Demo

Data Scientist

Search & Apply.io
Sunnyvale, CA Full Time
POSTED ON 1/17/2025
AVAILABLE BEFORE 4/17/2025

Please apply using the following link :

  • Application Link

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 of different types of open source ML frameworks.
  • Understanding of hypervisors / containers, especially Docker.
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