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About the role
Role Overview: Hillroute is a regulated quantitative hedge fund specializing in global digital asset trading. With cutting-edge technology and advanced quantitative techniques, the team achieves exceptional risk-adjusted returns. The collaborative approach with trusted service providers ensures transparency and effective communication. The team of seasoned professionals excels in the fast-paced digital asset industry, with expertise in quant trading, risk management, and machine learning. Key Responsibilities: - Utilize ML Ops techniques for managing ML experimentation at scale. - Effectively manage and coordinate ML experiments to ensure smooth operations. - Utilizing tools like weights and biases and coordinating with quantitative developers to set up experimentation frameworks. - Research and discover ML factors that can enhance trading strategies. - Identify and explore new features, data sources, and methodologies to improve ML models. - Research and analyze news data to identify relevant features for asset price prediction. - Implement LLM models to process news data and generate predictions. - Improve existing strategies. - Extensive programming background; with Python, Pandas, GitHub, and AWS being essential. - Utilize machine learning frameworks such as XGBoost, PyTorch, and TensorFlow. - Collaborate with the fund manager to create new or improve existing quantitative trading strategies using in-house platforms. - Design, implement, and optimize various machine learning models aimed at predicting liquid assets using a wide set of financial data and a vast library of trading signals. - Apply advanced mathematical techniques to model and predict market movements accurately. Qualifications Required: - 2 - 5 years of relevant work experience in machine learning and deep learning experimentations. - Solid programming skills in languages such as Python. - MS ideally in a quantitative data science discipline (e.g., statistics, computer science, mathematics, electrical engineering, outcomes research). - Must have a significant track record of machine learning implementations (modeling, data engineering, and experiment management). - Data and Feature Engineering; Dimensionality reduction techniques. - XGBoost and TensorFlow modeling frameworks preferred (PyTorch will also be considered). - Experience with experiment management concepts and frameworks. - Demonstrable exposure to research (execution and presentation) in an ML context will be an advantage. - Excellent analytical and problem-solving abilities, with a focus on working with large datasets. Additional Details: Although Hillroute works from the office in New Delhi, the company is flexible in its style and approach. Note: The benefits section was omitted as it did not contain any specific information relevant to the job description. Role Overview: Hillroute is a regulated quantitative hedge fund specializing in global digital asset trading. With cutting-edge technology and advanced quantitative techniques, the team achieves exceptional risk-adjusted returns. The collaborative approach with trusted service providers ensures transparency and effective communication. The team of seasoned professionals excels in the fast-paced digital asset industry, with expertise in quant trading, risk management, and machine learning. Key Responsibilities: - Utilize ML Ops techniques for managing ML experimentation at scale. - Effectively manage and coordinate ML experiments to ensure smooth operations. - Utilizing tools like weights and biases and coordinating with quantitative developers to set up experimentation frameworks. - Research and discover ML factors that can enhance trading strategies. - Identify and explore new features, data sources, and methodologies to improve ML models. - Research and analyze news data to identify relevant features for asset price prediction. - Implement LLM models to process news data and generate predictions. - Improve existing strategies. - Extensive programming background; with Python, Pandas, GitHub, and AWS being essential. - Utilize machine learning frameworks such as XGBoost, PyTorch, and TensorFlow. - Collaborate with the fund manager to create new or improve existing quantitative trading strategies using in-house platforms. - Design, implement, and optimize various machine learning models aimed at predicting liquid assets using a wide set of financial data and a vast library of trading signals. - Apply advanced mathematical techniques to model and predict market movements accurately. Qualifications Required: - 2 - 5 years of relevant work experience in machine learning and deep learning experimentations. - Solid programming skills in languages such as Python. - MS ideally in a quantitative data science discipline (e.g., statistics, computer science, mathematics, electrical engineering, outcomes research). - Must have a significant track record of machine learning implementations (modeling, data engineering, and exper