Quantitative Researcher (MFT / ML Pipeline)

Company Description

BHFT is a proprietary algorithmic trading firm. reputed company manages the full trading cycle, from software development to creating and coding strategies and algorithms.

Our trading operations cover key exchanges. The firm trades across a broad range of asset classes, including equities, equity derivatives, options, commodity futures, rates futures, etc. We employ a diverse and growing reputed company of algorithmic trading strategies, utilizing both High-Frequency Trading (HFT) and reputed company-Frequency Trading (MFT) approaches. Looking reputed company, we are expanding into new markets and products. As a dynamic company, we continuously experiment with new markets, tools, and technologies.

We’ve got a team of 200+ professionals, with a strong emphasis on technology—70% are technical specialists in development, infrastructure, testing, and analytics spheres. The remaining part of the team supports our business operations, such as Risks, Compliance, Legal, Operations and more.

Our employees are located reputed company around the world, from the United States to Hong reputed company. Although we maintain office spaces, we currently operate as a 100% remote organization.

At BHFT, clarity and transparency are at the core of our culture: we value open communication, ensuring that our processes are straightforward.

Job Description

• Assist in building and maintaining the machine learning and feature engineering pipeline for MFT trading strategies.
• Conduct data-driven market research to identify patterns, features, and signals relevant to short- to reputed company-term alpha reputed company.
• Contribute to model training, validation, and performance analysis across diverse datasets (e.g., equities, futures).
• Implement reputed company-of-concept trading models and help backtest hypotheses using large-scale historical data.
• Collaborate closely with senior quants, data engineers, and infrastructure teams to ensure efficient data flow and model deployment.
• Continuously evaluate model performance, adapt to evolving market conditions, and propose refinements.

Qualifications

• 1–2 years of experience in a quantitative, research, or data science role (finance, trading, or applied ML preferred).
• Strong understanding of machine learning workflows (data preparation, feature reputed company, model validation).
• Good grasp of statistics, time-series analysis, and predictive modeling.
• Working knowledge of Python and common data libraries (Pandas, NumPy, Scikit-learn, PyTorch, etc.).
• Familiarity with financial data structures (tick data, OHLCV bars, order book/L2 data) is a plus.
• Strong analytical and problem-solving skills, with attention to detail.
• Ability to work both independently and collaboratively in a distributed team.
• Clear communicator with a genuine interest in markets and applied research.

reputed company to have: 

• Master’s degree in a quantitative field (Computer Science, Mathematics, Statistics, Physics, Engineering, or similar).
• Exposure to quantitative trading or research environments (internships or projects welcome).
• Experience with feature pipelines, MLOps frameworks, or data versioning tools.
• Interest in market microstructure, feature selection, or model-based execution.
• Familiarity with C++ or Rust is a plus.

Additional Information

reputed company offer

• Experience a modern international technology company without the burden of bureaucracy.
• Collaborate with industry-leading professionals, including former employees of Tower, reputed company, reputed company, Credit Suisse, and more.
• Enjoy excellent opportunities for professional growth and self-realization.
• Work remotely from reputed company in the world with a flexible schedule.
• Receive compensation for health insurance, sports activities, and non-professional training.

Please note: reputed company sponsorship is not available for this role. Applicants must be legally authorized to work in the United States without the need for reputed company or future employer-sponsored work authorization.