DeltaX is hiring Business Analyst 🚀 ( Intern )

Qualification : Any Graduate
Experience : Freshers
Location : Pune

Apply link : Check out this job at DeltaX: https://www.linkedin.com/jobs/view/4235059592

Google hiring Software Engineer

Apply link: https://careers.google.com/jobs/results/135150532506329798-software-engineer-iii

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Google hiring Data Scientist

Apply link: https://careers.google.com/jobs/results/87405889337598662-data-scientist/

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PayPal is hiring Data Analyst 🚀

Experience : 2 Years
Location : Bangalore

Apply link : https://paypal.eightfold.ai/careers/job?domain=paypal.com&pid=274907105391&query=R0125910&domain=paypal.com&sort_by=relevance&job_index=0

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Adobe hiring Machine Learning Engineer

https://adobe.wd5.myworkdayjobs.com/en-US/external_experienced/job/Machine-Learning-Engineer-5_R155218

Paypal hiring Data Scientist 1

Apply link: https://paypal.eightfold.ai/careers/job?domain=paypal.com&pid=274907105521&query=R0126369&domain=paypal.com&sort_by=relevance&job_index=0

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Optum hiring Data Scientist

Apply link: https://careers.unitedhealthgroup.com/job/22011510/data-scientist-hyderabad-in/?src=JB-22455&utm_source=linkedin.com&utm_medium=job_posting

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Capgemini hiring Data Scientist

Apply link: https://careers.capgemini.com/job/Kolkata-Data-Scientist-C/1208045701/?feedId=388933&utm_source=LinkedInJobPostings

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Everyone thinks being a great data analyst is about advanced algorithms and complex dashboards.

But real data excellence comes from methodical habits that build trust and deliver real insights.

Here are 20 signs of a truly effective analyst 👇

✅ They document every step of their analysis
➝ Clear notes make their work reproducible and trustworthy.

✅ They check data quality before the analysis begins
➝ Garbage in = garbage out. Always validate first.

✅ They use version control religiously
➝ Every code change is tracked. Nothing gets lost.

✅ They explore data thoroughly before diving in
➝ Understanding context prevents costly misinterpretations.

✅ They create automated scripts for repetitive tasks
➝ Efficiency isn’t a luxury—it’s a necessity.

✅ They maintain a reusable code library
➝ Smart analysts never solve the same problem twice.

✅ They test assumptions with multiple validation methods
➝ One test isn’t enough; they triangulate confidence.

✅ They organize project files logically
➝ Their work is navigable by anyone, not just themselves.

✅ They seek peer reviews on critical work
➝ Fresh eyes catch blind spots.

✅ They continuously absorb industry knowledge
➝ Learning never stops. Trends change too quickly.

✅ They prioritize business-impacting projects
➝ Every analysis must drive real decisions.

✅ They explain complex findings simply
➝ Technical brilliance is useless without clarity.

✅ They write readable, well-commented code
➝ Their work is accessible to others, long after they're gone.

✅ They maintain robust backup systems
➝ Data loss is never an option.

✅ They learn from analytical mistakes
➝ Errors become stepping stones, not roadblocks.

✅ They build strong stakeholder relationships
➝ Data is only valuable when people use it.

✅ They break complex projects into manageable chunks
➝ Progress happens through disciplined, incremental work.

✅ They handle sensitive data with proper security
➝ Compliance isn’t optional—it’s foundational.

✅ They create visualizations that tell clear stories
➝ A chart without a narrative is just decoration.

✅ They actively seek evidence against their conclusions
➝ Confirmation bias is their biggest enemy.

The best analysts aren’t the ones with the most tools—they’re the ones with the most rigorous practices.

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Apple hiring Machine Learning Engineer

Apply link: https://jobs.apple.com/en-us/details/200604647/machine-learning-engineering-manager-photos

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Location: Bangalore

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Google hiring Data Scientist

Apply link: https://careers.google.com/jobs/results/79726425535324870-data-scientist/

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Statistics Interview Questions

Topics to Cover:

• Descriptive statistics
• Probability
• Hypothesis testing
• Regression analysis

Questions and Answers:

1 Q: What is the difference between descriptive and inferential statistics?

A: Descriptive statistics summarize the main features of a dataset (e.g., mean, median, mode), while inferential statistics use samples to make inferences about a larger population.

2 Q: Define p-value in hypothesis testing.

  A: The p-value is the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. A low p-value (< 0.05) indicates strong evidence against the null hypothesis.

3 Q: What is the central limit theorem?

  A: The central limit theorem states that the distribution of the sample mean approximates a normal distribution as the sample size becomes large, regardless of the population's distribution.

4 Q: Explain the concept of correlation.

  A: Correlation measures the strength and direction of the relationship between two variables. It ranges from -1 (perfect negative) to +1 (perfect positive), with 0 indicating no correlation.

5 Q: What is linear regression?

  A: Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.

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