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How to become a data scientist: A cheat sheet

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Data scientists are in great demand, taking the coveted No. 2 spot on Glassdoor's Best Jobs in America list for 2021 with 5,971 job openings, and the demand continues to grow. In 2012, the Harvard Business Review billed data scientists as the sexiest job of the 21st century."

Among data scientists, many different jobs can exist. "There are data scientists that focus very much on advanced analytics. Some data scientists only do natural language processing," said Dana Seidel, data scientist. "And the work encompasses many diverse skills, she said, including "project management skills, data skills, analysis skills, critical thinking skills."

SEE: Hiring kit: Data scientist (TechRepublic Premium)

The field is in such high demand because businesses need data analytics to stay competitive. "In the end, the main reason demand is still high is because if your competitors are relying on data-driven decision making and you aren't, they will surpass you and steal your market share.

Therefore companies have to adapt and employ data science tools and techniques or they will simply be forced out of business," said Christopher Zita in an article on Towards Data Science.

To help those interested in the field better understand how to break into a career in data science, we've created a guide with the most important details and resources.

SEE: All of TechRepublic's cheat sheets and smart person's guides

Executive summary

  • Why is there an increased demand for data scientists? Nearly every company now has the ability to collect data, and the amount of data is growing larger and larger. This has led to a higher demand for employees with specific skills who can effectively organize and analyze this data to glean business insights.

  • What are some of the data scientist job roles? Core data scientist, researcher and big data specialist are some of the top job titles in the data science field.

  • What skills are required to be a data scientist? The common skill set for a data scientist includes machine learning, Python, Hadoop SPARK and SQL, according to Glassdoor. 

  • Which industries have the hottest markets for data scientists? The cities with the fastest-growing tech salaries between 2019-2020 according to the DICE 2021 Tech Salary Report include Charlotte, North Carolina (+13.8%); Orlando, Florida, (+13.4%); New York, New York (+11.6%); Austin, Texas (+9.7%); and Philadelphia, Pennsylvania (+8.3%). Other top-ranking cities in this category were Detroit, Phoenix, Houston, Minneapolis and Baltimore. In addition to the traditional "tech hubs" this list includes a number of emerging cities. Some of the top-paying industries are aerospace product and parts manufacturing, $119,590; telecommunications, $102,180; federal executive branch (OEWS designation) $101,560; oil and gas extraction, $101,130; and software publishers, $96,510.

  • What is the average salary of a data scientist? The national average base salary for data scientists was $117,288 as of September 2021, according to Glassdoor. LinkedIn placed the national average base salary at $119,378 for September  2021. Salaries vary greatly depending on location; the positions with the highest salaries are in San Francisco, San Jose, Seattle and New York City.

  • What are typical interview questions for a career in data science? "In an interview, expect to answer technical questions about your ability to perform quantitative tests as well as create clear visualizations of large, complex data sets. Come ready to discuss past projects you've worked on and how you communicate data findings clearly and concisely in order to help solve business-related problems," Glassdoor suggested.

  • Where can I find resources for a career in data science? The Data Science Association, The Institute for Operations Research and the Management Sciences and the International Institute for Analytics are national and international organizations where you can seek out information about the profession as well as certification and training options. A number of online courses in programming languages such as Python, R and SQL are available from many providers.

Additional resources:

Why is there an increased demand for data scientists?

As every company becomes a tech company to some degree, the need for skilled professionals who can analyze that data and glean business insights increases.

"As the size of data at companies grow larger and larger, there is higher demand for employees with specific skills who can effectively organize and analyze this data," said Pablo Ruiz Junco, Glassdoor economic research fellow. "At the same time, the amount of people with these skills is still relatively low compared to the demand, which results in higher pay."

SEE: Python is eating the world: How one developer's side project became the hottest programming language on the planet (cover story PDF) (TechRepublic)

Technology advances and the massive volumes of online data available are affecting every sector, and have tremendous impacts on the economy, said Karen Panetta, IEEE fellow and dean of graduate engineering at Tufts University. This so-called "data avalanche" is not just about the sheer volume of data, but also the speed at which it changes and grows, and the diverse types of data available.

