Data Scientist

Job Responsibilities:

1. Model Building and Optimization:

- Design and build machine learning and deep learning models, including data cleaning, feature engineering, model development, and parameter tuning.

- Apply data mining, machine learning, deep learning, and optimization techniques to develop recommendation systems and decision support models.

2. Performance Evaluation and Improvement:

- Design performance metrics and conduct model validation and optimization to ensure accuracy and effectiveness in real-world applications.

- Manage the model lifecycle, continuously monitoring and improving model prediction performance and execution efficiency.

3. Technical Research and Application:

- Enhance technical knowledge by reading papers and technical documents, staying updated with the latest industry trends.

- Conduct reinforcement learning research to address challenges in the financial domain.

- Apply computer vision techniques to complete projects, including defining problem scopes, selecting optimal deep learning methods, and evaluating and optimizing results.

4. Requirements Gathering and Analysis:

- Conduct interviews with business stakeholders to gather relevant information, perform preliminary data exploration, and plan analysis.

- Handle data processing and statistical analysis, and perform forward-looking model predictions.

5. Data Visualization and Reporting:

- Develop data visualization dashboards and assist in designing and testing analytical dashboards and platform features.

- Analyze data, create presentations, and regularly compile experimental results and research findings for effective communication to internal and external stakeholders.

6. Product Support and Issue Analysis:

- Assist internal product managers in analyzing potential research and development issues for future projects, providing technical support and recommendations.

Essential Qualifications:

1. At least one year of hands-on experience in the full data analysis process.

2. Relevant experience in data analysis, including data mining, machine learning, deep learning, optimization, statistical modeling, and natural language processing (NLP).

3. Proficiency in Python and related libraries (such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).

4. Familiarity with SQL, capable of performing complex queries and data operations.

5. Practical experience in model optimization and parameter tuning.

6. Prepared a 5-10 minute PPT presentation of portfolio work.