The Senior Data Scientist will lead research efforts in analyzing crypto market data, focusing on data sourcing, accessibility, and usability, while applying statistical and machine learning techniques to derive insights and improve trading strategies. The role emphasizes staying current with industry advancements and benchmarking model performance.
The individual in this role will be a part of a new Data Science team, which is focused on generating extensive research across the crypto market and blockchain. The ideal candidate has experience in large-scale data processing and understands the data science workflow. This opportunity will provide the right candidate an opportunity to have a large impact on the research direction and productivity of a newly formed effort within a successful proprietary trading firm.
Responsibilities:
- Data Sourcing: Identify high-quality and relevant data to support research objectives, then create corresponding tickets for the data team.
- Data Accessibility and Usability: Develop and maintain tools and platforms for better visualization and exploration capabilities for the research team.
- Identify Patterns and Trends: Apply statistical and ML techniques to analyze historical data, detecting patterns and trends for trading strategies and research teams.
- Research and Development: Stay updated on the latest advancements in data science and the crypto industry by monitoring research, industry reports, and attending relevant events.
- Benchmarking: Regularly evaluate and improve existing models by benchmarking their performance against state-of-the-art techniques.
- Performance Metrics: Developing metrics and dashboards to track the performance of trading strategies, individual symbols, and the overall portfolio (including PnL attribution).
Requirements:
- Minimum 5 years in data science field, 2 years in leadership roles
- Master’s degree in EE, CS, Physics, Mathematics or other relevant discipline
- Background in large-scale data processing and analysis
- Skilled in Python programming
- Familiar with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Experienced in modeling and forecasting, especially time series analysis (e.g., NLP)
- Experienced in developing and articulating model architecture, strongly emphasizing code quality for effective system design.
- Crypto industry knowledge is helpful but not a requirement.
- Experience in finance and trading is beneficial but not required
Top Skills
Python
Similar Jobs
Artificial Intelligence • Hardware • Information Technology • Machine Learning • Semiconductor
The SMAI Senior Data Scientist will analyze diverse datasets in industrial manufacturing, focusing on statistical modeling, feature extraction, and machine learning. Responsibilities include data extraction using SQL, cleansing, applying analytical techniques, and software development. Knowledge of machine learning methods, toolsets like Python or R, and experience with manufacturing data are required.
Top Skills:
JavaScriptPythonRSQL
Food • Manufacturing
The Senior Data Scientist will enhance supply chain operations by developing data products, advanced algorithms, and insights. Responsibilities include data management, team coordination, and stakeholder engagement, leveraging AI for supply chain optimization.
Top Skills:
PythonSQL
Food • Transportation • Financial Services
The Senior Data Scientist will drive data-driven decision-making by conducting tailored analyses, defining key business metrics, designing and implementing A/B experiments, and developing custom data models. The role focuses on collaboration with product teams to enhance product innovation and optimize features using analytics.
Top Skills:
PythonRSQL
What you need to know about the Singapore Tech Scene
The digital revolution has driven a constant demand for tech professionals across industries like software development, data analytics and cybersecurity. In Singapore, one of the largest cities in Southeast Asia, the demand for tech talent is so high that the government continues to invest millions into programs designed to develop a talent pipeline directly from universities while also scaling efforts in pre-employment training and mid-career upskilling to expand and elevate its workforce.