"Knowing how to use a spreadsheet and a traditional database will not suffice in the emerging Big Data revolution," Panetta said. "Analyses need to be done in real-time, where decisions can be critical. Being able to simply know how to use the software tools is only part of this challenge. Understanding the data across disciplines, being able to communicate its meaning, and using statistics will be the differentiating factors from a traditional 'number cruncher.'"

Additional resources:

What are some of the data scientist job roles?

Generally speaking, data scientists mine data and analyze it for specific company interests, and then work with marketing departments to capitalize on that knowledge. These workers must be familiar with data-gathering software, programming, and warehousing techniques.

Data scientist jobs fall into 10 categories, according to Towards Data Science.

Data scientist—A data scientist knows a bit of everything, and they can offer insights on the best solutions for a specific project. They are in charge of researching and developing new algorithms and approaches. In large companies, they oversee projects from start to finish.

Data analyst—Data analysts are responsible for visualizing, transforming and manipulating the data. They are often in charge of preparing the data for communication by making reports that show trends and insights.

Data engineer—Data engineers are responsible for designing, building and maintaining data pipelines. They make sure that the data is ready to be processed and analyzed. They need to keep the ecosystem and the pipeline optimized and efficient.

Data architect—A data architect is similar to a data engineer. They both need to ensure that the data is well-formatted and accessible. Data architects also design, create and maintain new database systems that match the requirements of a specific business model. 

Data storyteller—This is the newest job role in this list. Data storytelling is not just about visualizing the data and making reports and stats; rather, it is about finding the narrative that best describes the data and uses it to express it. The data storyteller helps people understand the data.

Machine learning scientist—A machine learning scientist researches new data manipulating approaches and designs new algorithms to be used. 

Machine learning engineer—Machine learning engineers need to be very familiar with the various machine learning algorithms like clustering, categorization and classification and are up-to-date with the latest research advances in the field. Machine learning engineers need to have strong statistics and programming skills in addition to some knowledge of the fundamentals of software engineering.

Business intelligence developer—Business Intelligence developers design and develop strategies that allow business users to find the information they need to make decisions quickly and efficiently. BI developers need to have at least a basic understanding of the fundamentals of business models.

Database administrator—A database administrator will be in charge of monitoring the database, making sure it functions properly, keeping track of the data flow, and creating backups and recoveries.

Technology specialized roles—As the data science field grows, more specific technologies will emerge. As the field develops, new specialized job roles will be created. These job roles apply to data scientists and analysis as well. 

Additional resources:

What skills are required to be a data scientist?

Here are the 12 marketable skills a data scientist might need, according to an Indeed report:

  1. Cloud computing
  2. Statistics and probability
  3. Advanced mathematics
  4. Machine learning
  5. Data visualization skills
  6. Query languages
  7. Database management
  8. Visualizations
  9. Python coding
  10. Microsoft Excel
  11. R programming
  12. Data wrangling

"If you're looking to enter the field of data science and build a solid foundation of experience that will stand out in the eyes of future employers, there are three core skills you need: Python, R and SQL," said Pablo Ruiz Junco, Glassdoor economic research fellow. "With these skills, you'll be eligible to apply to over 70% of all online job postings for data scientist roles. Plus, expanding your skills beyond these foundational languages can lead you to a higher salary and allow you to cast a wider net when applying."

Additional resources:

What is the average salary of a data scientist?

Average salary figures differ slightly for U.S. data scientists depending on which job site you look at. LinkedIn says the average base pay is $119,378 , and Glassdoor says the average base pay for the position is $117,288.

Data scientists in San Francisco are the highest paid, with a median base salary of $160,525, followed by San Jose, California ($107,226), Seattle ($143,300), and New York City ($151, 527), according to Indeed.

The Bureau of Labor Statistics said the median pay for a data scientist with a master's degree in 2020 was $126,830 per year.

