The Role and Compensation of Machine Learning Scientist (MLs)

The compensation for machine learning scientists (MLs) typically ranges between 40 to 85 lakhs per annum, depending on their level of experience. For someone with 5 years or 6 years of experience, they can expect to be on the higher end of this bracket, with a compensation range of 60 to 75 lakhs. However, this can vary widely based on individual performance and company budgets.

As one's experience grows, so does their compensation. Machine Learning Scientist (MLS) 3 roles typically have a minimum of six years to seven years of experience and extend up to 15 or even 20 years of experience. Their compensations range from 85 lakhs to 1.7 crores per annum, with the typical range being 6 to 15 years of experience.

There is also an MLS4 role, which is Machine Learning Scientist or Applied Scientist, sometimes referred to as Staff Scientists or Senior Staff Scientists. These roles typically have anywhere between 10 to 20 years of experience and are often held by senior professors at top universities who get hired by companies for their expertise. The compensations for these roles range from 1.8 crores to 3.5 to 4 crores, depending on the individual's skills and knowledge.

As one moves up the career ladder, so does their compensation. Machine Learning Scientist (MLS) 5 roles typically have 15 years of experience or more and compensations range from 4 crores to above. This role is often referred to as a Senior Principal Scientist or Senior Staff Scientist and is essentially like a director-level position on the managerial side.

Beyond these roles, there are even seniorer positions such as Principal Scientists who are typically at the Vice President level. These individuals are referred to as Distinguished Scientists and have phenomenal researchers and engineers who are also at the same level. Their compensations vary widely based on their individual performance and what they bring to the company.

Growing in a Fang-like Company is Extremely Competitive

Gaining experience in a Fang-like company is extremely competitive, requiring not only technical skills but also other essential skills such as people management. The competition keeps increasing as one moves up the career ladder, making it even more challenging to secure top roles. Additionally, there is a significant amount of luck involved in career advancement, and those who get into good projects with growth are at an advantage.

By the time someone reaches senior positions such as Senior Manager or Director, they will be managing teams of scientists and engineers, requiring them to develop people management skills and expertise in managing large complex programs. They must also make informed scientific decisions on what to do next, which can never be easy. The compensation for these roles may seem high, but so is the level of competition, responsibility, and skill required.

