As a Data Science Intern at UKG, you will work on real-world data science and data engineering problems, gaining hands-on experience in data analysis, data quality, machine learning concepts, and AI-assisted development. You will collaborate with cross-functional teams to understand business problems and develop data-driven solutions.
UKG is a leading provider of cloud-based human capital management, payroll, and workforce management solutions. With a strong presence in India, UKG offers a dynamic work environment that fosters innovation, collaboration, and growth. As a Data Science Intern, you will be part of a team that leverages data science and AI to drive business decisions and improve customer experiences.
Why required: Python is a fundamental skill for data science, and UKG uses it extensively for data analysis, machine learning, and AI development.
How recruiters evaluate: The recruiter will evaluate your proficiency in Python, including your understanding of libraries such as Pandas, NumPy, and Scikit-learn.
Why required: Statistics is essential for data analysis, machine learning, and AI development. UKG requires a strong understanding of statistical concepts to drive business decisions.
How recruiters evaluate: The recruiter will assess your knowledge of statistical concepts, including probability, regression, and hypothesis testing.
Why required: Data analysis is a critical skill for data science, and UKG requires a strong understanding of data analysis techniques to drive business decisions.
How recruiters evaluate: The recruiter will evaluate your ability to analyze and interpret data, including data visualization and communication skills.
Why required: Machine learning is a key skill for data science, and UKG uses it extensively for predictive modeling and AI development.
How recruiters evaluate: The recruiter will assess your knowledge of machine learning concepts, including supervised and unsupervised learning, neural networks, and deep learning.
Why required: Pandas is a popular library for data manipulation and analysis in Python. UKG uses Pandas extensively for data science tasks.
How recruiters evaluate: The recruiter will evaluate your proficiency in Pandas, including data manipulation, filtering, and grouping.
Why required: NumPy is a fundamental library for numerical computing in Python. UKG uses NumPy extensively for data science tasks.
How recruiters evaluate: The recruiter will assess your knowledge of NumPy, including array operations, indexing, and data manipulation.
Why required: Scikit-learn is a popular library for machine learning in Python. UKG uses Scikit-learn extensively for predictive modeling and AI development.
How recruiters evaluate: The recruiter will evaluate your proficiency in Scikit-learn, including supervised and unsupervised learning, model selection, and hyperparameter tuning.
Why required: SQL is a fundamental skill for data science, and UKG uses it extensively for data analysis and data engineering tasks.
How recruiters evaluate: The recruiter will assess your knowledge of SQL, including query writing, data modeling, and database design.
Why required: Relational databases are a critical component of data science, and UKG uses them extensively for data storage and retrieval.
How recruiters evaluate: The recruiter will evaluate your understanding of relational databases, including data modeling, database design, and query optimization.
Freshers with a strong foundation in data science, computer science, statistics, mathematics, engineering, or a related field can apply.
Experienced professionals with a background in data science, machine learning, or AI can also apply.
Graduates with a degree in data science, computer science, statistics, mathematics, engineering, or a related field can apply.
B.Tech graduates with a strong foundation in data science, computer science, statistics, mathematics, engineering, or a related field can apply.
MCA graduates with a strong foundation in data science, computer science, statistics, mathematics, engineering, or a related field can apply.
Diploma holders with a strong foundation in data science, computer science, statistics, mathematics, engineering, or a related field can apply.
The average salary for a data science intern in India is around ₹25,000 - ₹40,000 per month.
Freshers can expect a salary range of ₹20,000 - ₹30,000 per month.
Experienced professionals can expect a salary range of ₹40,000 - ₹60,000 per month.
Salaries can vary based on location, company, and experience. This role offers opportunities for growth and development in data science and AI.
A data scientist role involves working on complex data science problems, developing predictive models, and driving business decisions.
A machine learning engineer role involves developing and deploying machine learning models, working on AI development, and collaborating with cross-functional teams.
A data engineer role involves designing and implementing data pipelines, working on data quality and data governance, and collaborating with data scientists and analysts.
Develops strong technical skills in data science and Python programming.
Develops strong technical skills in machine learning and Scikit-learn.
Develops strong technical skills in data analysis and visualization.
To succeed in this role, focus on developing strong technical skills in data science, machine learning, and AI. Stay up-to-date with new developments in the field, and practice problem-solving skills. Communicate complex technical information effectively, and collaborate with cross-functional teams.
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Posting Details
Posted: July 24, 2026
Full-Time
On-site
LocationsShowing 1 location
Bengaluru-Karnataka,India
Job Details
Description
We are looking for a motivated and analytical Data Science Intern to join our team. The intern will work on real-world data science and data engineering problems, gaining hands-on experience in data analysis, data quality, machine learning concepts, and AI-assisted development.
The ideal candidate should have a strong foundation in Python, statistics, data analysis, and machine learning, along with a curiosity to understand business problems and translate them into data-driven solutions.
Analyze and explore large datasets to identify patterns, trends, anomalies, and data quality issues.
Perform data preprocessing, cleaning, transformation, and feature engineering.
Develop and evaluate statistical and machine learning models under the guidance of senior team members.
Support the development of data quality and data scoring solutions.
Work with structured and semi-structured data from multiple sources.
Create scripts and utilities for data analysis and validation.
Assist in defining data benchmarks, metrics, and validation criteria.
Participate in experimentation and evaluation of AI/ML approaches.
Collaborate with Data Scientists, Software Engineers, and Product teams to understand requirements and solve real-world problems.
Document analysis, findings, methodologies, and results clearly.
Leverage modern AI-assisted development tools responsibly to improve productivity and accelerate experimentation.
Pursuing or recently completed a degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
programming knowledge in Python.
Good understanding of:
Statistics and probability
Data structures and algorithms
Data analysis and visualization
Basic machine learning concepts
Experience with libraries such as Pandas, NumPy, and Scikit-learn.
Basic understanding of SQL and relational databases.
Strong analytical and problem-solving skills.
Ability to learn new technologies and concepts quickly.
Exposure to machine learning or deep learning projects.
Experience with data visualization tools or libraries such as Matplotlib, Seaborn, or Plotly.
Familiarity with cloud platforms such as AWS, Azure, or GCP.
Knowledge of data quality, data profiling, or data governance concepts.
Experience working with APIs or large datasets.
Exposure to Generative AI or Large Language Models.
Experience using AI coding assistants such as GitHub Copilot or similar tools.
Participation in academic, personal, or open-source data science projects.
Strong analytical thinking and the ability to break down complex problems.
Curiosity and willingness to learn beyond academic concepts.
Ability to ask the right questions and understand the problem before jumping to a solution.
Ownership and accountability for assigned work.
Ability to work collaboratively with cross-functional teams.
Good communication skills and the ability to explain technical concepts clearly.
A practical mindset and willingness to experiment, learn from failures, and iterate.
Bachelor’s or Master’s degree in:
