Data Analyst/Data Scientist

Hourly rate: members only

Availability: members only

Willingness to travel: Nearby (100 km)

Professional status: Student

Last updated: 20 Mar 2024

Total work experience:

Language skills: English,

Personal summary

Data Collection and Preparation: Gather and preprocess data from various sources, ensuring its quality, completeness, and suitability for machine learning tasks. Clean, transform, and feature engineer datasets to extract relevant information and improve model performance. Collaborate with data engineers to design and implement data pipelines for efficient data ingestion and processing. Model Development: Assist in developing and refining machine learning models to solve specific business problems. Conduct exploratory data analysis to gain insights into the data and identify potential modeling approaches. Implement and fine-tune machine learning algorithms, considering factors such as accuracy, scalability, and interpretability. Experiment with different modeling techniques and frameworks to optimize model performance. Experimentation and Evaluation: Design and execute experiments to evaluate the performance of machine learning models and algorithms. Analyze experimental results and iterate on models to improve their effectiveness. Develop metrics and benchmarks to assess the impact of machine learning solutions on key business metrics. Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to integrate machine learning capabilities into Amazon's products and services. Communicate effectively with stakeholders to understand requirements, provide updates on progress, and solicit feedback. Participate in design reviews, code reviews, and other collaborative activities to ensure the quality and reliability of machine learning solutions. Documentation and Reporting: Document the development process, including data sources, preprocessing steps, model architecture, and evaluation results. Prepare reports, presentations, and other artifacts to communicate findings and insights to both technical and non-technical audiences. Maintain documentation and version control for code, models, and other project artifacts. Continuous Learning: Stay abreast of the latest developments in machine learning research and technology. Participate in training programs, workshops, and conferences to enhance your skills and knowledge. Share learnings and best practices with team members to foster a culture of continuous improvement.

Skills