If you’re skillful Data Science Engineer, stay at home, we have a project for you that needs your hands.
TECHNOLOGIES:
Python
SQL
AWS
Project overview
Project scope and responsibilities
Hello,
We are looking for an experienced Data Science Engineer for our client to join an international team and help us grow.
We’re looking for someone for whom working with data is both a calling and a passion. As a Data Science Engineer specializing in data pipelines, you’ll play a key role in developing, deploying, and optimizing cloud solutions that deliver business value and enhance the user experience.
What can you expect during your day?
- Data Engineering: Managing data mining, collecting and processing large volumes of data, and creating appropriate data models.
- NLP and Machine Learning: Researching, promoting, and implementing semantic capabilities through Natural Language Processing, text analysis, and ML techniques.
- Analysis and Business Insights: Defining requirements and the scope of analyses, and presenting and reporting business outcomes to management using visualization tools.
- Model Optimization: Evaluation and research in the field of optimizing data models and algorithms to improve the accuracy of analyses.
- Problem-solving: Identifying the root causes of organizational problems and proposing alternative solutions.
What do we expect from you?
- At least 4 years of experience in a relevant position in the sector.
- English proficiency at a minimum B2 (Upper Intermediate) level.
- Strong analytical thinking and the ability to clearly communicate complex technical concepts to a variety of audiences.
- Ability to work in a dynamic and collaborative international environment.
Technical Skills and Knowledge
- Programming and Data: Proven experience with Python (NumPy, SciPy, Pandas, etc.) and SQL.
- Cloud and ETL: Experience with AWS, Cloud ELT/ETL, and Snowflake.
- Machine Learning: Practical applications of ML techniques (Clustering, Logistic Regression, Random Forests, SVM, Neural Networks).
- Architecture and Lifecycle: Strong knowledge of the end-to-end data lifecycle (traditional data warehouses, databases, data lakes, data mesh, data fabric).
- Data Governance: Deep understanding of data governance, data quality, metadata, modeling, and architecture.
Project requirements
