Data Science Intern – Hyderabad

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Internship opportunity; no minimum number of years of work experience is specified in the supplied description2 days ago

ExperienceInternship opportunity; no minimum number of years of work experience is specified in the supplied description

QualificationBachelor’s or master’s degree in Computer Science, Computer/Software Engineering, or a related field, or an equivalent combination of education

Key Responsibilities

Work with product managers, senior engineers, and other team members to understand requirements.
Prototype and help build large-scale distributed applications.
Develop and implement custom machine learning models for real-world challenges.
Translate business requirements into technical solutions and model designs.
Clean, preprocess, and analyse large datasets.
Ensure data quality and integrity throughout the modelling process.
Address imbalanced datasets to improve model performance.
Prepare suitable data for training, validation, and evaluation.
Deploy and host machine learning models in suitable environments.
Help ensure that deployed models are scalable and reliable.
Monitor models in production and support ongoing maintenance.
Implement feedback loops to improve models continuously.
Evaluate models using suitable performance metrics.
Identify and help mitigate model biases.
Assess model accuracy and fairness against the intended use case.
Use evaluation results to inform model improvements.
Work with product managers, engineers, and other stakeholders to understand business needs.
Translate business requirements into technical requirements and architecture.
Explain technical concepts and findings clearly to non-technical stakeholders.
Work through ambiguous problems, incorporate feedback, and deliver improvements iteratively.
Document data processing and modelling procedures.
Record model performance and relevant evaluation results.
Provide insights and recommendations based on the analysis.
Communicate findings clearly to relevant team members.

Skills & Eligibility

The supplied description accepts a bachelor’s or master’s degree in Computer Science, Computer Engineering, Software Engineering, or a related concentration. An equivalent combination of education may also be considered, according to the job description.
Applicants should check the official listing for any specific requirements regarding current enrolment, graduation dates, degree specialisation, or eligible graduating batches. These details are not confirmed in the supplied information.
Candidates should have a solid foundation in computer science, including:
Data structures and algorithms.
Software design principles and design patterns.
Programming fundamentals and problem-solving.
Continuous integration and continuous delivery (CI/CD) concepts.
Understanding how software components are designed, tested, and maintained.
Proficiency in Python or R is required by the supplied description. Applicants should be comfortable writing code to process datasets, implement model experiments, evaluate results, and build reproducible workflows.
Experience with machine learning libraries such as TensorFlow, PyTorch, or Scikit-learn is also required or expected under the listed qualifications.
Candidates should understand data preprocessing techniques and how to handle imbalanced datasets. Practical knowledge should include cleaning records, identifying missing or inconsistent values, preparing features, selecting suitable evaluation metrics, and checking how class imbalance affects model performance.
Familiarity with deploying machine learning models using AWS, Google Cloud Platform, or Microsoft Azure is listed as a qualification. Applicants should understand how a model moves from development into a hosted environment and what is required to operate it reliably.
The description highlights knowledge of evaluation metrics and techniques for identifying and reducing model bias. Candidates should be ready to explain how they assess a model beyond simply reporting accuracy.
A good understanding of large language model architecture, fine-tuning, and training LLMs is listed in the supplied requirements. The description also specifically calls for a robust understanding of AI agents.
Applicants should be prepared to discuss relevant projects, explain the design choices they made, and describe how they evaluated the reliability and usefulness of their systems.
The supplied description accepts a bachelor’s or master’s degree in Computer Science, Computer Engineering, Software Engineering, or a related concentration. An equivalent combination of education may also be considered, according to the job description.
Applicants should check the official listing for any specific requirements regarding current enrolment, graduation dates, degree specialisation, or eligible graduating batches. These details are not confirmed in the supplied information.
Candidates can focus their preparation on the technical areas most relevant to the role.
Python or R: Practise data manipulation, writing reusable functions, debugging, and building reproducible analyses.
Machine learning: Understand supervised learning, model training, validation, testing, overfitting, and model selection.
Data preprocessing: Practise handling missing values, categorical data, outliers, feature scaling, and imbalanced classes.
Model evaluation: Understand precision, recall, F1-score, confusion matrices, ROC-AUC, and the limits of accuracy as a metric.
Machine learning libraries: Be comfortable using at least one of TensorFlow, PyTorch, or Scikit-learn in a practical project.
Cloud deployment: Understand the basic process of packaging, deploying, hosting, and monitoring a model on a cloud platform.
LLMs and AI agents: Prepare to explain model architecture at a high level, fine-tuning concepts, agent workflows, and how you evaluated an AI-powered application.
Software engineering: Review data structures, algorithms, testing, design patterns, Git workflows, and CI/CD fundamentals.
Communication: Practise explaining a technical project, its trade-offs, its performance, and its business value in clear language.
These preparation suggestions are based on the supplied qualifications; they are not a separate list of additional official requirements.
The supplied description lists a bachelor’s or master’s degree in Computer Science, Computer/Software Engineering, or a related field, or an equivalent combination of education.
The listed skills include data preprocessing, handling imbalanced datasets, model evaluation, bias reduction, cloud deployment, and production model monitoring.
October 20, 2026

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