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How did you get to where you are today? What is your story? What incidents and experiences shaped your career path?

Summarized By: Jeff Musk on Fri Sep 11 2020
sure, Absolutely. So Aziz. Probably aware. I've spent most of the first phase of my career a data scientist, first as an individual contributor. Eventually the manager, uh, most recently as a executive and then in the last several months have started doing the best thing on the side as well. Um, that is a pretty unusual trajectory. I got here through a pretty weird path. I was originally a nuclear physicist by academic training. Way back in the day, um, and I got to nuclear physics, mostly because I was studying to be a physicist, and I realized I had the neck for computer program, and I really liked kind of sitting at this intersection of physical on numerical simulation, intersecting computer programming, available products. Um, after I left academia, I realized that that basically what the industry calls data signs on bond and I kind of came in industry before data signs was the term. But what we have now come to call data science, actually, what I was focused on, um, so I got my start in industry in uber was very early on in the process, I was basically hired as a software engineer who was responsible for Building Network, which was two mathematically and sort of numerically complex for your typical Python developer. And I really like that in that gave me an opportunity to do a lot of data sign and was a lot of data work, but was always kind of grounded in a product that people could like use every day. But there was sort of, I mean, feedback loop. Um, over time realized that I was shipping a lot of the early features of the uber experience. I felt the very first versions of dynamic pricing research price being uber and that you kind of became a cornerstone of uber's ability to optimize the marketplace became sort of critical of the business. That strategy and I ended up working on the exclusively for several years. Um, you know, after a couple of years, I was basically managing a team. Uber at the time, was pretty flat, you know, sort of not formally hierarchy. But I realized I really loved managing that one of the fundamentally true things about my career. Instead, I like to coach and I like to mentor, and I just sort of grow people. I felt sort of called the People Management. When I was in academia, I felt sort of called the Teaching was sort of one career about everything about in the industry. The involved was was management on DSO. Spend a lot of time building out data signs as a function with a number. Spent a lot of times sort of building organizations, you hiring out like the 1st 50 people and bringing in like a more seasoned executive because I was still fairly early on in my career of that point, Um, and then you executive just sort of followed from that. I've always I've realized that I've sort of this knack for early stage data at the early stage companies, So it's been kind of a repeat trajectory of kind of higher. The 1st 50 people you know, uber. And then I went to tall. I did something very similar, Uh, and that sort of continued that path. What

What is your investment philosophy? What type of founding team, industry domains, business models, and stages do you invest in?

Summarized By: Jeff Musk on Fri Sep 11 2020
good question. So why didn't cover in? My story is kind of on nights and weekends. I've been doing an angel investor. Um, working as an angel investor in the last year or so have transitioned much more of a full time investor, Um, in the areas. So why I say that is the areas I tend to invest in and the stages I tend to invest in our the stages that I always felt like I have a lot of experience in as an operator. One the value proposition is much more directly is much more clear. The founders that my experience is very directly a but the wrestling with what they're working on. Um, but also I feel like just my expertise will give me an unfair advantage to recognize really great opportunities that maybe the rest of doesn't see eso with the maps to members of answering the question directly, I'm looking for a i companies and ML companies at the earliest stage. You know, the really rough strike zone for what a I company is is You know, if the data side just quit the company folds is kind of the rial. What I tend to look for it. Um, industry domain. I don't have a narrow aperture. I consider most most industries. Um, I fundamentally believe that ai as a domain has always been theoretically applicable to farm or than just enterprise sas or, you know, or sort of consumer mobile app. Uh uh. And but I think that a lot of work in the last several years on the both in terms of the state of the art of a I research as well as data infrastructure and data platforms have been turning theory into reality that, you know, some of the best investments I've made our AI for fish farms or a I for, you know, uh, convenient story. So, uh, any industry really intrigues me, but always at the earliest agent, I'm looking for founders who really benefit from somebody was sort of bottoms up operational experience, Um, in opportunities that I feel like I'm the first to really see in the formal investing industry.

What information, statistics, or slide deck do you like to see in a founders' first email?