"WEBVTTKind: captionsLanguage: enhi friends one of the questions that our students ask us are what are the typical compensations at fang companies or fang like companies some of the top companies that hire the same talent that you'll encounter at companies like facebook amazon google netflix etc now at many fan like companies there are multiple machine learning and data science related roles you have the data analyst then you have the data scientist you also have machine learning engineers who do some amount of software engineering and some amount of machine learning sort of like 50 50 then you have the machine learning or the research or applied scientist roles then titles and names could differ slightly from company to company typically your machine learning engineers and machine learning scientists make the highest compensation followed by data scientists followed by data analysts that's the typical order in which you can sort the compensations at any given level again the compensations of machine learning engineers and machine learning scientists is very close with a slight difference the compensation for data scientists is slightly less than machine learning engineers and machine learning scientists while the compensation for data analysts is slightly on the lower side as compared to data scientists again some of these titles could be different at different companies again like companies is a reasonable set of companies and hence the titles could differ slightly in this video we will focus on machine learning engineer and machine learning scientist type of roles again the compensation at most of these companies have multiple components you typically have the base compensation which is given to you as cash on monthly basis you also have cash component that is either given annually or half yearly typically then you also have the stock component that is often given either every six months or every 12 months for most of these companies since they're publicly traded you can sell the stock as soon as you get within just a few days so for all practical purposes the stock is equivalent to cash for all practical reasons so we will use the stock the cash and the base pay we'll take the the sum of all three when we discuss some of these typical compensations and we will only talk about gross compensation not ctc because gross includes all of the compensation which can be converted to cash in the in the one year duration typically when some of these companies go to campus placements in their ctc they also include other things like stock compensations over the next four years cash bonuses over the next four years and also some benefits whether it's life insurance or health insurance so we will not talk about those right because these ctcs can be exaggerated because primarily there is cash and also stock components over the next four years any experienced professional will not talk so much about ctc they'll talk about gross compensation only so we'll focus on it for your definition gross compensation is the total money either in stock or cash that employees make in one year duration okay so i hope this definition is clear again all of the compensations we are discussing are in india not in u.s europe or any other geography most of these compensations are approximate compensations they are not the exact compensation there will be some variability from different companies to different companies different teams to different teams and things like that okay so first let us go to the machine learning scientist one right it's often written as mls one or machine learning engineer one roles typically freshers who have strong foundational skills typically get into this machine learning scientist or applied scientist one rows and typically people between zero to two to three years experience get into these entry level machine learning scientist rules and their compensations in india typically range between 22 to 40 lakhs per annum this is the grass compensation again if you already have one year experience you could be hired at 30 35 lakhs per annum compensation this all compensations are in indian rupees only okay so then you have the machine learning scientist too or the equivalent roles some companies have slightly different titles like applied scientist or research scientist these are people who typically have between two to seven years of experience typically not necessarily always but very often at least two years of experience is typically expected for mls 2 or machine learning scientist two roles and their compensations typically range between 40 to 85 lakhs per annum if you are somebody with say 5 years or 6 years of experience you will be on the higher end of this bracket you could expect 60 75 60 to 75 lakhs compensation in such a scenario the third role is called machine learning scientist three or also referred to as applied scientist three and these are typically referred to as senior scientists these people typically have a minimum of six seven years and and the experience could extend to 15 or even 20 years i've seen senior scientists with about 20 years experience also but the typical range you'll encounter is 6 to 15 years of experience and their compensations in fact like companies is anywhere from 85 lakhs to 1.7 crores per annum again please remember as your salary increases your tax that you have to pay to government of india also increases let's not forget that reality then there is a mls4 which is machine learning scientist or applied scientist for these are also sometimes referred to as staff scientists some companies also call them a senior staff scientist some companies call them as principal scientists etc there they typically have anywhere between 10 to 20 years of experience lot of senior professors at top universities typically get hired as principal scientists at many of these companies now the compensation for them ranges anywhere from 1.8 crores to 3.5 to 4 crores depending on how skilled the the principal scientist is and how wide and deep their knowledge is so the typical range is anywhere from one and half to three and a half crores per hour again your principal scientists if you want to compare them to the managerial roles they're typically like your cd managers similarly your senior scientists are typically like your managers then after principal scientist comes your senior principal scientists or senior staff scientists and things like that these are also called as applied scientist 5 or machine learning scientist 5 roles and they typically have 15 years of experience or more and their compensations range from 4 crores to above again there is a huge variability in compensation as people go up based on the skills they bring to the company and the organization based on what they can deliver and what they have built in the past right and a senior principal scientist is sort of like a director level uh on the managerial side again you also have other more senior people you have scientists at vice president level you could also have some people at senior vice president level and beyond a vice president and senior vice president you typically have the ceo and scientists who are at the vice president level are typically referred to as distinguished scientists you have some phenomenal researchers phenomenal engineers who are at the distinguished scientist or distinguished engineer level they're only a handful in any major company right but and of course their compensations vary quite uh quite a lot depending on what they bring to the table what they've built and what their task with building in the new organization but they're typically at a vice president level uh in some of these tech companies again please understand that growing in any fang like company is very very competitive right it's super duper competitive in addition to just having technical skills like machine learning and engineering skills you need to build lot of other skills as you grow up so most of these roles are like a pyramid there will be lot of people at mls one but the competition keeps getting severe and you have to compete with some of the best people to keep growing up it's not going to be easy the level of competition only increases as you go up of course sometimes you get lucky you get into good very nice projects which grow there is also some luck involved in your careers but in addition to some luck and in addition to the technical skills that you build you also need to be a good people manager because you will manage teams you will mentor a lot of people uh by the time you go into a senior site by the time you go into a senior manager or a director level person you'll be managing considerable sized scientists or teams of scientists and engineers right so you have to learn some of those people management skills you have to learn about how to manage large complex programs how to innovate for a product so it's not just technical skills all other skills also we need to be built and need to and you need to make very informed scientific decisions on what to do next and they're never easy right so while the compensations might look slightly high so is the competition so is the responsibility that you will have to hold on your shoulders as somebody who is a principal scientist or or a senior principal scientist or a vice president at many of these companies we hope that this gives you a rough estimate of what you could be in a fang like company in terms of compensations responsibilities and skills that you will have to build