As seen above with the salary differences between core data scientists, researchers, and big data specialists, the skills that individual data scientists bring to the table can have a large impact on pay. Job seekers should consider what role they are most interested in and make a cost-benefit analysis of which skills are worth spending time learning.

Additional resources:

Which data science job roles pay the highest salaries?

While analysts predicted that demand for data scientists would boom by 2020, that demand slowed down in 2020, thanks to the COVID-19 pandemic. Fortunately, that slowdown isn't expected to last.

According to a report from Indeed, the 15 highest-paying data jobs by national average salary in 2021 are:

  1. Machine learning engineer: $149,847
  2. Enterprise architect: $144,013
  3. Data architect: $133,840
  4. Big data engineer: $132,571
  5. Data modeler: $93,476
  6. Data scientist: $122,519
  7. Infrastructure engineer: $113,546
  8. Business intelligence developer: $100,494
  9. Statistician: $99,055
  10. Database administrator: $97,730
  11. Business intelligence analyst: $96,737
  12. Database developer: $89,250
  13. Data warehouse manager: $84,221
  14. Data analyst: $75,225
  15. Database manager: $65,558

Additional resources:

What are typical interview questions for a career in data science?

"To assess if a candidate can be successful as a data scientist, I'm looking for a few things: baseline knowledge of the fundamentals, a capacity to think creatively and scientifically about real-world problems, exceptional communication about highly technical topics, and constant curiosity," said Kevin Safford, senior director of engineering at Umbel.

A junior data scientist can expect questions like the following in a job interview, according to Forrester analyst Kjell Carlsson:

  • Walk me through the project that you are most proud of where you used data/data science/machine learning/advanced analytics. What was your role on the project, and what did you do in each step?
  • Tell me about a project where you used (insert language or skill here, e.g., Python, R).
  • Tell me about a time you had to work with someone who is not data-savvy on a data science project.
  • Pretend I am not a data scientist, explain (insert data science topic, e.g., cross validation, unsupervised learning, etc.) to me.
  • Tell me about a time you had to work with very messy data.
  • Tell me about your experience working in teams.
  • Tell me about a time when you had to become an expert on a new technique quickly.

The interviewee might be given a mini-case study based on a data science project the team has undertaken, with questions such as: What data would you need? What are the hypotheses you would like to test? What technique(s) would you use to evaluate them?

An interview may also include an exercise in which the interviewee is given a data set and a broad question, and asked to present their findings, Carlsson said.

For more senior positions, these questions may come up, according to Daniel Miller, vice president of recruiting at Empowered Staffing:

  • Have you built a data warehouse from scratch? If so, tell me about the process you created in order to successfully implement the data warehouse. (If they have not been part of it from scratch, you can ask if they have been part of a department that dealt with a company merger or acquisition of data and how they handled it.)
  • What types of customized dashboards have you built, and what information/analytics were being presented through your dashboard?
  • Tell me about the most complicated data project you have worked on, and what you were able to do in order to achieve success.
  • How are you with explaining and presenting data to executive and senior leadership?

Additional resources:

Where can I find resources for a career in data science?

The Data Science Association, The Institute for Operations Research and the Management Sciences and the International Institute for Analytics are national and international organizations where you can seek information about the profession as well as certification and training options.

Some educational institutions have created data science degree programs, including University of California Berkeley, Northwestern University, Carnegie Mellon University and Kennesaw State University. Some of these schools offer online courses.

You can find a number of online programming courses, such as those in Python, R and SQL, from many providers. Programs and seminars are also available through the IEEE Computer Society.

A number of certifications in data science are also available. These include the vendor-neutral Certified Analytics Professional (CAP), the Dell EMC Proven Professional certification program, the Microsoft Certified Solutions Expert (MCSE) and the SAS Data Science Certification.

Additional resources:

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Editor's note: This article was has been updated to reflect the latest information. 

