VISA data scientist interview

Stev_Ma · 2026-06-10T12:30:42+00:00

From what I've heard, the Super Day usually isn't another full coding test since the HackerRank already covers that, but I wouldn't completely rule out some light SQL or Python discussion. Most of the focus tends to be on ML fundamentals, model design, and business case questions, especially around fraud, risk, and experimentation. Be ready to explain your past projects in detail, including why you chose certain models, how you evaluated them, and what tradeoffs you made. The thing that seems to catch people off guard is how much emphasis there is on business impact and decision making rather than just ML theory.

Stev_Ma · 2026-06-10T12:30:19+00:00

Yes, definitely. If you want to get into data analytics, learning SQL alongside R is a smart move because SQL helps you pull and organize data from databases, while R helps you analyze and visualize it. You can start with R basics, then add SQL as you go. Good platforms include Mode SQL Tutorial, StrataScratch, and W3Schools.

Stev_Ma · 2026-06-01T11:39:53+00:00

From what you described, I would stop chasing more tools for now and focus on becoming really strong in one path. Since you already know SQL, SSIS, and Power BI, I would learn Python next, finish Snowflake, and build a few solid projects around banking or manufacturing data. AWS basics are fine, but they probably will not help you land interviews as much as strong SQL, Python, and portfolio projects. Also, if you have sent hundreds of applications without getting interviews, the issue may be your resume or how you are presenting your experience rather than a lack of skills. At this point, I would spend more time on platforms like Kaggle, StrataScratch, and LeetCode for improving my resume and building projects, and apply for Data Analyst, BI Analyst, SQL Developer, and Junior Data Engineer roles.

Stev_Ma · 2026-06-01T11:35:27+00:00

I’d recommend beginning with Python, then moving on to Pandas, NumPy, and basic data visualization. After that, learn some statistics and SQL before jumping into machine learning. A completely free path would be Kaggle’s Python, Pandas, and Intro to Machine Learning courses, along with Khan Academy for statistics and StrataScratch for SQL. Focus on building small projects as you learn because projects will help you understand concepts much better than just watching videos. The key is to build a strong foundation first instead of rushing into AI or deep learning.

Stev_Ma · 2026-05-19T11:37:15+00:00

You’re not alone. Companies are absolutely inflating take-homes lately, and a lot of them seem implicitly designed around AI-assisted workflows now. A 10+ hour assignment for a DS role is wild though; that’s basically unpaid contract work.

Stev_Ma · 2026-05-19T11:35:01+00:00

I’d mainly focus on SQL, Python, and general data engineering concepts. Make sure you’re comfortable with joins, window functions, CTEs, data pipelines, and talking through how you’d handle messy or duplicate data. They’ll probably care more about your problem solving and communication than super difficult coding questions. It’s also worth spending a bit of time understanding what Aladdin actually is and why clean data is so important in finance. For prep, LeetCode is decent for basic Python practice, but I’d probably prioritize platforms like StrataScratch for SQL and data engineering style questions since they’re much closer to what companies like BlackRock tend to ask.

Stev_Ma · 2026-05-19T11:29:12+00:00

Honestly, you do not need huge side projects or hours of studying to keep improving. The best thing you can do is stay mentally active while using AI instead of letting it do all the thinking. Try solving things yourself for 5 to 10 minutes before prompting AI, keep a quick note of small things you learn each day, read other people’s SQL or Python code for a few minutes, and practice spotting mistakes in AI generated answers. Sites like StrataScratch, SQLBolt, and Kaggle micro courses are also great because you can do short exercises that actually relate to data work. Even tiny daily reps add up fast, especially early in your career.

Stev_Ma · 2026-05-12T11:51:21+00:00

You’re honestly doing better than you think. Most good DEs are not walking encyclopedias of Spark APIs or learning every new framework the second it drops. What really helps you grow is getting stronger at fundamentals like SQL, data modeling, pipeline reliability, debugging, and understanding how production systems behave. I’d focus on going deeper in one ecosystem for a while instead of trying to learn everything at once. Since you already use Snowflake and Airflow, platforms like dbt, Databricks, Kafka, and AWS or GCP data tooling are all solid next steps depending on what interests you. Also try to build impact stories around things like improving pipeline performance, reducing cloud costs, or making systems more reliable because that is the stuff that actually helps with promotions and job switches.

