Zensar Technologies – DE&A – AIML – Data Science (Data Creation, Labeling & Model Evaluation)

Job Description

The DE&A – AIML – Data Science role at Zensar Technologies is focused on supporting the development and evaluation of Artificial Intelligence and Machine Learning models through high-quality data creation and annotation. This position plays a vital role in enabling AI systems to learn accurately by ensuring that training and validation datasets are precise, well-structured, and logically consistent. The role is especially aligned with use cases in autonomous systems and computer vision, where data quality directly impacts model performance and safety.

In this role, you will contribute to labeling and evaluating complex datasets, including visual data that requires attention to detail and strong contextual understanding. A key aspect of the work involves describing scenes clearly, applying logical reasoning, and ensuring annotations reflect real-world conditions such as road signs, traffic behavior, and environmental context. Your contributions help data scientists and ML engineers build, test, and refine models that perform reliably in real-world scenarios.

This opportunity is well-suited for individuals who are detail-oriented, language-proficient, and curious about how AI systems learn from data. You will work within Zensar’s Data Engineering and Analytics ecosystem, collaborating with global teams in an environment that values learning, inclusivity, and innovation. The role offers strong exposure to practical AI workflows and serves as an excellent foundation for a long-term career in data science and applied machine learning.

Roles & Responsibilities

• Create, label, and validate datasets used for training and evaluating AI and machine learning models.

• Perform accurate annotation of visual data, including bounding boxes and other computer vision labeling techniques.

• Describe scenes clearly and logically to ensure annotations capture real-world context and intent.

• Apply reasoning and consistency checks to ensure data quality across large-scale datasets.

• Evaluate labeled data to identify errors, ambiguities, or inconsistencies that could affect model performance.

• Collaborate with data science and AI teams to understand labeling guidelines and project objectives.

• Follow defined annotation standards, workflows, and quality benchmarks for data creation tasks.

• Contribute to continuous improvement of data labeling processes through feedback and best practices.

• Ensure datasets align with regional and domain-specific requirements such as road signs and traffic rules.

Requirements & Eligibility

• College education in any discipline, with a strong interest in data science, AI, or machine learning domains.

• Strong English writing skills, particularly in describing scenes, actions, and logical flows accurately.

• Fluency in English with the ability to understand and document detailed contextual information.

• Familiarity with road signs and driving environments in the United States; exposure to EU regions is an added advantage.

• Prior experience or exposure to AV or computer vision data labeling, including bounding boxes, is preferred.

• High attention to detail and ability to maintain accuracy while working with large datasets.

• Basic understanding of how labeled data is used in AI/ML model training and evaluation.

• Willingness to learn new tools, annotation platforms, and AI-related workflows.

Expected Salary

For entry-level Data Science Data Creation and Labeling roles at Zensar Technologies in India, the expected salary typically ranges between ₹3 LPA to ₹6 LPA, depending on skills, prior exposure to AI data workflows, and performance during selection. Zensar also provides structured learning opportunities and a supportive environment for long-term career growth in analytics and AI.

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