Chief Analytics Officer

3 days ago

Gujrat, Punjab, Pakistan beBeeMachineLearning Full time $100,000 - $150,000
Key Responsibilities

The primary objective of this role is to design, implement and deploy machine learning models on large datasets using tree-based methods and deep learning techniques. This includes developing predictive models that can accurately forecast outcomes and regression models that can identify relationships between variables.

In addition, the ideal candidate will be responsible for building and maintaining robust data integration pipelines in Python to integrate and process data from various sources. This involves designing efficient workflows, handling data quality issues and ensuring seamless integration with existing systems.

Furthermore, the successful applicant will apply machine learning and AI techniques to generate, clean and validate proprietary datasets. This includes identifying patterns, anomalies and trends in complex data sets and developing strategies to improve data accuracy and reliability.

To ensure model performance and efficiency, the candidate will conduct backtesting, validation and performance evaluation of data-driven models. This includes monitoring model outputs, refining algorithms and optimizing performance metrics to achieve better results.

Qualifications and Experience
  • A minimum of 6 years of experience in Machine Learning, Data Science or Quantitative Research.
  • Strong proficiency in Python programming language (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
  • Solid understanding of statistical modeling, regression and time-series analysis.
  • Experience designing and deploying predictive and deep learning models.
  • Excellent analytical and problem-solving skills with the ability to work independently.
  • Excellent verbal and written communication skills in English.
  • Bachelor's or Master's degree in Computer Science, Data Science, Engineering or a related field.
Desirable Skills
  • Experience in quantitative finance, trading or investment-related projects.
  • Familiarity with systematic strategies, risk modeling or portfolio optimization.
  • Exposure to cloud platforms (AWS, GCP, Azure) and large-scale data infrastructure.
  • Knowledge of financial data APIs, Bloomberg/Refinitiv or alternative datasets.

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