Rapido – Data Science Intern (6-Month Internship) | Apply Now

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Job Overview




    Category
    Details




    Job Role
    Data Science Intern




    Company
    Rapido




    Location
    Not Mentioned (May be Remote/Hybrid Based on Team)




    Duration
    6 Months Internship




    Experience
    Students / Recent Graduates / Freshers




    Education
    Data Science, Statistics, Computer Science, or related fields




    Skills Required
    Python, SQL, Statistics, Data Analysis, ML Basics

About Company

Rapido is India's largest bike taxi platform, offering affordable commuting solutions across multiple cities. The company uses data-driven processes to optimize fleet operations, enhance customer experience, and improve rider-partner engagement. Rapido’s Data Science team focuses on solving real-world optimization, prediction, and decision-making challenges using advanced analytics and machine learning. The company values innovation, collaboration, and a strong learning mindset.

Detailed Responsibilities

As a Data Science Intern at Rapido, you will:

  • Work on data cleaning, preprocessing, analysis, and visualization tasks.

  • Build and validate basic machine learning models under the guidance of senior data scientists.

  • Analyze large datasets to find patterns, insights, and business opportunities.

  • Collaborate closely with cross-functional departments including Product, Engineering, and Operations.

  • Contribute to real-world data problems that directly improve Rapido’s product ecosystem and decision-making workflows.

  • Gain industry-level experience in analytics, modeling, storytelling with data, and scalable data processes.

Eligibility Criteria

  • Students or recent graduates from Data Science, Statistics, CS, or related fields.

  • Strong fundamentals in Python, SQL, statistics, and data analysis.

  • Good understanding of basic ML concepts such as regression, classification, and evaluation metrics.

  • Curiosity, creativity, and willingness to learn continuously.

  • Ability to work independently as well as collaborate with larger teams.

Skills Required / Preferred Skills

  • Practical experience with Python libraries like Pandas, NumPy, Matplotlib, or Seaborn.

  • Understanding of SQL queries and relational databases.

  • Basic machine learning knowledge.

  • strong analytical thinking and problem-solving skills.

  • Effective communication and ability to translate data into insights.

Selection Process




    Stage
    Description




    Application Screening
    Evaluation of resume, skills, and academic background




    Technical Task / Assessment
    Basic data analysis or ML assignment to test skills




    Technical Interview
    Discussion on Python, SQL, statistics, and problem-solving ability




    Final Round
    Interaction with team for culture fit and learning potential

Salary & Benefits

Data Science Internships at product-based companies typically offer competitive stipends based on skill level and internal budget. While the exact stipend is not disclosed, similar internships usually range around entry-level industry standards and may include learning resources, mentorship programs, hands-on project exposure, collaborative work culture, and chances of pre-placement or full-time hiring based on performance. Interns gain valuable real-world experience working with large-scale datasets and modern ML workflows, improving future employability.

How to Apply (Step-by-Step)

  1. Visit the official application form.

  2. Fill in your personal and academic details.

  3. Upload your updated resume/CV.

  4. Submit your application.

  5. Check your email regularly for updates from the hiring team.

👉 Apply here

Resume Tips

  • Keep your resume clean and highlight technical skills.

  • Add academic or personal projects involving data analysis or ML.

  • Clearly list your skills in Python, SQL, and ML libraries.

  • Use a professional email and updated contact details.

Assessment / Interview Tips

  • Practice Python pandas, SQL joins, and statistics fundamentals.

  • Revise ML basics and common evaluation metrics.

  • Be ready to explain your previous projects clearly.

  • Show eagerness to learn and work with real-world data.

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