Data Science Internship

The Zenotalent interview process typically starts with a timed online aptitude test, followed by a technical interview that probes your Python coding, data manipulation, and basic machine learning knowledge. The final HR round focuses on cultural fit and communication. Questions are moderate in difficulty; thorough preparation of core concepts and clear articulation of past projects help candidates succeed.

Zenotalent promotes a collaborative, remote‑first environment where learning is continuous and ideas are shared openly. Employees enjoy a healthy work‑life balance with flexible hours, regular knowledge‑sharing sessions, and clear pathways for career growth.

Open to Undergraduate and Postgraduate students from Engineering, Arts, Commerce, Science & other streams. Minimum aggregate of 60% (or CGPA 6.0/10) in the most recent degree. Candidates graduating in 2024, 2025, or 2026 are eligible. No active backlogs allowed at the time of joining. Must have a basic understanding of data science concepts and be willing to learn.

Round 1: Online aptitude & logical reasoning test → Round 2: Technical interview focusing on Python, data manipulation, and basic ML concepts → Round 3: HR interview assessing cultural fit and communication skills

Zenotalent is a fast‑growing technology‑driven startup that focuses on building intelligent solutions for businesses across sectors. Founded by a team of engineers and data enthusiasts, the company has quickly established a culture of continuous learning, collaboration, and innovation. With a flat hierarchy and open‑door policy, every team member gets the chance to contribute ideas, experiment with new tools, and see the impact of their work in real time. The organization values fresh perspectives, especially from young talent, and invests heavily in upskilling through internal workshops, mentorship programs, and access to industry‑leading resources. The Data Science Internship at Zenotalent is designed for students who are eager to apply classroom knowledge to real‑world problems. Interns will work closely with senior data scientists and product engineers on end‑to‑end data pipelines, from gathering raw data to delivering actionable insights. This role offers a hands‑on experience in a supportive environment where curiosity is encouraged and mistakes are treated as learning opportunities. Over a six‑month period, interns will build a solid foundation in machine learning, data preprocessing, and exploratory analysis while contributing to projects that directly influence product decisions. Key Responsibilities: 1. Assist in collecting, cleaning, and preprocessing structured and unstructured data sets. 2. Perform exploratory data analysis (EDA) to uncover patterns, trends, and anomalies. 3. Support the development, training, and evaluation of machine learning models using Python libraries. 4. Generate visualizations and dashboards to communicate findings to cross‑functional teams. 5. Document data pipelines, model assumptions, and experiment results. 6. Collaborate with senior data scientists on feature engineering and model optimization. 7. Prepare concise reports and presentations for stakeholders. 8. Participate in code reviews and adopt best practices for version control (Git). 9. Stay updated with the latest research and tools in data science and suggest improvements. 10. Contribute to brainstorming sessions for new data‑driven product ideas. Tech Stack: Python, Pandas, NumPy, Scikit‑learn, Matplotlib/Seaborn, SQL, Jupyter Notebook, Git, Docker (basic). The internship provides exposure to cloud platforms like AWS or GCP for data storage and model deployment. Growth Path: Successful interns may be offered a full‑time Junior Data Scientist role, with clear progression to Data Analyst, Data Scientist, and eventually Lead Data Scientist positions as they gain experience and demonstrate impact. Why Join Zenotalent? The company offers a vibrant, remote‑first work culture that balances autonomy with mentorship. Interns receive a competitive stipend, flexible working hours, and the chance to work on projects that matter. The experience gained here equips candidates with a portfolio of real‑world data science work, making them highly marketable for future roles.