Experience: 0-1 year
Agar tum Data Science me career banana chahte ho aur ek top Indian startup me real-world experience lena chahte ho, toh yeh post tumhare liye hai. Swiggy — India ki sabse badi food delivery aur quick commerce companies me se ek — Data Science Intern ke liye applications maang rahi hai. Internship 6 mahine ki hai, remote ya Bangalore office dono options hain, aur jo log genuinely data aur analytics ke baare me passionate hain unke liye yeh ek genuinely exciting opportunity hai.
Apply karne ka link neeche diya hua hai — form fill karna bahut simple hai.
Swiggy Ke Baare Me
Swiggy ko introduction ki zaroorat nahi — yeh India ki leading food delivery aur instant delivery platform hai jo 2014 me Bangalore me shuru hui. Abhi Swiggy 500 se zyada cities me operate karti hai aur inke paas Instamart, Dineout aur Swiggy Genie jaise multiple products hain.
Data aur analytics Swiggy ke business ka core hain — delivery time predictions, demand forecasting, restaurant recommendations, surge pricing — yeh sab data science se hi power hote hain. Toh yahan internship karna matlab genuinely meaningful aur impactful data ka kaam milna.
Internship Ki Poori Detail
| Detail | Jankari |
|---|---|
| Role | Data Science Intern |
| Company | Swiggy |
| Duration | 6 Mahine |
| Work Mode | Remote ya Work From Office (Bangalore) |
| Eligibility | Bachelor’s ya Master’s degree pursuing students |
| Stipend | Form fill karne ke baad pata chalega |
Kaun Apply Kar Sakta Hai?
- Bachelor’s ya Master’s degree pursuing students — degree complete nahi honi chahiye, abhi padh rahe ho toh bhi apply kar sakte ho
- Data, analytics aur real-world problem solving me genuine interest hona chahiye
- Seekhne ki aadat, innovate karne ka mindset, aur impact create karne ki eagerness honi chahiye
Yeh internship specially un students ke liye hai jo abhi college me hain aur industry experience chahte hain — kisi specific branch ka mention nahi hai, toh Statistics, Mathematics, Computer Science, Economics ya koi bhi relevant background wale apply kar sakte hain.
Kaam Kya Karna Hoga?
Swiggy jaise product-driven company me Data Science Intern ka kaam genuinely exciting hota hai. Broadly yeh areas cover honge:
- Real-world data problems pe kaam karna — delivery optimization, customer behavior analysis, demand prediction jaise areas
- Data collect karna, clean karna aur analyze karna
- Machine learning models build karna ya existing models improve karna
- Data visualizations aur insights present karna business teams ke liye
- Senior data scientists ke saath collaborate karna aur live projects me contribute karna
- Analytics findings ko actionable recommendations me translate karna
Swiggy me intern hone ka matlab hai ki tum actually woh data touch karoge jisse lakho orders roz optimize hote hain — yeh kisi college assignment se bahut alag aur zyada valuable experience hai.
Yeh Internship Kyun Karni Chahiye?
Brand Value: Swiggy ka naam resume pe hona — especially Data Science me — interviews me seedha shortlist karvata hai.
Real Impact: Swiggy ke data problems real scale pe hain — crores of transactions, millions of users. Is scale pe kaam karna ek rare learning opportunity hai.
Flexible Work Mode: Remote ya Bangalore office — dono options hain. Agar Bangalore me ho toh office join kar sakte ho, bahar ho toh remote bhi chalega.
6 Mahine Ka Exposure: 6 mahine kaafi lamba time hai genuinely kuch seekhne ke liye — ek month ki internship se bahut zyada valuable.
Network: Swiggy me kaam karte hue jo connections banenge — data scientists, product managers, engineers — woh aage career me bahut kaam aayenge.
👉 Direct Apply Link
Click Here to Apply – Swiggy Data Science Internship 2026
Interview Ki Taiyari – Yeh Questions Zaroor Padho
Swiggy ka Data Science interview Python, SQL, Statistics aur machine learning basics pe focused hoga. Niche important questions diye hain.
