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Top Remote Entry Level Data Science Jobs in Pittsburgh, PA

20+ Job Results
3 Days Ago
USA
Remote
206 Employees
Entry level
206 Employees
Entry level
Big Data • Cloud • Productivity • Software • Database • Analytics • Automation
The Data Scientist will collaborate with cross-functional teams to design innovative products using advanced data science techniques including statistics, machine learning, and natural language processing. Responsibilities include developing algorithms, measuring impacts, and improving data validation and deployment methodologies.
Top Benefits:
401-K
Commuter Benefits
Company Equity
+52 More
Yesterday
USA
Remote
255 Employees
16-18
Entry level
255 Employees
16-18
Entry level
Pet
The Data Annotation Specialist will review and label video data to support machine learning projects, maintain organized records, and ensure data quality standards. This part-time role offers flexible hours while contributing to improving products for dog owners.
Yesterday
USA
Remote
67 Employees
75K-150K Annually
Entry level
67 Employees
75K-150K Annually
Entry level
Sports • eSports
As a Data Scientist at Zelus Analytics, you will perform data modeling and quantitative analysis, develop and validate statistical models, collaborate with engineering for model implementation, and support research projects aimed at enhancing player and team performance.
2 Days Ago
United States
Remote
1,651 Employees
Entry level
1,651 Employees
Entry level
News + Entertainment
The Performance Science Data Engineer role involves utilizing data science techniques to enhance live entertainment experiences, focusing on analyzing performance metrics and improving overall event execution. The position may collaborate cross-functionally to drive data-driven decisions and optimize audience engagement.
2 Days Ago
USA
Remote
1,500 Employees
25K Annually
Entry level
1,500 Employees
25K Annually
Entry level
Financial Services • Metaverse
The Salesforce Administrator will ensure the accuracy and organization of Salesforce data, perform audits, implement data validation, and create reports and dashboards to support business insights and decision-making.
Top Benefits:
401-K
401-K Matching
Commuter Benefits
+46 More
4 Days Ago
United States
Remote
40 Employees
135K-175K Annually
Entry level
40 Employees
135K-175K Annually
Entry level
Software
As a Data Engineer at Archive, you will build data pipelines, develop AI use cases, architect data infrastructure, and improve reporting practices. You will work with large datasets and collaborate with various teams to solve complex problems and enhance product metrics.
5 Days Ago
United States
Remote
39 Employees
110K-140K Annually
Entry level
39 Employees
110K-140K Annually
Entry level
Software
Research and develop color perception algorithms and computer vision for camera and AR applications, work on imaging technology for camera capture and augmented reality experiences, collaborate with cross-functional teams, and provide leadership for new project development.
7 Days Ago
United States
Remote
4,788 Employees
Entry level
4,788 Employees
Entry level
Digital Media • Gaming • Software
The Research Associate role at a new AI startup involves data collection, QA, data analysis, and improving AI interactions with humans. This position offers opportunities for career advancement and learning new software skills on the job.
7 Days Ago
USA
Remote
55 Employees
Entry level
55 Employees
Entry level
Artificial Intelligence • HR Tech • Machine Learning • Social Impact • Software • Consulting
The Revenue Operations Analyst will manage and optimize the Marketing Ops, Sales Ops, and Customer Success Ops at Wonderlic. Responsibilities include overseeing the RevOps tech stack, ensuring compliance with processes, onboarding team members, streamlining operations, and setting up insightful dashboards.
Top Benefits:
401-K
401-K Matching
Customized Development Tracks
+36 More
9 Days Ago
3 Locations
Remote
91 Employees
Entry level
91 Employees
Entry level
Software • Web3
As a Data Scientist at CoLab, you will design and deploy machine learning models, optimize features with large datasets, conduct A/B testing, and communicate insights through visualizations. Collaboration with engineering teams for model deployment and maintenance is key, along with ensuring data governance and infrastructure optimization.
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