Sours: https://www.techrepublic.com/article/cheat-sheet-how-to-become-a-data-scientist/

Merrell Men's Primer Canvas

#1 New York Times Bestseller

Over 2 million copies sold

In this generation-defining self-help guide, a superstar blogger cuts through the crap to show us how to stop trying to be "positive" all the time so that we can truly become better, happier people.

For decades, we’ve been told that positive thinking is the key to a happy, rich life. "F**k positivity," Mark Manson says. "Let’s be honest, shit is f**ked and we have to live with it." In his wildly popular Internet blog, Manson doesn’t sugarcoat or equivocate. He tells it like it is—a dose of raw, refreshing, honest truth that is sorely lacking today. The Subtle Art of Not Giving a F**k is his antidote to the coddling, let’s-all-feel-good mindset that has infected modern society and spoiled a generation, rewarding them with gold medals just for showing up.

Manson makes the argument, backed both by academic research and well-timed poop jokes, that improving our lives hinges not on our ability to turn lemons into lemonade, but on learning to stomach lemons better. Human beings are flawed and limited—"not everybody can be extraordinary, there are winners and losers in society, and some of it is not fair or your fault." Manson advises us to get to know our limitations and accept them. Once we embrace our fears, faults, and uncertainties, once we stop running and avoiding and start confronting painful truths, we can begin to find the courage, perseverance, honesty, responsibility, curiosity, and forgiveness we seek.

There are only so many things we can give a f**k about so we need to figure out which ones really matter, Manson makes clear. While money is nice, caring about what you do with your life is better, because true wealth is about experience. A much-needed grab-you-by-the-shoulders-and-look-you-in-the-eye moment of real-talk, filled with entertaining stories and profane, ruthless humor, The Subtle Art of Not Giving a F**k is a refreshing slap for a generation to help them lead contented, grounded lives.

Sours: https://enchipai.com/Zjc_w_NDZmMm/12173643904616/5-02c7-b4cdc
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Primer CEO on Natural Language Processing, Misinformation, and the Art of ML-Driven Defense

Analyzing Employee Reviews: Google vs Amazon vs Apple vs Microsoft

Whether it is for their ability to offer high salaries, extravagant perks, or their exciting mission statements, it is clear that top companies like Google and Microsoft have become talent magnets. To put it into perspective, Google alone receives more than two million job applications each year.

Working for a top tech company is many people’s dream, it was certainly mine for a long time, but shouldn’t we be asking ourselves “Is it really worth working for one of these companies?” Well, who better to help us answer this question than their own employees. In this article, I will walk you through my analysis of Employee Reviews for Google, Microsoft, Amazon and Apple and try to uncover some meaningful information that will hopefully illuminate us when deciding which company it’s worth working for.

I will start by describing how I cleaned and processed the data, and then talk about my analysis with the help of some visualizations. Let’s get started!!

Data Collection

The employee reviews data used for this analysis was downloaded from the Kaggle Datasets and it was sourced from Glassdoor — a website where current and former employees anonymously review companies and their management. The dataset contains over 67k employee reviews for Google, Amazon, Facebook, Apple and Microsoft.

The reviews are separated into the following categories:

  1. Index: index
  2. Company: Company name
  3. Location : This dataset is global, as such it may include the country’s name in parenthesis [i.e “Toronto, ON(Canada)”]. However, if the location is in the USA then it will only include the city and state[i.e “Los Angeles, CA” ]
  4. Date Posted: in the following format MM DD, YYYY
  5. Job-Title: This string will also include whether the reviewer is a ‘Current’ or ‘Former’ Employee at the time of the review
  6. Summary: Short summary of employee review
  7. Pros: Pros
  8. Cons: Cons
  9. Overall Rating: 1–5
  10. Work/Life Balance Rating: 1–5
  11. Culture and Values Rating: 1–5
  12. Career Opportunities Rating: 1–5
  13. Comp & Benefits Rating: 1–5
  14. Senior Management Rating: 1–5
  15. Helpful Review Count: A count of how many people found the review to be helpful
  16. Link to Review : This will provide you with a direct link to the page that contains the review. However it is likely that this link will be outdated

