Scaler Review: DSML Placements (88% Rate, ₹21 LPA Avg)
While going through multiple platforms and reviews, you'll find very different answers about scaler dsml placements. Some suggest that very few learners secure jobs after the program, while the audited placement report presents a very different picture.
When the numbers don't match, you would then end up thinking: what is each source actually measuring? And we’re here to answer that.
We understand the questions raised by viewers and anyone wanting a definite idea about the program. Hence, we'll walk through the audited DSML placement report, explain what each metric represents, and examine the context behind the numbers, i.e, from placement methodology and salary movement to role-wise outcomes and learner stories.
Let's Start With the Criticism
Every learner's experience with Scaler is different, and so are the opinions you'll find online. While many learners share positive outcomes, others raise questions about their placement journey. We believe those questions deserve to be acknowledged, especially when they're about something as important as career outcomes.
While reviewing public discussions around our DSML program, we found that most placement-related concerns centred around the following three claims. In the sections that follow, we'll examine each one against the independently audited placement report for the Class of 2024-25.
We encountered claims like:
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"Do not join this DSML course for placements, coz it's probably less than 5%... 95% placements which they claim is a 2-3 yr old KPMG audit report when there was lots of hiring in software engineering." - From Grapevine
-
"Waste of Money and Time. There are no placement opportunities in Scaler... I have applied for 30 jobs in the last 9 months. I got 0 calls for interview. They tell placement rate is 85%..." - From Quora
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"I hoped to get a decent package around 8–10 LPA but now I am not getting even a full-time job." - From Quora
And when you hear the said opinions, you would also come to think:
- Is the placement rate actually what Scaler reports?
- What do the placement numbers include?
- What kind of salary outcomes are learners seeing across different roles?
With the questions laid out, let's look at what the latest audited placement data shows. You can download Scaler's 3rd Placement Report and explore the numbers alongside the sections below.
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What the Audited Data Shows
For the Class of 2024-25, our Modern Data Science & Machine Learning (DSML) program included 3,989 learners, with an 88% placement rate among eligible learners. These scaler dsml placements outcomes were independently assessed by B2K Analytics, the same agency that audits IIM Ahmedabad's placement reports.
If you're wondering whether this percentage includes everyone who enrolled, the answer is no.
The 88% placement rate is calculated for learners who completed the program and met the placement eligibility criteria during the reporting period. We've chosen to report it this way because it accurately reflects the group that entered the placement process, rather than combining learners at different stages of their journey into a single percentage.
And we also won’t ask you to take these numbers at face value. The report includes the methodology used to verify them, including supporting documentation, learner verification calls and random sample validation conducted as part of the independent audit. That way, you can see not just the outcomes, but also how those outcomes were measured.
What About Salary Outcomes?
For learners in our Modern Data Science & Machine Learning (DSML) program, you’ll find the average CTC increasing from ₹9.5 LPA before joining the program to ₹21 LPA after placement in the report. That's an average salary increase of 149%, or roughly 2.5× the pre-program salary. Both figures describe the same outcome in different ways: 149% is the percentage increase, while 2.5x compares the final salary to the starting salary.
Also, please remember that average salaries also don't tell the complete story, which is why we've reported different salary bands alongside them. The top 25% of placed DSML learners secured a CTC of ₹42 LPA, to give you a clearer picture of outcomes across the cohort rather than relying on a single average.
The report also records the highest CTC of ₹2.8 crore. Just like for DSML, even for other courses, there are learners with the highest salary packages whom we have categorised as outliers separately.
Where DSML Learners Land (Including the Floor)
Salary also depends on the role you move into after completing the program. Not every learner starts in the same position, which is why we've included the role-wise salary distribution from the audited report instead of relying only on the overall average.
| Role | Average CTC |
|---|---|
| Data Science Leadership | ₹27 LPA |
| Senior Data Scientist | ₹25 LPA |
| Data Scientist | ₹20 LPA |
| Senior Data Analyst | ₹18 LPA |
| Data Analyst | ₹13 LPA |
If you're comparing your own career path with these numbers, it's important to compare like with like. A learner stepping into a Data Analyst role shouldn't expect the same compensation as someone joining as a Senior Data Scientist or moving into Data Science Leadership.
