Entry Level Data Analyst with AI Knowledge
About Zensar Technologies
Zensar is a leading digital solutions and technology services company partnering with global organizations on their digital transformation journey. A technology partner of choice, with strong track-record of innovation, credible investment in digital solutions and assertion of commitment to client’s success, Zensar’s comprehensive range of services and solutions enable clients achieve new thresholds of performance. Part of the $40 billion APAX Partners’ portfolio of companies, Zensar is uniquely positioned to help existing businesses run efficiently, manage legacy transformation and plan business growth through innovative digital platform. https://www.zensar.com
Working at Zensar
Working at Zensar is an enriching experience. While work is driven by innovation and passion, fun is taken seriously too. An open environment is encouraged making it easy to brainstorm with colleagues. Creative thinking is encouraged through time out activities. Moreover, the offices have been designed to foster creativity and communication, bringing a little bit of home into work every day. Zensar provides and a comprehensive benefit package for all fulltime employees.
Zensar is seeking entry level Data Analyst with AI knowledge remote in the USA.
What you will be doing:
Data Readiness and Value Assessment:
- Evaluate the Value of AI:
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- Operators need to determine which AI applications will provide the most significant benefits to their network operations.
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- AI models rely on high-quality data. Operators must evaluate their data sources, data flows, and the readiness of their data for AI applications.
- Operators need to collect the necessary data sets, including potentially data shared by external partners, and ensure that data is properly cleaned, transformed, and prepared for training AI models.
- Infrastructure and Expertise:
Model Training and Inference:
- AI workflows involve two main phases: model training, which is resource-intensive and typically done in a core data center or public cloud, and model inference, which is more latency-sensitive and best hosted at the digital edge.
Infrastructure Requirements:
- Operators need to ensure they have the necessary infrastructure to support both model training and inference, including compute power, storage, and network bandwidth.
Internal Expertise:
- Operators may need to invest in training their staff or hiring AI specialists to manage and maintain AI-powered systems.
- Continuous Improvement and Monitoring:
- Performance Monitoring
- Operators must continuously monitor the performance of their AI models to ensure they are meeting their objectives and making accurate predictions.
Model Retraining and Improvement:
- As data and network conditions change, operators need to continuously retrain and improve their AI models to maintain their accuracy and effectiveness.
Actionable Insights:
- Operators need to translate the insights generated by AI models into actionable steps that improve network performance, reduce costs, and enhance customer experience.
- 4. Specific AI Applications and their Impact:
Predictive Maintenance:
- AI can analyze historical data to forecast equipment failures and performance degradation, allowing for proactive maintenance scheduling.
Dynamic Resource Allocation:
- AI can optimize resource allocation, such as bandwidth, spectrum, and compute power, in real-time to meet changing network demands.
Network Traffic Optimization:
- AI can analyze network traffic patterns to optimize routing, reduce congestion, and improve network performance.
Enhanced Customer Experience:
- AI can personalize customer experiences by providing tailored services, optimizing network performance for different user needs, and providing better support.
Collaboration and Partnerships:
- Vendor and Partner Relationships
- Operators may need to collaborate with vendors and partners to develop and implement AI-powered solutions.
Open Data Sharing:
- Operators may need to consider open data sharing initiatives to leverage the power of community-driven AI development.
What you need to have
- Identification and labeling of named entities in text, such as companies, locations, job titles, and skills.
- Classifying documents into various categories.
- Validating outputs of Machine Learning models – This will be the major work
- Identifying common patterns in datasets.
- Ensuring accuracy and reliability in data annotation.
- Knowledge with Network Engineers
Education: Bachelor’s in engineering. Master Preferred
Disclaimer:
Equal Employment Opportunity and Affirmative Action at Zensar Technology is an Equal Employment Opportunity (EEO) and Affirmative Action Employer encouraging diversity in the workplace. All qualified applicants will receive consideration for employment without regard to their race, creed, color, ancestry, religion, sex, national origin, citizen status, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.