Python & Data Analysis Questions
Q. What is the difference between a list, tuple, and set in Python?
A list is ordered and mutable — you can add, remove, or change elements. A tuple is ordered but immutable — once created, it cannot be changed, making it faster and memory-efficient. A set is unordered and contains only unique elements — it is useful when you want to eliminate duplicates or check membership quickly.
Q. How do you handle missing values in a dataset?
First, understand why the data is missing — is it missing completely at random, or is there a pattern. Then choose the right strategy. You can drop rows or columns if the missing data is minimal. You can fill with the mean or median for numerical columns, or the mode for categorical ones. For time series data, forward-fill or backward-fill works well. In some cases, missing values themselves can be a signal — so creating a separate binary column indicating missingness can also add value.
Q. What is the difference between map(), apply(), and applymap() in Pandas?
map() works on a single Series and is used for element-wise operations — usually to substitute values using a dictionary or function. apply() works on both Series and DataFrames and applies a function along an axis — row-wise or column-wise. applymap() works element-wise on an entire DataFrame. In newer versions of Pandas, applymap() has been replaced by map() for DataFrames.
Q. How would you merge two DataFrames in Pandas?
Use pd.merge(df1, df2, on=’common_column’, how=’inner’). The how parameter accepts ‘inner’, ‘left’, ‘right’, or ‘outer’ — similar to SQL JOIN types. For index-based merging, use df1.join(df2). For concatenating DataFrames vertically or horizontally, use pd.concat().
Q. What is the use of groupby() in Pandas?
groupby() splits the DataFrame into groups based on one or more columns and lets you apply aggregation functions like sum(), mean(), count(), or custom functions to each group. It is the Pandas equivalent of SQL’s GROUP BY. For example, grouping orders by city and calculating average delivery time per city is a classic Swiggy-style use case.
SQL Questions
Q. Write a query to find the top 3 cities with the highest number of orders.
SELECT city, COUNT(order_id) AS total_orders FROM orders GROUP BY city ORDER BY total_orders DESC LIMIT 3;
Q. What is the difference between RANK() and DENSE_RANK()?
Both assign ranks to rows based on an ordered value. RANK() skips numbers after a tie — if two rows share rank 2, the next rank assigned is 4. DENSE_RANK() does not skip — the next rank after a tie at 2 is still 3. DENSE_RANK() is more useful when you need continuous ranking without gaps.
Q. What is a window function and when would you use it?
Window functions perform calculations across a related set of rows without collapsing them into a single output like GROUP BY does. They are used for running totals, moving averages, ranking within groups, and comparing a row’s value to the previous or next row using LAG() and LEAD(). For example, calculating a 7-day rolling average of daily orders is a window function use case.
Q. How would you find customers who ordered more than once in the last 30 days?
SELECT customer_id, COUNT(order_id) AS order_count FROM orders WHERE order_date >= CURRENT_DATE – INTERVAL ’30 days’ GROUP BY customer_id HAVING COUNT(order_id) > 1;
Statistics & Machine Learning Questions
Q. What is the difference between supervised and unsupervised learning?
In supervised learning, the model is trained on labeled data — where the correct output is already known — and learns to predict outputs for new inputs. Examples include linear regression, logistic regression, and decision trees. In unsupervised learning, the model works with unlabeled data and finds hidden patterns or structures on its own. Examples include K-Means clustering and PCA.
Q. What is overfitting and how do you prevent it?
Overfitting happens when a model learns the training data too well — including its noise and outliers — and performs poorly on new, unseen data. It means the model has memorized rather than generalized. Prevention techniques include cross-validation, regularization (L1/L2), reducing model complexity, adding more training data, and using dropout in neural networks.
Q. What is the difference between precision and recall?
Precision is the percentage of positive predictions that are actually correct — it answers “of all the cases the model predicted as positive, how many were truly positive.” Recall is the percentage of actual positives that the model correctly identified — it answers “of all the actual positive cases, how many did the model catch.” In fraud detection, recall is more important — missing a fraud is worse than a false alarm. In spam filtering, precision might be more important — you do not want to mark legitimate emails as spam.