Summarized By: Jeff Musk on Fri Sep 11 2020
um, I think that that one of the most common mistakes founders tend to make eyes. They probably over estimate the degree of sophistication, at least in my domain and ai them up. They overestimate How much is it necessary to start reaching out to investors? So the theme I've seen with a lot of B. C s that I subscribed to, there's no such thing as too early. I'm often talking to people while they're at their last job or where they've got kind of half of a business. Um, so I would say an incomplete sort of structure makes sense. Probably speaking, you know what I'm coming to look for? Um, in the first email is sort of a little bit about the founder, and usually some of it is sort of your career path. But what I'm really trying to figure out is it's sort of why this company, why this problem and why do you feel like you have conviction that you're the right person to solve this problem? And so you kind of want to see a little bit of like, Hey, I'm working on a company in the space I'd love to understand, like the sort of domain or problem you're chasing and then a little bit about you just sort of, you know, really? For example, like, I'm really interested in optimizing the transportation and logistics layer of, you know, trucking in the Midwest. You know, I grew up in a family of truckers, just graduated with a PhD in computer science, Uh, and feel like, you know, I'm seeing whatever the opportunity you feel like you're seeing is like, Would you like to talk with me about it is like a perfectly wonderful first thing.

Can you walk us through the due-diligence process, and metrics you look for? What are the steps and a timeline from a founder's first email to cutting out a check?

Summarized By: Jeff Musk on Fri Sep 11 2020
sure. Uh, the the first of all say, in the earliest stages there is very rarely a rigorously perform a process of, uh and same thing with timeline. I've run an entire process and as quickly as 36 hours from from sort of first intro toe team that sending the safe and something a check. Other times, these relationships that go on eight or nine months were either I'm not ready or the founder is kind of, you know, taking timeto really get hipster her confidence in conviction around the idea. But broadly speaking, three earliest ages. What I'm looking for is you know what kind of what problem you're solving that you have kind of conviction of like, look, this is it specifically the gap in the market place I'm seeing and you really should have a problem. I think, especially with a i N m l founders is very, um, common for them as the main experts that kind of have a piece of technology or kind of think of the solution first and then work backwards to the problem. Um, that's not always a bad idea, but but it is a fairly common ate. The pattern that you kind of forget to make sure that there is a problem that somebody will pay for the other. So start understanding of the problem. Ideally, some research research can be a simple as I'm running surveys or, you know, doing a lot of interviews. It can also be as elaborate them market sizing exercises and sort of gardener reports. Um, I'm looking for war progress around a product or M V P. At the seed stage. Having a prototype is great. Having a paying customer you're probably over delivering having no idea of what you wanna build is inadequate. But there needs to be some kind of narrative. Here's a problem. Here's what I know. It's painful. Here's what I want to build and constantly kind of white solve that problem. I'm usually looking for some indication of that. You're at the beginning stages of assembling the right team for this. So that and this is both, you know, have you thought through the problems faced, Understand? Oh, wow. This is gonna be really hard to sell, So I'm gonna start finding a go to market person early on. I'm building a consumer product start. Really need to have a product person involved. There's kind of ah wisdom that you demonstrate by by making sure you have the right folks. You can also make a mistake and this and that. You know, we want to build like a consumer lifestyle like technology app, but we don't have anybody on the team who can actually write software for a living. It's a is like a big red flag, and then the second thing you look for is just caliber of the team. And this is both, like, helps me feel good about the trajectory of the company, but especially if they're not the founders. But there's somebody kind of brought it halfway through the process. You're also showing some soft skills of that. You, as a founder, can sell right that you are the first people you sell before you sell your first customers or your first employees. You'd like Thio as an investor see that they do that, Um, that's in a timeline. A Zai mentioned timeline could be longer, short as possible, but usually it's a series of a couple meetings where, especially early, I'm like we're gonna be working together for 10 to 11 years. You know Thio. If this thing goes public to go to an exit, so it's important we know each other, Um, and so I usually do a series of meetings, usually minimum to sometimes as many three or four with the team. Um, if you have customers or you have sort of advisers anybody who's kind of adjacent to the company, it's usually prudent that I do a little trust but verify exercise and touching with them to make sure what you're telling me about how enthusiastic the customers, for example, it was actually validated from the first person. Uh, but earliest days there isn't a ton of company shirt like track record to diligence, so usually it's people, it's customers, and then I'm usually movinto. Do I want to cut the check up?

In a term sheet, what are typical terms for funding, cap table, governance, and liquidation? What should founders look out for in a term sheet?

