Data Scientist
Please apply using the following link :
https : / / app.workstory.io / public / job?meta eyJpZCI6MTF9
Responsibilities
- Help design, implement, and validate the ML Pipelines while collaborating with other data scientists.
- Coordinate and collaborate with other Software Development groups so that ML Pipeline fits well with the rest of our software applications.
- Balance adding new features with the need for stability and performance.
- Grow development capabilities to align with the pace of business needs.
Qualifications
- Master's degree or higher in Computer Science, Computer Engineering, Electrical Engineering or similar discipline with industrial experience in software development.
- 3+ years of experience with Python coding.
- 3+ years of recent experience working as a Data Scientist in industry.
- Experience with developing production-grade code, preferably in Python.
- Experience with data science and machine learning, including Python libraries such as NumPy, SciPy, Pandas, and Scikit-learn.
- Strong professional written and verbal communication skills.
- Ability to pass a Data Science skills-based test.
- Experience with relational or NoSQL databases such as Oracle, Cassandra, Redis, or similar.
- Ability to create model-ready data from raw data, at scale.
- Ability to translate business problems into data science pipelines.
- Comfort with ML theory to recommend solutions beyond the standard libraries.
- Must be able to work independently and as part of a diverse interdisciplinary and international team.
- Communicates clearly to technical and non-technical audiences.
- Empathy with customer business challenges.
- Ability to map business problems to software and data science techniques.
- Understanding of fundamental data science and machine learning pipeline including data cleansing, feature engineering, imputation, model tuning, and model prediction.
- Basic understanding of the pros and cons of different machine learning algorithms, and basic understanding for different types of open source ML frameworks.
- Understanding of hypervisors / containers, especially Docker.
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