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REQUIREMENT_ID_1389 β€’ 3-DAY_ACTIVE_POLICY

Data Science Intern

Company
Company MICO
Type
Opportunity Type Internship
Salary
Stipend / Salary Rs 15,000/month
Location
Location Bengaluru, Karnataka, India
Posted Date
Posted Date Today
Python Pandas NumPy Scikit-learn Statistics Probability Linear Regression Logistic Regression Exploratory Data Analysis Data Cleaning Feature Engineering Machine Learning Fundamentals Model Evaluation SQL Matplotlib Seaborn Jupyter Notebook
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Aptitude Practice Questions Open Resource β†—
Helps you sharpen quantitative and logical reasoning skills essential for the online assessment stage.
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Company Interview Preparation Guide Open Resource β†—
Provides a collection of common interview questions and answer strategies useful for Mico's technical round.
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Mico Data Scientist Interview Guide Open Resource β†—
Focused preparation material covering topics frequently asked in Mico's data science interviews.
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Algorithm Practice Problems Open Resource β†—
Offers a wide range of coding problems to improve problem‑solving speed for the coding test.

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.

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

MICO β€” Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC πŸ“–
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource β†—
COMPANY GUIDE 🎯
MICO Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for MICO and off-campus tech roles.

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource β†—
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Python, Pandas, NumPy, Scikit-learn, Statistics, Probability, Linear Regression, Logistic Regression, Exploratory Data Analysis, Data Cleaning, Feature Engineering, Machine Learning Fundamentals, Model Evaluation, SQL, Matplotlib, Seaborn, Jupyter Notebook) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
MICO Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the difference between WHERE and HAVING clauses in SQL? Answer β–Ό
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer β–Ό
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer β–Ό
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer β–Ό
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer β–Ό
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Why do you want to join MICO as a Data Science Intern?
Preparation Tip: Highlight MICO's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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