Summarized By: Jeff Musk on Fri Sep 11 2020
Yeah. Good question. Um, so, as I mentioned, I'm usually some of the first money into companies. Um, eso typical funding rounds will vary plus or minus 20% and given market cycle. But I'd say right now your typical Siri's your start your typical see fundraise in the Bay Area has evaluation of probably somewhere between 6 to 10 million post line if you're doing a pre seed round, which which sometimes happens if if you're kind of in a position where you where you need some outside money just to kind of finish flushing out the idea you're looking between two and $6 million post money valuation attack those kind of terms, especially in the Bay Area, where usually working with funds there capitalized on the order of tens to hundreds of millions of dollars. Um, you're the first thing I look out for is investors who want to make the process too complex. You know, safe notes are wonderful invention for just allowing a really low cognitive load for way for founders to get early money in, Um yeah, and one thing like tender without for his founders, who are who are like what the investment company the $2 million evaluation. But I expect you didn't occur. All the legal costs of, like formal formulation doing a price around you're running the whole process. Um, there's kind of be a pragmatism in terms of what? For the amount of money I'm investing on the amount of effort you're required to basically execute the round. If you ever feel like they're just forcing you to go through too many loops there. Too many hoops hurdles. They probably are, um, usually earliest stage rounds. Early stage investors are targeting somewhere between 15 and 25% ownership For a pre seed round, they might go smaller, just pragmatic toe number of around you're gonna have to do. But, you know, the typical, like 20% ownership terms still apply. And and then for liquidation and governance early on in the process, um, these should be almost always 99.9% of the time. It's one X liquidation in very little governance requirement. Again, there's kind of this push pull of how how much effort you're going to need to go through to unlock the capital funding. Um, I don't usually recommend that you like form a board for your serious seed investors. Um, definitely not for Siri's precede. And if you do, it should be with the understanding that these investors only occupy that chair for, like around her, too. Um, but usually the governance process is a lot more informal. You know, most of what you're focused on it. And I was execution and kind of trying to get to, Ah, product market fit kind of sick.

How can founders reduce the risk of product-market fit? What are the common mistakes that founders make and how can they avoid those?

Summarized By: Jeff Musk on Fri Sep 11 2020
Yep. Um, yeah. I already touched on one, but it's very common in the A i n m l space for people to, um, either develop a product that they wish existed without actually making sure that the rest of the world wants it. The other thing, I think it's fairly common with data scientists. Well, I'll speak on data science. I suspect it might be more general if you kind of fall into the like, Wouldn't that be neat Trap where you talk to a bunch of friends and you say, Hey, I'm thinking about starting a company. Um, and here's the thing I wanna work on and you get a lot of enthusiastic responses and people are like, Oh, my gosh, that's so cool. Like, I wish I had a model diagnostic tools. I wish whatever you're working on, um, and when you actually take it to the next step of like, OK, but would you pay me money for it? Or like, or like, you know, Is it painful enough that you'd actually give me, you know, like a meaningful revenue stream? Suddenly all the interest dies up, you know, like all this was free. Great, but it's not actually a revenue generating product. Um, fairly common anti pattern. So So you want to make sure. I mean, enthusiasm is nice, but you have to kind of make sure the problem is painful enough and novel enough. But there's a least at the beginning, like a claim propensity to pay. And then, as you kind of get further along, you know, one of the big reasons why investors pushed Founders to get to their first revenue dollars, one that I think there's a lot of sort of. It takes a lot of financial risk of the company, but more importantly, it kind of forces you to figure out how to sell this and validate that there's actually somebody that they're cool spend money on me. Um, that's a big part of it, for sure.

How do you set goals and track a startup's progress? How much do you get involved in the day-to-day operations? When do you intervene?