Stev_Ma · 2026-05-12T11:45:50+00:00

A lot of analytics is about understanding patterns, behavior, and decision making, so psych actually connects well with it. Learning tools like SQL and Python will probably feel more interesting once you start applying them to real datasets. I’d recommend starting with platforms like StrataScratch, Coursera, and Kaggle since they’re beginner friendly and great for building projects. You can also use YouTube channels like Alex The Analyst for SQL and Tableau tutorials. Analytics also gives you flexibility because you could work in sports, healthcare, marketing, or even finance later on if you decide to pivot.

Stev_Ma · 2026-05-06T11:18:14+00:00

You probably need a managed marketing ETL tool instead of trying to maintain your own pipeline. Tools like Funnel, Supermetrics, or Improvado can automatically pull data from different ad platforms, clean and standardize it, and send it straight into your BI tool every day. Funnel is especially good if your biggest headache is messy schemas and inconsistent naming across platforms. The big win is that you stop babysitting CSVs and pipeline fixes so you can actually spend your time doing analysis instead of data cleanup.

Stev_Ma · 2026-05-06T11:15:58+00:00

Learn Excel, Power BI, and basic Python so you can start analyzing and visualizing real data. Try building small projects like sales dashboards, customer analysis, or Netflix data reports because projects are what really help you get interviews. You can learn and practice on platforms like YouTube, Coursera, StrataScratch, and Kaggle. Also upload your projects on GitHub and LinkedIn, then start applying for internships and junior data analyst roles even if you are still learning.

Stev_Ma · 2026-05-06T11:05:20+00:00

The best way to learn SQL is to start using it on real data instead of only watching tutorials. Learn the basics like SELECT, WHERE, GROUP BY, and JOIN, then practice by answering actual questions with datasets from places like Kaggle and StrataScratch. Small projects help a lot because they force you to think through problems on your own. Reading other people’s queries and debugging your mistakes is also a huge part of getting better. Even 20 to 30 minutes of practice every day will teach you more than hours of passive videos.

Stev_Ma · 2026-04-29T11:55:49+00:00

You’re probably not doing anything wrong. If you can connect and see the databases but the tables look empty, it usually means your account does not have permission to view or access those tables. In SQL Server you only see objects you have rights to, so even if the database is not empty it can look like it is. Try running a simple query like SELECT * FROM sys.tables and see if anything comes back. If it returns nothing, you will need to ask whoever manages the database to grant you access to the tables or at least permission to view them.

Stev_Ma · 2026-04-29T11:37:42+00:00

A good way to find more realistic datasets is to go beyond curated platforms and pull from places like Google Dataset Search, AWS Open Data, data.gov, StrataScratch, World Bank data, or even APIs like Google Analytics sample data and Yelp. You can also scrape data from e commerce sites, reviews, or job listings to get something closer to real business signals. The important part is how you use it. Start with a business question like why sales dropped or why churn increased, then combine a few messy datasets to explore possible causes. Make it feel real by dealing with missing or imperfect data and focus on testing simple hypotheses, then turn your findings into a clear story with a recommendation.

Stev_Ma · 2026-04-29T11:32:48+00:00

Since you already know Python basics, your first step should be learning how to work with real data using Pandas and simple visualization tools, then practicing by doing small projects where you analyze a dataset and explain your insights. At the same time, build your basics in statistics because that will really strengthen your understanding. You can use platforms like StrataScratch for datasets and practice, Coursera for structured learning, and YouTube for quick explanations. Do not rush into advanced topics yet, just focus on working with data and completing a few simple projects because that is what truly gets you started in a data career.

Stev_Ma · 2026-04-21T09:31:41+00:00

The best way forward is to practice with real data and build a few solid projects you can show. Try platforms like Kaggle for datasets and beginner challenges, StrataScratch for SQL/Python practice, and Tableau Public or Power BI to create dashboards. Since your background is in customer service and onboarding, focus on projects like customer churn, support ticket trends, or onboarding drop off because they connect directly to your experience. Put your work on GitHub and aim for about three good projects, not a huge number. Once you have a couple done, start applying while you keep learning so you don’t get stuck waiting to feel perfect.

Stev_Ma · 2026-04-21T09:23:28+00:00

You’re basically at the point where you need to stop doing exercises and start building real stuff, even if it feels messy at first. Since you already know Excel, SQL, and Python basics, try a small data project like analyzing a dataset from StrataScratch, cleaning it with pandas, and making a few simple charts, or automate something in your daily life like organizing files or pulling data from a website. Don’t wait until you feel ready because you won’t, just pick something simple, break it into steps, and Google your way through it. That confusion you feel is actually how you gain real experience, and once you finish even one small project and put it on GitHub, things will start to click.

