Machine Learning Scientist - Algorithmic Trading

Placements24; Kimberley; Ibrido; CAD 102.000 - 154.000; Tempo pieno

Vantaggi offerti da questo lavoro

Competitive salary and bonus

Health insurance

Retirement contributions

Generous paid time off

Hybrid work model

Professional development

Descrizione del lavoro

Our client, a leading AI and Emerging Technologies firm, seeks a Machine Learning Scientist to join the Quantitative Trading team in Kimberley. You will research, design, and implement ML models for algorithmic trading, analyze large financial datasets, and develop predictive models for asset movements in a high-impact financial environment.

You will backtest strategies, collaborate with researchers and PMs, optimize for low-latency execution, and stay current with ML advances.

Competenze

• Master’s or PhD in Computer Science, Statistics, Mathematics, Physics, Economics, or a related quantitative field.
• 3+ years of experience applying machine learning techniques to financial markets, preferably in algorithmic trading or quantitative research.
• Proficiency in Python and core ML libraries (scikit-learn, TensorFlow, PyTorch, Keras).
• Strong experience with time-series analysis and modeling techniques.
• Experience with financial data analysis and market microstructure.
• Familiarity with deep learning architectures for sequential data (RNNs, LSTMs, Transformers).
• Excellent quantitative, analytical, and problem-solving skills.
• Strong programming skills and experience with data manipulation tools (Pandas, NumPy).
• Effective communication and collaboration skills, able to explain complex models.

Mansioni

• Research, design, and implement machine learning models for algorithmic trading strategies.
• Analyze large financial datasets, including market data, news sentiment, and alternative data sources.
• Develop predictive models for asset price movements, volatility, and other market indicators.
• Build and backtest trading algorithms, evaluating performance and risk-adjusted returns.
• Collaborate with quantitative researchers and portfolio managers to refine and deploy trading strategies.
• Optimize ML models for low-latency execution in live trading environments.
• Stay current with advancements in machine learning, deep learning, and their applications in quantitative finance.
• Contribute to the development of the firm's trading infrastructure and analytical tools.
• Ensure compliance with all relevant financial regulations and firm policies.

Conoscenze

Python proficiency

Time-series analysis

Financial data analysis

Deep learning for sequential data

Communication skills

Formazione

Master's or PhD in CS/Statistics/Math/Physics/Economics

Strumenti

scikit-learn

TensorFlow

PyTorch

Keras

Pandas

NumPy

Descrizione del lavoro

About the Role

Our client, a key player in the AI and Emerging Technologies sector, is seeking a skilled Machine Learning Scientist to join their Quantitative Trading team in Kimberley, Northern Cape. This role is designed for individuals passionate about applying advanced machine learning techniques to financial markets, developing sophisticated trading strategies, and optimizing portfolio performance. You will work on challenging problems involving large-scale time-series data, predictive modeling, and algorithmic execution. This is a unique opportunity to leverage AI in a high-impact, fast-paced financial environment. The ideal candidate possesses a strong quantitative background, excellent programming skills, and a deep understanding of various ML algorithms, particularly those suited for financial applications.

Key Responsibilities

• Research, design, and implement machine learning models for algorithmic trading strategies.
• Analyze large financial datasets, including market data, news sentiment, and alternative data sources.
• Develop predictive models for asset price movements, volatility, and other market indicators.
• Build and backtest trading algorithms, evaluating their performance and risk-adjusted returns.
• Collaborate with quantitative researchers and portfolio managers to refine and deploy trading strategies.
• Optimize ML models for low-latency execution in live trading environments.
• Stay current with advancements in machine learning, deep learning, and their applications in quantitative finance.
• Contribute to the development of the firm's trading infrastructure and analytical tools.
• Ensure compliance with all relevant financial regulations and firm policies.

Requirements

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, Physics, Economics, or a related quantitative field.
• 3+ years of experience applying machine learning techniques to financial markets, preferably in algorithmic trading or quantitative research.
• Proficiency in Python and core ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, Keras).
• Strong experience with time-series analysis and modeling techniques.
• Experience with financial data analysis and market microstructure.
• Familiarity with deep learning architectures relevant to sequential data (e.g., RNNs, LSTMs, Transformers).
• Excellent quantitative, analytical, and problem-solving skills.
• Strong programming skills and experience with data manipulation tools (e.g., Pandas, NumPy).
• Effective communication and collaboration skills, with the ability to explain complex models to non-experts in Kimberley.

Benefits

• Our client offers a highly competitive salary and bonus structure, alongside a comprehensive benefits package including health insurance, retirement contributions, and generous paid time off.
• We provide a stimulating work environment with access to high-quality financial data and cutting-edge technology.
• Opportunities for professional development, attending industry conferences, and continuous learning are actively encouraged.
• This role in Kimberley, Northern Cape offers the chance to make a significant impact in the financial technology space.
• We value work-life balance and offer flexible working arrangements within our hybrid model.
• Join our dedicated team and contribute to the future of algorithmic trading.