Scribd
Data Scientist II
San Francisco · Mid · Contract
Sponsorship not specifiedDetected 104 days ago
PythonAlgorithmsSQLDatabricksMachine LearningDeep LearningPyTorchscikit-learnNumPySparkData ScienceNLPLLMsStatisticsA/B TestingResearchCommunicationCollaboration
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odds of building a lasting career here
40Risky
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
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Files H-1B transfers
8 transfer filings in the last year, covering 8 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
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About the role
- We work in cross-functional teams collaborating with Machine Learning Engineers, Data Engineers and Product.
- We act as a key driver for innovation, whether it's in product surface experimentation, metadata generation or model development.
- Our areas of impact include content enrichment, representation learning, recommendations, search, translation and many others, applied to diverse media across text, image, and audio.
Responsibilities
- Hands-on experience building ML pipelines and working with distributed data processing frameworks like Apache Spark, Databricks, or similar.
- We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences create a foundation for the best ideas.
- Come join us in building something meaningful.
Requirements
- Proficiency in Python.
- Intermediate level or greater experience with SQL or PySpark.
- Employees must have their primary residence in or near one of the following cities.
Compensation
- At Scribd, Inc. your base pay is one part of your total compensation package and is determined within a range.
- Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location.
- In the state of California, the reasonably expected salary range is between $118,000 [minimum salary in our lowest geographic market within California] to $184,000 [maximum salary in our highest geographic market within California].
Benefits
- Comprehensive health, dental, and vision coverage
- Mental health support and disability coverage
- Generous paid time off, including vacation, sick time, holidays, winter break, volunteer time, and sabbaticals
- Paid parental leave and family support benefits
- Retirement matching and employee equity
- Learning and development programs and professional growth opportunities
- Wellness and home office stipends
- Collaborate with other Data Scientists, Machine Learning Engineers and ML Data Engineers on cross-functional projects
- 4+ years of post qualification experience in NLP, developing machine learning models, working with systems at scale and deploying to production environments.
- Intermediate level in at least three of these fields: classification algorithms, natural language processing, search, information retrieval, named entity recognition, deep learning, generative models.
- Scribd Flex (flexible work model)
Company info
- The Applied Research team is a group of data scientists and content specialists who are experts in leveraging machine learning, natural language processing and generative AI models to develop solutions which deliver value to our users and business.
- Along with Product and Engineering partners, we design solutions and collaborate in cross-functional squads to maximize business impact.
- We operate at a scale of hundreds of millions of documents, millions of users and billions of user interactions.
- Role Overview
- We are seeking a Data Scientist II with experience developing and deploying machine learning models.
- You will help design and implement high impact AI and ML systems.
- We are seeking a curious and collaborative individual with an eye for simplicity, end-end visibility and impact and that is excited about building models using massive amounts of data, using language models and deploying models.
This listing is sourced directly from Scribd's careers page and normalized into a canonical job model.