Here is what the data looks like in tabular form:

Data Cleaning

After doing some basic data exploration, I decided to do the following to get the data ready for my analysis:

  • Only include employee reviews for Google, Amazon, Microsoft and Apple. Although Facebook and Netflix had a good number of reviews, combined, they represented less than 4% of the dataset, so I decided to exclude them from this analysis for simplicity purposes.
  • The “Link” and “Advice to Management” columns were dropped since I didn’t think they would be as insightful as the other columns.
  • Rows with missing values in the “Date” column were dropped.
  • A new column named “Year” was created containing the different years when the reviews were made.
  • Rows with missing values in the following columns were dropped: “company”, ‘year’, “overall-ratings”, and “job-title”.
  • Rows with missing values in all columns were dropped.
  • Columns containing numeric values were converted to the appropriate data type.

Which company has the most reviews?

I began my analysis by visualizing the distribution of employee reviews for each of the 4 companies I selected.

Interpretation: We can clearly see that Amazon has the most employee reviews (over 25,000). This is great since it probably means that we’ll see good mix of opinions. Although Google has the least amount of employee reviews, it is still large enough to be significant and be able to compare it to the other companies.

Lets take a look at how these reviews are distributed throughout the years for each company.

Interpretation: As we can see, there is a decade worth of employees reviews available, but they only go up to 2018.

  • Microsoft: Most reviews are from the past 4–7 years
  • Google: Most reviews are from the past 4 years
  • Amazon: Most reviews are from the past 3–4 years
  • Apple: Most reviews are from the past 2–4 years

Based on these observations and considering how fast these companies are growing and changing every year, I decided to continue my analysis with employee reviews from the last 4 years available (2015 to 2018), since I believe they will be the most relevant.

Who is reviewing?

Now that we know how many reviews we are dealing with, let’s figure out who is writing them. This question can be answered in many different ways and my first approach was to figure out the job title of the reviewers, here is what the top 5 looks like:

Anonymous Employee 21910
Software Engineer 930
Specialist 648
Software Development Engineer 618
Warehouse Associate 585

Unfortunately, most of the job titles are labeled “Anonymous Employee”. Considering that often times companies have a slightly different titles for the same job, I decided to not dig any deeper. Instead, let’s take a look at how many of the reviewers are current and former employees

As we can see, most of the reviews come from current employees, but to get some more insight let’s see what this distribution looks like for each company:

Interpretation: Once again, we see that most of the reviews for each company are from current employees. These are a few thoughts that came to mind when trying to interpret the data: Is having a large number of reviews from current employees a good thing or does it mean more bias? Perhaps, having more reviews from former employees could give us the type of insights that we don’t often read about these companies. Let’s continue…

Which company has the highest overall rating?

Let’s take a look at how the average overall rating for each company has changed over the past few years (2015–2018)

Interpretation: We can see that the average overall rating for every company, except Apple, has not decreased since 2015. Google holds the highest average overall rating among the 4 and it has remained that way for the past couple of years. Lets talk about the trends for each company:

  • Google: Seems to have started decreasing slightly since 2016.
  • Microsoft: Increasing slowly since 2015
  • Apple: Seems to be decreasing slowly.
  • Amazon: Has increased dramatically from 2015 to 2017.

Which company offers better Work-Life Balance?

Let’s find out how good these companies are at allowing their employees to have a life outside of work:

Interpretation: Google has the highest work-life balance rating (over 4 stars) and Microsoft comes as a close second. Amazon seems to fall short when it comes to providing good work-life balance.

Which company has better Culture Values?

Let’s find out how the employees the rate core principals and ideals of their company:

Interpretation: Google has the highest rating for culture values and Apple places second (over 4 stars). Amazon has the lowest rating of the 4, but with just over 3.5 stars.

Which company has better Career Opportunities?

How good are they at helping you advance your career?