Summarized By: Jeff Musk on Fri Sep 11 2020
good question. So a za former operator, I, um I always have a bias total, like, personally solve the company's problems that I just learned to kind of control is I think I bias towards action bias towards building. Um, So how much do I get involved in day to day operations? Probably a little too much being 100. Um, but I think I'm kind of depriving the company and opportunity to develop some muscles right when that happens. And I'm when they're kind of focus on this that, you know, pain is and struggle is both, if done too much of it. It's essentially problematic to accompany, But a little bit of pain is really helpful and really constructive. It kind of, you know, the bugs that you spend the most amount of time tracking down and are the hardest to solve are the ones you never commit again. There's kind of this pain can be instructive. So, um, I tend to try not to intervene. Um, just because a company is struggling where I tend to get involved is either it is The existential were kind of through the like, constructive paying phase than I obviously take a lot more. I believe it to be existentially get more involved. The other thing I tend to do is if you kind of think of a startup as, like it's a game you're playing and you only have a certain amount of moves you could make or certain decisions you can make until your counter goes to zero on the counter. In this case is your financial runway. Then you're naturally incentivized to skip is many steps as possible and avoid as many false sort of blind alleys as possible, especially early on. So I tend to give a lot of coaching and advice around like what I call plausible bad ideas, you know, ideas that, like sounds like a really good idea but you know, are sort of painful or bad for non obvious reasons. If I could just help kind of cut those kind of inefficiencies out, um, you know, I think it's a really great way to be helpful goals and tracking of a start of progress early on, following the mindset of building optimized like early on, we don't tend to be very KP. I driven most of goals, and progress is just sort of that's what I plan to do. And usually it's some sort of bullying outcome. I plan to build this and I plan to build Service B. And I plan to talk to these three customers. And progress is like, did you do with them that kind of thing? That that would be the occasion if you look for, um, yeah.

What are the typical profiles of Limited Partners (LPs)? How do VCs raise funds from LPs? What are the typical investment terms?

Summarized By: Jeff Musk on Fri Sep 11 2020
good question. Um um can't share too much about the renegade LPC. A sort of company policy, but typical profiles, um, high net worth, individuals, family offices, you know, and usually a family office. You're looking at sort of families with networks and the 9 to 10 figures. 100 million, single, single digit billion range. Um, how did VCs raised funds from LPs? It's very analogous to how founders raise funds from V. C. That you you craft a pitch back. And whereas the founder is sort of saying, Here's a problem I see in the in the industry or in the market VCs air saying you hear some opportunities we see in the existing investing space here are some angles, but we think have merit that the world hasn't considered yet. Founders Air kind of selling their track record. And here's why I'm the right person to solve this problem. VCs are selling your investment track record. Here's why. You should sort of take my thesis really seriously. Um, typical investment terms kind of like the sea run the gamut there in a lot, by the way, Like one of things I talked to with founders a lot is kind of How do you want to structure your realm? Like, do you Do you want on Lee one investor in your realm Because you don't want a man is a lot of relationships, and you just kind of wanna do it quickly and move on. Do you want, you know, a big party round with, like, 50 different investors because then you can tap into, like, 50 different people sort of brain power. Something in the middle? Um, V six sometimes feel very similarly. You know, you have things like corporate venture funds that, like their fundraisers, they kind of go back to the board of whatever company. There are part of other people you could go to the extreme of, like a rolling fund where you're almost like it's like a SAS subscription for people who send your recorder. My preference is Aziz. I sort of thought about funds as I'd like to have a a find a number of equally distributed part because I think there is some value in sort of a diversity of opinion, diversity of experience, I mean that both in the sort of metaphorical as well, like literally, you know, people from underrepresented backgrounds with former are most useful LPs. Um, and but I also don't want this kind of power play of, like, certain people, you know, carry 80% of decision making power. Um, like people who are all sort of equal collaborators who loved who loved to work with the renegade team. Kind of a za partnership of equals. Each of us has our own role. The plane.

What qualities and accomplishments does your team look for while hiring associates or interns? What is the interview process and what type of questions are asked?

Summarized By: Jeff Musk on Fri Sep 11 2020
good question. I mean qualities for sure. Um, yeah, a demonstrated pattern of sort of curiosity, of the ability to kind of ask your own questions and then the ability to answer them, which is a combination of both resourcefulness as well as the ability to focus. There's kind of this, I think, some the associates who tend to struggle most are are ones who get really excited by certain parts of the process of sort of like, Oh, I like to prospect for companies or I really like toe like research one company. But the kind of do both successfully requires cure like curiosity and focus and knowing when to do, which I tend to like a bias towards action and like opinionated nature. This is not true for everybody. Um, but you know the ability to kind of be candid, forthright, kind of skipped right toe the sort of most interesting or media part of the conversation. But doing the way which is not off putting or or disrespectful or sort of breast, um is an art form, but But I think can be really effective. It kind of high bandwidth communication within team. Uh and, um, yeah, say That's kind of where it starts. And then obviously domain fit. If you're researching a i N m l company and you got to know a lot about a i N m l This is kind of

How would new industry developments affect the job market? What skills, majors, and upcoming job roles would you encourage students to consider?