all the very besthi friends one of the questions that our students ask us are what are the typical compensations at fang companies or fang like companies some of the top companies that hire the same talent that you'll encounter at companies like facebook amazon google netflix etc now at many fan like companies there are multiple machine learning and data science related roles you have the data analyst then you have the data scientist you also have machine learning engineers who do some amount of software engineering and some amount of machine learning sort of like 50 50 then you have the machine learning or the research or applied scientist roles then titles and names could differ slightly from company to company typically your machine learning engineers and machine learning scientists make the highest compensation followed by data scientists followed by data analysts that's the typical order in which you can sort the compensations at any given level again the compensations of machine learning engineers and machine learning scientists is very close with a slight difference the compensation for data scientists is slightly less than machine learning engineers and machine learning scientists while the compensation for data analysts is slightly on the lower side as compared to data scientists again some of these titles could be different at different companies again like companies is a reasonable set of companies and hence the titles could differ slightly in this video we will focus on machine learning engineer and machine learning scientist type of roles again the compensation at most of these companies have multiple components you typically have the base compensation which is given to you as cash on monthly basis you also have cash component that is either given annually or half yearly typically then you also have the stock component that is often given either every six months or every 12 months for most of these companies since they're publicly traded you can sell the stock as soon as you get within just a few days so for all practical purposes the stock is equivalent to cash for all practical reasons so we will use the stock the cash and the base pay we'll take the the sum of all three when we discuss some of these typical compensations and we will only talk about gross compensation not ctc because gross includes all of the compensation which can be converted to cash in the in the one year duration typically when some of these companies go to campus placements in their ctc they also include other things like stock compensations over the next four years cash bonuses over the next four years and also some benefits whether it's life insurance or health insurance so we will not talk about those right because these ctcs can be exaggerated because primarily there is cash and also stock components over the next four years any experienced professional will not talk so much about ctc they'll talk about gross compensation only so we'll focus on it for your definition gross compensation is the total money either in stock or cash that employees make in one year duration okay so i hope this definition is clear again all of the compensations we are discussing are in india not in u.s europe or any other geography most of these compensations are approximate compensations they are not the exact compensation there will be some variability from different companies to different companies different teams to different teams and things like that okay so first let us go to the machine learning scientist one right it's often written as mls one or machine learning engineer one roles typically freshers who have strong foundational skills typically get into this machine learning scientist or applied scientist one rows and typically people between zero to two to three years experience get into these entry level machine learning scientist rules and their compensations in india typically range between 22 to 40 lakhs per annum this is the grass compensation again if you already have one year experience you could be hired at 30 35 lakhs per annum compensation this all compensations are in indian rupees only okay so then you have the machine learning scientist too or the equivalent roles some companies have slightly different titles like applied scientist or research scientist these are people who typically have between two to seven years of experience typically not necessarily always but very often at least two years of experience is typically expected for mls 2 or machine learning scientist two roles and their compensations typically range between 40 to 85 lakhs per annum if you are somebody with say 5 years or 6 years of experience you will be on the higher end of this bracket you could expect 60 75 60 to 75 lakhs compensation in such a scenario the third role is called machine learning scientist three or also referred to as applied scientist three and these are typically referred to as senior scientists these people typically have a minimum of six seven years and and the experience could extend to 15 or even 20 years i've seen senior scientists with about 20 years experience also but the typical range you'll encounter is 6 to 15 years of experience and their compensations in fact like companies is anywhere from 85 lakhs to 1.7 crores per annum again please remember as your salary increases your tax that you have to pay to government of india also increases let's not forget that reality then there is a mls4 which is machine learning scientist or applied scientist for these are also sometimes referred to as staff scientists some companies also call them a senior staff scientist some companies call them as principal scientists etc there they typically have anywhere between 10 to 20 years of experience lot of senior professors at top universities typically get hired as principal scientists at many of these companies now the compensation for them ranges anywhere from 1.8 crores to 3.5 to 4 crores depending on how skilled the the principal scientist is and how wide and deep their knowledge is so the typical range is anywhere from one and half to three and a half crores per hour again your principal scientists if you want to compare them to the managerial roles they're typically like your cd managers similarly your senior scientists are typically like your managers then after principal scientist comes your senior principal scientists or senior staff scientists and things like that these are also called as applied scientist 5 or machine learning scientist 5 roles and they typically have 15 years of experience or more and their compensations range from 4 crores to above again there is a huge variability in compensation as people go up based on the skills they bring to the company and the organization based on what they can deliver and what they have built in the past right and a senior principal scientist is sort of like a director level uh on the managerial side again you also have other more senior people you have scientists at vice president level you could also have some people at senior vice president level and beyond a vice president and senior vice president you typically have the ceo and scientists who are at the vice president level are typically referred to as distinguished scientists you have some phenomenal researchers phenomenal engineers who are at the distinguished scientist or distinguished engineer level they're only a handful in any major company right but and of course their compensations vary quite uh quite a lot depending on what they bring to the table what they've built and what their task with building in the new organization but they're typically at a vice president level uh in some of these tech companies again please understand that growing in any fang like company is very very competitive right it's super duper competitive in addition to just having technical skills like machine learning and engineering skills you need to build lot of other skills as you grow up so most of these roles are like a pyramid there will be lot of people at mls one but the competition keeps getting severe and you have to compete with some of the best people to keep growing up it's not going to be easy the level of competition only increases as you go up of course sometimes you get lucky you get into good very nice projects which grow there is also some luck involved in your careers but in addition to some luck and in addition to the technical skills that you build you also need to be a good people manager because you will manage teams you will mentor a lot of people uh by the time you go into a senior site by the time you go into a senior manager or a director level person you'll be managing considerable sized scientists or teams of scientists and engineers right so you have to learn some of those people management skills you have to learn about how to manage large complex programs how to innovate for a product so it's not just technical skills all other skills also we need to be built and need to and you need to make very informed scientific decisions on what to do next and they're never easy right so while the compensations might look slightly high so is the competition so is the responsibility that you will have to hold on your shoulders as somebody who is a principal scientist or or a senior principal scientist or a vice president at many of these companies we hope that this gives you a rough estimate of what you could be in a fang like company in terms of compensations responsibilities and skills that you will have to build all the very best\n"