Interpretation: Google has the highest rating for career opportunities (over 4 stars). This shouldn’t comes as a surprise considering how big the company is and how many different types of technologies they are working with. Apple has the lowest rating at just below 3.5 stars.

Which company offers better Benefits?

Let’s find out how well these companies are doing in terms of benefits/perks for their employees.

Interpretation: Google has the highest rating for benefits/perks with over 4.5 stars. Apple and Microsoft also seem to offer good benefits, but Amazon falls a bit short.

Which company has better Senior Management?

Leadership is an important function of management, let’s see how Senior Management’s leadership is rated at these companies:

Interpretation: Google has the highest rating for Senior Management, but at just below 4 stars which is its lowest when compared to its other ratings. Amazon has the lowest rating for senior management.

What are pros of each company?

Let’s explore the pros comments using word-clouds:

Sours: https://towardsdatascience.com/analyzing-employee-reviews-google-vs-amazon-vs-apple-vs-microsoft-4dc3c036666b

Ai glassdoor primer

Robert E. Siegel

Robert Siegel is a lecturer in management and has led primary research and written cases on Google, Charles Schwab, Daimler, AB InBev, Box, Stripe, Target, AngelList, 23andMe, C3.ai, Majid Al Futtaim, Tableau, PayPal, SurveyMonkey, Medium, Autodesk, Minted, Zuora, Axel Springer, and Michelin, amongst others.

He is also a general partner at XSeed Capital and a venture partner at Piva Capital. He sits on the board of directors of Luum and Avochato. He led investments in Sparta Science, Zooz (acquired by PayU of Naspers - NPN), Hive, Lex Machina (acquired by LexisNexis of the RELX Group - RELX), CirroSecure (acquired by Palo Alto Networks - PANW), Nova Credit, The League, Teapot (acquired by Stripe), Brewbird, Pixlee and SIPX (acquired by ProQuest).

Robert is a member of the supervisory board of TTTech Auto AG, and is chairman of the strategic advisory board for TTTech Computertechnik AG in Vienna, Austria. He is a member of the industry advisory boards for HERE Technologies and Tulco, and is the copresident emeritus of Stanford Angels & Entrepreneurs, an alumni association that fosters relationships to strengthen the Stanford startup community. Robert was on the board of SmartDrive Systems for 14 years (acquired by Omnitracs), has coauthored several articles for the Harvard Business Review and California Management Review, and is a frequent contributor to Fortune, TechCrunch, VentureBeat and Forbes.

Robert was previously general manager of the video and software solutions division for GE Security, with annual revenues of $350 million. He was also executive vice president of Pixim, Inc., a fabless semiconductor firm specializing in image sensors and processors (acquired by Sony). Before Pixim, Robert was cofounder & chief executive officer of Weave Innovations Inc. (acquired by Kodak), a network services developer that invented the world’s first digital picture frame, and delivered photos and other digital media to PCs and internet / mobile devices.

Robert served in various management roles at Intel Corporation, including an executive position on their corporate business development team, in which he invested capital in startups that were strategically aligned with Intel’s vision.

Robert is the coinventor of four patents and served as lead researcher for Andy Grove’s best-selling book, Only the Paranoid Survive.

Robert holds a BA from UC Berkeley and an MBA from Stanford Graduate School of Business. He is married with three children.

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Sours: https://www.gsb.stanford.edu/faculty-research/faculty/robert-e-siegel
''Why I Fire People'' - Elon Musk

Resources

Prologue

I was previously working at Twitter as a software engineer. My team’s mission was to maintain and upgrade one of Twitter’s main infrastructure services.

We owned numerous Tier 1 services, and that came with significant responsibility. If there even was a couple of minutes of downtime in our of our services, users would not be able to post Tweets or search for Tweets (this actually happened during one of my on-calls). If our services did not run like a well-oiled machine, company revenue would decrease drastically.

Because of thehigh stakes in owning these services, I learned a great deal. I learned how to handle increased traffic, detect key metrics when service health is below SLA, automate operational tasks, etc.