Summarized By: Jeff Musk on Fri Sep 11 2020
I mean, let me start by evangelizing data science. Um, if you look at my Lincoln Emerging Jobs report ai engineer ml engineer data Scientists have been like the top three since 2016 I think data science growing at 74% annually according to Wellington. So let's just start there, um, new industry developments affecting the job market. I mean, I think the trend, especially with Covic if you kind of think of covert, is a kind of accelerating all of these existing trends, um, continues to push towards opportunities in the knowledge economy. So anything around technology or technological product development, I think there's a great job prospects. Um, and similarly, if you feel called to investing, um, Europe, understanding that domain, but then kind of layering on some sort of business or or sort of finance curriculum, I think next to 10 cents

What responsibilities and decisions does one handle in a job like yours? What are the challenges? What strategies are effective in dealing with these challenges?

Based on experience at: Angel Investor, Jigsaw Venture Capital
Summarized By: Jeff Musk on Fri Sep 11 2020
so I mean, as an angel, you're usually a one person operation. Eso responsibilities and decisions or all on your plate everything from which accounting firm should we be using with Or how do we mark our portfolio toe market all the way until, like, should be invest in this company, um, to stuff like marketing. And what's the best way to support this company? How do I find them there? You know that there data analyst has these specific requirements eso the challenges air obviously low down thing, all of that, that there's just like constant triage and prioritization exercise, which is incredibly multi dimensional. You want your portfolio to do well, you want to be best position for success in the future. You want all of that strategies that are effective and done with those challenges. A lot of introspection, a lot of sort of the ability to kind of non un emotionally evaluate the caliber of your decision, um, and kind of the ability to get really comfortable making decisions with incomplete operate information. You know, the ast the saying goes, are you know, we say it a lot in the community, like the batting average rules apply. What I mean by that is in baseball. If you connect on 40% of pitches, you're in the Hall of Fame. Your Ted Williams. Um you know, most of the average major league baseball batting averages, like 2027 26% of the time connecting. So recognize that you got to kind of make a lot of decisions quickly. You've got to kind of behave prudently. Um, but all you really got to connect on is like one and three, and you will do you do incredibly well, if you could do that consistently.

How did the school prepare you for your career? Think about faculty, resources, alumni, exposure & networking. What were the best parts in each of your college programs?

Based on experience at: MS, Physics, Michigan State University
Summarized By: Jeff Musk on Fri Sep 11 2020
Yeah. I mean, physics is a really great, um, foundational crash course for data science. Because physics is about the world of observing physical observations, physical explanations of the world, translating them into mathematical formula, solving those mathematical formula with some aim in mind and then taking those results and applying them back in the real world to learn something about the natural world. Um, Hamza Sciences does that, um, every day in terms of business, in terms of industry, it's such this sort of mapping of the physical world to the digital mathematical. The great thing about physics of if any of these students will let's do this or like pure mathematicians is they have a reputation for like, kind of, uh, low key. Breaking the math rules in ways which are probably a theorist would find very offensive. But but but, like, kind of gets you to the right answer. Um, so much for startups is that mindset is kind of, you know, like we don't get credit for showing our work if we can have to skip ahead or kind of break a few of the rules, but ultimately, for the right reasons, or to get to the right place. You know, the kind of justify the means and that things s O I really I really strongly encourage people think about physics, especially intersecting of user science as a domain. Um and then I think you know, the especially these universities out here, which are which, on the West Coast. I'm obviously from the Midwest. But these universities that have large alumni networks that really well known for computer science so much of recruiting and placement and and ways toe not to say that you're not guaranteed a job, but you're guaranteed to move to the top of the list of the top 10% of the list. Um, just by having the ability to find commonalities with some of these firms, we hired five people from Miami of Ohio and the 1st 50 people at Uber because Ryan Graves are first CEO was the Miami graduate, and he just tend to get those alumni recruiting blessed. So come, you know, leverage your your alumni networks for sure. Do you think about your career

What three life lessons have you learned over your career? Please discuss the stories behind these lessons, if possible. Stories could be yours or observed.

