What are the responsibilities and job description for the Data Scientist position at SQL Pager LLC?
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 group 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
