Intern (Data Science)

**Join Our Journey at VuNet**:
At VuNet, we're at the forefront of developing an innovative Business Observability platform. Our approach integrates big data and machine learning to revolutionize how customer journeys are monitored, and user experiences enhanced. Our cutting-edge solutions are transforming digital payment experiences for major financial institutions, fostering financial inclusion nationwide. In our dynamic environment, we encourage our teams to tackle challenging customer and business issues. We value creativity and efficiency, rapidly transforming brilliant ideas into exceptional products that our customers adore. Our approach is grounded in teamwork, with cross-functional groups delving into details and engaging in constructive debates, all united by our mission to establish VuNet as a leader in the tech product industry.

**About the role**:
We seek a passionate and detail-oriented Data Science Intern to join our team and support various analytical and data-driven initiatives. This internship is ideal for students or early-career professionals enthusiastic about data analysis, machine learning, and real-world problem-solving. You will have the opportunity to work on impactful projects involving data preprocessing, exploratory data analysis (EDA), model development, and dashboarding — gaining valuable experience in the data science lifecycle while working alongside experienced data scientists and engineers.

**What We’re Looking For**:

- Assist in cleaning, preprocessing, and analyzing large datasets to uncover actionable insights.
- Conduct exploratory data analysis (EDA) using visual and statistical techniques.
- Develop and validate supervised/unsupervised learning models (e.g., classification, regression, clustering) for business use cases.
- Support performance evaluation and tuning of machine learning models using appropriate metrics.
- Contribute to feature engineering and model improvement efforts.
- Help create dashboards and visual reports using tools like Grafana.
- Document code, methodology, and findings for internal sharing and future reference.
- Collaborate closely with the AI/ML team to understand requirements and deliver analytical solutions.

**Must-Have**:

- Foundational understanding of data science and machine learning concepts, including supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering), model evaluation metrics, and basic data preprocessing.
- Proficiency in Python, Data Structures, and Algorithms, with working knowledge of pandas, numpy, matplotlib/seaborn, and scikit-learn. Familiarity with AI libraries/frameworks such as TensorFlow, Keras, PyTorch (optional but advantageous).
- Comfort with Jupyter Notebooks and version control using Git.
- Familiarity with SQL for data querying and manipulation.
- Strong analytical thinking: ability to break down problems, analyze trends, and interpret patterns in data.
- Curiosity and willingness to learn new tools, techniques, and domain-specific nuances.

**Good-To-Have**:

- Exposure to unsupervised learning techniques such as clustering or dimensionality reduction.
- Prior experience working on data science or analytics projects, academic or personal (Kaggle projects count too).
- Awareness of current trends in Generative AI, LLMs, or large-scale ML (not a requirement, but a bonus).
- Familiarity with cloud platforms like AWS or GCP is a plus.

**Qualification**:

- B. S/B. E/B. Tech/ MCA/M. Tech (with Data Science)
- Good problem-solving attitude and eagerness to learn.
- Ability to work collaboratively in a team environment.

**Culture**:
VuNet believes in delivering WoW through service.

VuNet supports career development programs to expand our People skills and enhance expertise with various training programs.

VuNet advocates open culture by having transparency, inclusiveness, adaptability, and collaboration with an environment that fosters employee satisfaction, motivation, and trust, and having open communication, collaboration, and innovation within the teams.