You'll also notice that Data Analyst roles average ₹13 LPA, which is materially lower than the overall DSML average of ₹21 LPA. We've included this because it reflects an important reality: entry-level analyst roles typically pay less than more experienced data science positions, and the overall average includes learners placed across a range of roles and seniority levels.
Looking at the role-wise breakdown alongside the overall average gives you a more complete picture of the outcomes. Rather than expecting every learner to follow the same path, you can see how compensation varies depending on the role and level at which they enter the industry.
The Roles With the Highest Growth
If you're still deciding which role to work towards, the audited report highlights an interesting trend. The highest salary growth wasn't recorded in Data Science; it was in Data Engineering.
Among learners in the audited cohort, Data Engineers recorded an average salary increase of 265%, followed by Data Analysts at 153% and Senior Data Scientists at 142%. These figures compare learners' salaries before and after the program, showing how compensation changed as they moved into these roles.
This doesn't mean one role is inherently better than another. It does show that if you're evaluating different career paths within the data ecosystem, Data Engineering has emerged as the fastest-growing route in terms of salary growth for this audited cohort. It's one more reason to explore the broader range of opportunities in data, rather than focusing only on Data Scientist roles.
Who Succeeds in DSML?
One of the assumptions we often come across is that you need years of experience to benefit from a data science program. The audited report suggests otherwise. Nearly 39% of learners in the DSML cohort had between 0 and 2 years of work experience, showing that early-career professionals made up a significant share of the placed cohort.
At the same time, career transitions aren't limited to one type of learner. One story featured in the report is Vivek Prakash, who moved from serving as an Ex-Submarine Captain in the Indian Navy to applying machine learning in the defence sector. As he puts it:
"I used the machine learning models taught at Scaler to explain how sonar data can be used via AI, and that was what got the attention of the defence heads."
If you'd like to understand how Vivek made that transition, from the Indian Navy to building AI-driven defence solutions, we've shared his complete journey in a separate feature story.
Scaler Placement Report and Statistics
Scaler learners achieved 2.5x salary growth with average post-Scaler CTC reaching ₹23L.
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Wondering who is behind the numbers? Here we have mentioned a few learner stories so that you can check out their experiences in detail.
1. Madhuri Kukreja's Journey
Madhuri joined Scaler during a phase of career growth and spent the program building confidence alongside technical skills. In her LinkedIn post, she talks about overcoming self-doubt, staying positive through the learning process, and emerging with greater confidence in her abilities. Today, she works as a Senior Product Analyst.
Read More At: Madhuri's LinkedIn Post
2. Sudhendu's Journey
Sudhendu entered Scaler without an academic background in coding and with years of believing programming simply wasn't his strength. Learning data science meant overcoming that fear from the ground up. He writes about the long hours spent understanding concepts with classmates, applying what he learned outside the classroom, and gradually becoming comfortable with coding. Looking back, he describes the experience as one that completely changed the way he viewed his own abilities.
Read More At: Sudhendu's LinkedIn Post
Turn Learning into Career Growth
3. Kuntal Datta's Journey
Kuntal joined Scaler to bridge the gap between learning data science concepts and applying them to real business problems. He highlights the Learner Profiling and Segmentation project as a turning point, in which techniques such as K-Means clustering, Hierarchical Clustering, and PCA became practical skills through hands-on work. By the end of the program, he had built the confidence to take on AI and data science roles backed by real project experience.
Read More At: Kuntal Datta’s LinkedIn Post
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FAQs
Q1. Does Scaler DSML actually get people placed?
According to Scaler's independently audited 2024-25 Placement Report, 88% of 3,989 eligible DSML learners secured placements. The report also records an average post-program CTC of ₹21L.
Q2. What is the average package after Scaler DSML?
The audited DSML cohort reported an average post-program CTC of ₹21L, up from ₹9.5L before the program. The top 25% averaged ₹42L, while Data Analyst roles averaged ₹13L.
Q3. Is Scaler DSML worth it for freshers?
Freshers are part of the DSML cohort, with 39% of learners having 0-2 years of experience. The report includes outcomes across experience levels, though entry-level roles typically have lower salary averages than experienced hires.