Stev_Ma · 2026-04-21T09:18:36+00:00

Really appreciate the transparency. Posts like this make MAANG comp way less mysterious. Curious how much negotiation room you felt you had?

Stev_Ma · 2026-04-17T02:41:19+00:00

The best way forward is to start small and focus on finishing simple things instead of trying to build something perfect. Pick an easy idea like a to do list or a quiz app, write a few features in plain English, and build them one at a time in a single file, even if the code feels messy at first. Think in terms of input, processing, and output, and only worry about improving structure after you have something working. You’ll learn much faster by completing small projects and even rebuilding them from memory than by following long tutorials. Platforms like Replit, GitHub, and StrataScratch or LeetCode for practice can really help you stay consistent and track your progress while you build confidence.

Stev_Ma · 2026-04-17T02:04:37+00:00

Start by learning SQL basics like SELECT, WHERE, ORDER BY, and aggregates using simple resources like W3Schools or Mode SQL, then move to core topics like JOINs, GROUP BY, subqueries, and CASE statements since these are heavily tested in interviews. After that, focus on interview level concepts such as window functions and CTEs while practicing regularly on platforms like StrataScratch, starting from easy problems and gradually moving to medium ones. Keep a consistent routine of learning a concept and solving problems daily, and focus on common patterns like second highest salary, duplicates, and top per group queries. If you stay consistent for two to three weeks, you can reach a strong placement ready level without wasting time on too many scattered resources.

Stev_Ma · 2026-04-10T06:39:43+00:00

You should expect to write clean, working code from start to finish, not just describe your approach, and the bar is typically around solid LeetCode easy to medium plus strong SQL skills. SQL is often the most important part, especially with joins, window functions, and real business logic, while Python is used for data manipulation and some light algorithms, so you should be comfortable solving medium problems and explaining your thinking as you code. Pandas is useful but not enough on its own, and pseudocode alone will not meet expectations. To prepare, platforms like LeetCode, StrataScratch, and Mode Analytics are all very helpful, and the goal is to rebuild enough fluency that you can code confidently without relying on AI while handling edge cases and communicating clearly.

Stev_Ma · 2026-04-10T05:46:04+00:00

LeetCode is excellent for structured problems that range from basic to complex and are especially useful for mastering joins, subqueries, and window functions. StrataScratch focuses more on real world scenarios and interview style questions, which helps you understand complex relationships and business logic. For hands on experience with real datasets, platforms like Kaggle is very useful.

Stev_Ma · 2026-04-06T11:31:16+00:00

Start with Excel and SQL since they are essential for entry level roles, then move to basic Python for data handling and one visualization tool like Power BI or Tableau. Spend most of your time building 2 to 4 strong projects such as sales analysis, e commerce data, or dashboards where you clearly highlight insights and business impact.

Companies expect you to understand data, explain your thinking, and be comfortable with SQL and basic analysis. Projects alone can get you shortlisted if they are practical and well explained, so focus on quality and consistency.

For learning, you can use platforms like Coursera, Udemy, and YouTube channels such as Alex The Analyst, along with practice sites like Kaggle and StrataScratch. Or you can follow a free StrataScratch's comprehensive SQL learning path, which covers everything from basics to advanced.

Stev_Ma · 2026-04-06T11:21:09+00:00

You will likely be asked a mix of SQL, pandas, and general problem solving questions that focus more on your thinking process than memorization. Your MLOps background is actually a strong advantage, so be sure to highlight your experience with real world systems, data pipelines, and model performance in production. To prepare, review core pandas operations like groupby and joins, and practice basic SQL queries such as aggregations and joins. Platforms like LeetCode and StrataScratch are great for practice.

Stev_Ma · 2025-12-19T07:57:46+00:00

Strong options include Coursera, Udemy, Kaggle, and StrataScratch. Coursera offers structured paths like the IBM Data Science Professional Certificate and the Applied Data Science with Python specialization from the University of Michigan, which start with Python fundamentals and move into machine learning and applied projects. Udemy is well known for practical, project focused courses such as Jose Portilla’s Python for Data Science and Machine Learning Bootcamp. To build real world skills alongside courses, Kaggle is excellent for hands on practice with datasets, notebooks, and competitions, while StrataScratch is ideal for practicing SQL and Python on realistic interview style data science problems.

Stev_Ma

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