality checks and automated monitoring so consultants can trust what they see.

Decision-Support Tools

• Design, build and maintain dashboards, APIs, notebooks and self-service apps that allow consultants to slice data, run analyses and evaluate hypotheses in minutes—not days.
• Own the full lifecycle: requirement gathering, rapid prototyping, production hardening, user training and continuous improvement.

LLM & Advanced Analytics Enablement

• Integrate and fine-tune LLMs for use cases such as semantic search across our data catalog, natural-language querying, code generation and insight summarization.
• Evaluate emerging ML/AI technologies and pilot the most promising ideas; shepherd successful proofs of concept into production.

Cross-Functional Partnership

• Embed with product, engineering and domain specialists to translate business needs into data solutions.
• Provide hands-on guidance to consultants—help them frame questions, interpret results and adopt new tools.

Research & Thought Leadership

• Conduct exploratory studies on novel algorithms, embeddings, or retrieval-augmented generation techniques that could unlock future value.
• Share findings via internal talks, docs and demos to keep the BRAIN community learning.

What You'll Bring

• Technical Expertise: Strong knowledge and hands-on experience in AI, Machine Learning (ML), and LLMs; proficiency in Python and C++; familiarity with PyTorch or TensorFlow.
• Quantitative Skills: Solid foundation in mathematics, statistics, and quantitative modeling; experience with financial datasets is a plus.
• Educational Background: Bachelor's or advanced degree in Computer Science, AI, Mathematics, Financial Engineering, or related fields from a leading university.
• Research Mindset: Creative problem solver with a passion for experimentation and unsolved challenges; strong interest in financial markets.
• Collaborative Skills: Ability to work in a team-oriented culture; excellent communication and presentation skills in English.

[1] WorldQuant defines alphas as mathematical models that seek to predict the future price movements of various financial instruments

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