Data Scientist

Role Summary

The data scientist understands how data can be used to create business value, identifies opportunities to do so and drives or implements data science projects to implement them.

Main Tasks

1. Working cross-functionally with business managers/product managers/engineers and designers to gather requirements and to understand their business processes.
2. Applying machine learning techniques, in core subject areas including: filter life time predictions, air quality management and recommendations and intelligent industrial filtration.
3. Designing experiments and back-test algorithms using historical data.
4. Making strategic data architecture recommendations.
5. Implementing data science experimental framework to enable organization-wide experiments.
6. Design, develop, test and deploy analysis, hypothesis, and machine learning models that solve business challenges and uncover insights to enhance business performances.
7. Design, implement, and manage data analytics pipelines from end-to-end.
8. Collect, cleanse and transform data from multiple data sources for report generation and visualization for business units and technical audiences.
9. Develop machine learning classifiers, algorithms and processes.
10. Data mining of data sets of various types and formats.
11. Document all aspects of software design, development, debugging, and release.

Your Profile

1. Masters Degree/PHD in Computer Science or a related technical field with the ability to manage stakeholders and communicate well.
2. Strong experience in Machine Learning with at least 5 years of Python or R development experience and good experience in SQL. Experience in Hadoop & Spark is advantageous.
3. Proficient in programming languages such as Python or Java is required.
4. Extensive data analytic experience in Pandas, NumPy, scikit-learn, Jupyter, R, etc.
5. Demonstrated experience applying and showing an understanding of machine learning algorithms for classification, regression, clustering, etc.
6. Developed and deployed applications running on public cloud systems, such as AWS or equivalent.
7. Experience working in Hadoop ecosystem and Spark is a plus.
8. Experience with common data science tools such as R, Python, scipy, sagemaker, AWS, DynamoDB, Tableau.

We offer