After a while, the on-calls started wearing me down. I would get woken up at like 4 or 5 AM from time to time for various reasons. Our read and write lag would increase exponentially from the increased traffic, or one of the servers was down. Unfortunately, we did not have any production system to perform automated tasks to remediate these issues.

I could not envision myself performing at this level for another four years. It was not mentally sustainable. As a result, I decided to start preparing for my next career transition.

I wanted one thing to change in this next chapter of my career. When I accepted my job offer for Twitter, I was not too ecstatic about the offer letter because I did not know how to negotiate properly. I wanted not to repeat this mistake. I wanted to feel like I was on top of the world when I signed that offer.

Coding Prep

Leetcode is a reliable resource to hone and touch up your coding interview skills. It has a huge bank of questions, and the questions are tagged by topic and by company.

I did not want to study aimlessly on leetcode. I had to strategize in how I would approach leetcode in order to optimize success in the shortest amount of preparation time required. I wanted to be certain that I could land a job offer in roughly 4 months. It was also important that I could get multiple offers. That way, it would be clear to me that the company I chose was the best fit for me. There would be no regrets accepting that job offer.

My round of interviewing that led to my Twitter offer gave me a rough ballpark of the type of questions that would get commonly asked. As a result, I created an agenda of topics to review that was ordered by popularity, and I allotted a certain amount of preparation time for each topic depending on how I felt about that subject.

During my previous round of interviewing, trees and graphs were one of my weaker areas. Unfortunately, I encountered numerous interviews that involved a question in that area. Because I was not well versed in that topic, I performed poorly in those interviews, and as a result, I did not receive an offer. I did not want to be in that situation again.

This was my agenda schedule:

  1. Hashmaps (1 week)
  2. DFS/BFS/Trees/Graphs (4 weeks)
  3. Backtracking (3 weeks)
  4. Heaps (2 weeks)
  5. Sliding Window (2 weeks)
  6. Stacks/Queues (1 week)
  7. Dynamic Programming (3 weeks)

Another crucial planning component was how much time I should focus on for each level of difficulty. Because I was applying for senior roles, I allocated

  • 60% on medium
  • 35% on hard
  • 5% on easy

It was not efficient for me to spend a large chunk of time on easy questions since I would not learn any new techniques. I wanted to focus on medium and hard questions, with an emphasis on medium.

For easy questions, I would strive to complete them in under 30 minutes. For medium questions, I would strive to complete them in under an hour. For hard questions, there was no goal for when to complete them by, since those questions would take several hours, sometimes days, to solve. Quite frequently, I needed hints to solve the hard questions, and there were times when I gave up even with the hints. When that happened, I would read people’s solutions in-depth and would not move off the problem until I truly understand the solution.

I would aim to do 2–3 questions per day during the weekday and 4–5 questions on weekends. Fridays were my rest day. It was very important not to burn myself out during this process.

If I was not able to solve the question without hints or if the problem took a lot longer than expected, I would jot down that question on a Google spreadsheet and make a note on why that question was tough or what I had to do differently. I would use this spreadsheet to spot patterns. I would realize that the same technique was applied to another similar question. I would also notice repeated mistakes that I kept making.

After 2 months of coding prep, I started doing mock interviews on Pramp once a week. I wanted to start finetuning my ability to solve a question under pressure in a live setting. My goal was to complete a Pramp question in under 20 minutes. I would track each performance on Pramp on a Google spreadsheet to visualize how I was trending.

I recommend reading “Tips for How to Succeed in Coding Interviews” to help crush your coding interviews.

System Design Prep

For me, prepping for system design was going to be tougher than prepping for coding questions. There was so much knowledge to learn, especially since I was still early in my career. I also felt I was not exposed to a wide array of large-scale systems at Twitter. I was only familiar with messaging systems like Kafka.

