Data Scientist

SQL Pager

Sunnyvale, California
30+ days ago

Highlights

Qualifications Master's degree or higher in Computer Science, Computer Engineering, Electrical Engineering or similar discipline with industrial experience in software development. Basic understanding of the pros and cons of different machine learning algorithms, and basic understanding for different types of open source ML frameworks.

Numbers & Facts

Location

Sunnyvale, California

Description

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

Data Scientist

SQL Pager

Highlights

Numbers & Facts

Description

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