Q. What is A/B testing and how is it used at a company like Swiggy?
A/B testing is a controlled experiment where two versions of something — A and B — are shown to different user groups to determine which performs better. At Swiggy, A/B testing might be used to test whether a new recommendation algorithm increases order value, whether a new UI layout improves conversion, or whether a discount strategy increases repeat orders. The results are evaluated using statistical significance to ensure the difference is not due to chance.
Q. What is the difference between correlation and causation?
Correlation means two variables move together — when one increases, the other tends to increase or decrease. Causation means one variable directly causes the change in another. A classic example — ice cream sales and drowning incidents are correlated because both increase in summer, but ice cream does not cause drowning. In data science, always validate causal claims with controlled experiments rather than observational data alone.
Q. Explain the bias-variance tradeoff.
Bias is the error from wrong assumptions in the model — a high-bias model is too simple and underfits the data. Variance is the error from sensitivity to small fluctuations in the training data — a high-variance model is too complex and overfits. The tradeoff is finding the sweet spot where both bias and variance are low enough to give good generalization on unseen data. This is typically done through cross-validation and regularization.
Case Study & Swiggy-Specific Questions
Q. How would you predict the estimated delivery time for an order?
This is a regression problem. Features would include distance between restaurant and delivery address, time of day, day of week, weather conditions, restaurant preparation time (historical average), traffic data, and delivery partner availability. You would train a regression model — possibly gradient boosting or a neural network — on historical order data and evaluate using RMSE or MAE. Real-time features like current traffic would need to be fetched from an external API.
Q. How would you identify restaurants that are at risk of churning from the Swiggy platform?
This is a churn prediction problem — a binary classification task. Features could include declining order volume over recent weeks, increasing cancellation rates, poor ratings trend, low acceptance rate from the restaurant, and reduced menu updates. Train a classifier on historical data of restaurants that churned and those that did not. Flag restaurants with high predicted churn probability for proactive retention efforts.
Q. If Swiggy wants to increase repeat orders, what data would you analyze?
Look at order frequency per customer, time between orders, what types of cuisines or restaurants drive repeat behavior, effectiveness of discount and loyalty programs, and how delivery experience (time, accuracy, rating) correlates with repeat purchase. Segment customers by behavior — identify what differentiates high-frequency users from one-time buyers — and design targeted interventions for each segment.
HR Questions
Q. Tell me about yourself.
Mention your degree and year, the data tools and languages you work with — Python, SQL, any ML libraries — and one or two projects that show your analytical thinking. Mention why Swiggy specifically excites you as a platform — their data scale, product diversity, or any feature you find interesting. Keep it to 2-3 minutes.
Q. Why do you want to intern at Swiggy?
Say that Swiggy operates at a scale where data decisions directly impact millions of users daily — delivery time, pricing, recommendations — and that level of real-world impact is what excites you most. Mention that working alongside Swiggy’s data team for 6 months will give you the kind of hands-on experience that no classroom can replicate.
Q. Tell me about a data project you have worked on.
Structure it clearly — what was the problem, what data did you use, what approach did you take, what tools did you use, and what did you find. Quantify the outcome if possible — “the model achieved 87% accuracy” or “the analysis helped identify a 15% drop in a specific region” is much stronger than a vague description.
Apply Se Pehle Yeh Checklist Dekho
✅ Resume ready rakho — Python, SQL aur any ML projects clearly mention hone chahiye
✅ GitHub pe koi project upload hai toh link ready rakho — bahut strong impression padta hai
✅ Form carefully fill karo — Google Form hai, ek baar submit karne ke baad edit nahi hoga
✅ Remote ya Bangalore — apni preference form me clearly mention karo
Aakhri Baat
Swiggy me Data Science Internship karna matlab India ki most data-driven companies me se ek ke real problems pe kaam karna. 6 mahine ka experience, flexible work mode, aur ek brand jo instantly resume me weight add karta hai — yeh combination bahut rare hota hai.
Agar tum genuinely data ke baare me passionate ho toh yeh form abhi fill karo.
👉 Abhi Apply Karo – Swiggy Data Science Internship 2026
Yeh post apne un college dosto ke saath share karo jo Data Science ya Analytics me hain — Swiggy jaisi company me internship milna ek genuinely badi achievement hoti hai. Aur aisi aur job aur internship opportunities ke liye humara blog bookmark karke rakho.