Data Science Intern

Round 1: Online Aptitude & Coding Test → Round 2: Technical Interview (Python, Statistics, ML concepts) → Round 3: HR Interview (fitment, communication, internship expectations)

Mico promotes a learning‑first culture where interns are treated as contributors rather than observers. The company offers flexible working hours, mentorship from senior data scientists, and encourages knowledge sharing through regular tech talks. Employees report a balanced work‑life environment with opportunities for rapid skill growth.

Mico's interview process typically starts with a timed online test that evaluates aptitude, Python coding, and basic statistics. The technical interview dives deeper into data‑science concepts, asking candidates to walk through EDA, model selection, and evaluation metrics. The HR round focuses on cultural fit, communication skills, and internship expectations. Preparation should include hands‑on projects, clear articulation of past work, and familiarity with Mico's product domains.

Currently pursuing or recently completed a Bachelor’s/Master’s degree in Data Science, Statistics, Mathematics, Computer Science or a related field. Minimum aggregate of 60% (or CGPA 6.0/10). Eligible batches: 2027, 2028, 2029. Maximum of 1 active backlog allowed as per company policy. Must have basic to intermediate proficiency in Python and a strong analytical mindset.

Mico is a fast‑growing technology solutions provider headquartered in Bengaluru, Karnataka. Founded in the early 2010s, the company has built a reputation for delivering data‑driven products to clients across e‑commerce, fintech, and healthcare domains. With a culture that encourages experimentation and rapid prototyping, Mico invests heavily in building a strong analytics backbone, making data science a core pillar of its business strategy. The firm’s mission is to turn raw data into actionable insights that drive measurable outcomes for its customers, and it continuously expands its talent pool to stay ahead of the curve. As a Data Science Intern at Mico, you will be immersed in real‑world projects that span the entire data lifecycle—from data acquisition to model deployment. Working under senior data scientists, you will get hands‑on exposure to large, messy datasets and learn how to translate business problems into analytical solutions. This internship is designed for students from the 2027, 2028 and 2029 batches who have a solid foundation in Python, statistics, and machine learning, and who are eager to apply classroom knowledge to practical challenges. Key responsibilities include: 1. Collecting, cleaning, and preprocessing raw datasets using Python libraries such as Pandas and NumPy. 2. Conducting Exploratory Data Analysis (EDA) to uncover trends, patterns, and anomalies. 3. Applying statistical techniques like hypothesis testing, correlation analysis, and regression modeling. 4. Building and evaluating baseline machine‑learning models (e.g., linear regression, logistic regression, decision trees). 5. Performing feature engineering, handling missing values, outliers, and categorical variables. 6. Assessing model performance using appropriate metrics (RMSE, accuracy, F1‑score, etc.). 7. Documenting analysis, visualising results with Matplotlib/Seaborn, and presenting insights to cross‑functional teams. 8. Collaborating in agile sprints, contributing to code reviews, and maintaining reproducible notebooks in Jupyter or Google Colab. 9. Staying updated with the latest ML research and suggesting improvements to existing pipelines. 10. Assisting in the deployment of models to production environments under guidance. The technical stack revolves around Python, Pandas, NumPy, Scikit‑learn, and basic SQL for data extraction. Familiarity with data visualisation tools and version control (Git) is advantageous. Successful interns often transition to full‑time Analyst or Junior Data Scientist roles, gaining mentorship, exposure to client projects, and a clear growth path within Mico’s analytics division. Joining Mico means working in a collaborative, merit‑based environment where curiosity is rewarded, and you will leave with a portfolio of real‑world data science projects that boost your employability.