I started prepping for system design around month 2 of interview prep. I looked at these various resources:

I knew that system design would be weighed very heavily for senior roles. I decided to use interview.io for mock system design interviews. These were helpful because I was paired up with FAANG senior/staff engineers, and they asked me tough questions. I would get constructive feedback on my performance. I would learn something new from each mock interview. For instance, I learned the various types of caches, such as write-back cache, write-through cache, and write-around cache, and understood the uses cases of each. These mock interviews were a life-saver. I recommend using their service for mock system designs.

Creating My “Company List”

Around month 3, I decided to compile a list of companies I wanted to apply to, and I organized them into 3 tiers.

Tier 1 companies were my target companies (i.e Google, Netflix, Snapchat, Pinterest, etc). These companies typically paid very well and had a good work/life balance. These companies also displayed strong revenue growth. I wanted to be confident that the stocks would continue to grow each year while working there. I also included hot pre-IPO companies in this list (Stripe, Coinbase, Databricks, etc) due to the potential of huge stock growth.

Tier 2 companies were public companies that I felt okay working there but did not feel too thrilled about them (ie Uber, Lyft, Salesforce, etc). These companies either had a stressful work environment or did not show strong company growth.

Tier 3 companies were companies that I felt okay rejecting offers if I did receive one. All of these were early series startups. It was unknown if these startups would ever IPO or get acquired.

Interviewing Timeline

I wanted to test the waters before interviewing for Tier 1 and Tier 2 companies. Around month 4, I polished my resume (I recommend reading“How to Pass over Hundreds of Tech Resume Screening), and I only submitted applications to Tier 3 companies. I heard back from most of them, and I was able to land a couple of offers from them.

Once the Tier 3 offers started flowing in, I felt confident enough to start scheduling calls with recruiters from Tier 1and Tier 2 companies. My schedule for that month was jam-packed. I would have virtual onsite interviews almost every day. There were many occasions in which I had to take PTO from work. I had to make sure that I did not arouse any suspicion from my manager or my co-workers. So, I made sure I was still doing Twitter work but only low-priority tasks.

The reason why I scheduled all my interviews so close to each other was that I did not want to deal with an offer expiration, the worst one being an exploding offer (in which an offer was valid for 2 or 3 days). I wanted all my offers to come in around the same time so that I could leverage them for negotiation.

Negotiation Time

After all my interviews, I received offers from DoorDash, LinkedIn, Uber, Stripe, and a couple of other pre-IPO companies. After speaking to hiring the managers to learn more about their teams, I narrowed it down to a handful of companies. One of the biggest requirements was that the team had a light on-call rotation.

When negotiation came around, I searched online (through Blind, Glassdoor, and other resources) on the type of offer that I should be expecting. I also read “Ten Rules for Negotiating a Job Offer.”

Essentially, this was how I negotiated my job offers:

  • I never revealed how much money I was making at Twitter. I did not want the recruiter to give me a lowball offer.
  • I never revealed my initial starting number. I would try to have the recruiter give me a starting number. Again, I did not want the recruiter to give me a lowball offer.
  • When a recruiter did give me a number, I would explain that I would take time to look it over with my family. That way, the recruiter did not pressure me in making a decision quickly.
  • I also leveraged my other offers to negotiate. This was the most helpful way to increase the compensation. If the recruiter cannot increase base salary, try asking for an increase in RSU or in signing bonus.

All of these tactics helped land me an offer that was more than double my salary. When I signed that offer, I felt super ecstatic because I did not believe I was able to achieve this task.

This was my third time doing interviews, and this one was the most successful. I applied to over 20 companies, landed a phone interview at 15 companies, landed virtual onsite at 12 companies, and received 7 job offers.

I do resume/interview workshops with clients applying for software engineering jobs. I have worked with over 50+ clients, and they have landed job offers at companies like DoorDash, Square, and 1Password.

If you need help with resume or interview prep, email me at my email [email protected].

You can find me on Instagram and LinkedIn.

Sours: https://towardsdatascience.com/how-i-doubled-my-salary-in-4-months-e8c5b2b9f5bc

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