Summarized By: Jeff Musk on Fri Sep 11 2020
right. Um, so one big lesson, which is true in data science, is that a great model wrapped in the horrible product is a horrible model. And what I mean by that story, like early on in my time and uber, um, I was responsible for the entire dynamic pricing product from from the M l model and the data pipelines that back stopped it all the way up through the back in and internal tooling the front end product as well as even the screen in the up they used to see. Um, it was designed as a web page, you know, like micro Web page would just send you html into the out. And so I done a ton of operations reproach theory around how to build, like, the best dynamic pricing. Well, like you think of, um, it was getting late in the project, so I just kind of rolled out like a HTML for Dummies book and put together this, like, little rinky dink web page that we sent over the wire. And it was like you might screenshots over there on the line, but it was sort of like, you know, like, um, whoever dynamic pricing like Thank you so much for using uber standing pricing system. It would be like 6.5, actually, like we hope you daily problems. So and it's just like wall of text and then like a like except reject button at the bottom. And we ran this experiment and we saw that, like, as pricing was going up, people's propensity to pay, or basically the ratio of people who would accept toe the number of people who saw the app kept going up a swell And what we realized after we got over this idea of like, Oh my gosh, people like the product more the more we charge for it, like what is going on? Economics doesn't apply was that everybody just thought this page in the APP was like a Terms and Conditions page. It was just like a wall of text, you know, and like and like really key in point of like your surgeon, your prices of other like six X, just people completely missed. And so they were like, Yeah, terms and conditions, whatever click. And so we like the single highest customer support ticket volume of any one day. The day after I wrote this model out in this product out, and I realized that we realized pretty quickly what was going on from the support ticket, and we brought the designer over and they literally didn't touch a single line of code, didn't touch a single line of machine learning model, literally just redesigned that screen and made it like, visually obvious. Like, Hey, this is the member of your You're paying like and completely inverted The product went from from being like the single worst day in uber customers support history to our most successful day in terms of marketplace success, whether we want change to design. So point being, don't forget the private context you're building all these models into otherwise your models will not be successful. Um, second life lesson. Maybe a little bit more broad. Um, always trying to meet the dumbest person in the room like I remember everybody said, like uber was like one of my first start ups. You know, I've done, like, played with some stuff in college, but my real first, like go it startups with this company called uber. I think at the time I interviewed, it was called Uber Cab and everybody's like, How do you know? How did you pick it? Like, you know, kind of what you think and s o much of I found my notes and so much of, like what I thought Uh huh. It's gonna be important about about uber you wrote. Like all the reasons I thought he would be successful, like, not mattered. Except for one thing. Which was I was convinced that every single person who interviewed me I have to meet the whole team. I did like a all day interview. Every person who interviewed me was smarter than me. And I don't know that's necessarily true in terms of like you points. But I certainly felt like every person I met like what struck me with it was not some people wasn't most people. It was like literally every person I talked to I was like, Oh, man, I could learn something from you. You are You are, like operating other different level with me. Um and fundamentally true to this day, Every time I joined the team and I start out feeling like the dumbest person in the room, I'm the most successful. I have the most fun So always look for that. If you're the smartest person in the room, you need to find different room. Uh, and the final thing is that I think people, um, tend to respect intention more than execution when you're working with them. And this is a book to in management. I found it to be true and investing. I found it to be true in product building that like when we were building this first product for uber, there was a lot broken with it, and it cost too much. And dynamic pricing was like a complete mess. But I think people got especially relative to like what the taxi experience with, like in San Francisco at the time. They got that despite all the rough edges, they kind of saw what we were trying to do like that. There was kind of this idea of like we can fundamentally reshape how transportation works, and I think that that intent and that understanding of that unlocked a lot of goodwill for us early on, which kind of got off the tail on the product market fit, as I've like, worked with companies, now is an investor. I think they get the intent that I'm really trying toe like, Yes, I want your company to be successful on driver retirement for me, like that's kind of how I how I justify my paycheck and my firm is successful. But more fundamentally like, I'm just really excited about this idea of technology in A I kind of becoming more broadly brought into the world. And so even for the companies I passed on, you know, and I kind of I people tell me that I give, like, unusually detailed reasons like reasons for passing emails, you know, because I really do want to say, like, Look, this isn't for me here. A couple things I would do different. Um, you know, I did a study that, like, 20% of my investments, I think it was something like 19.5% have come from founders who I e. The ultimate passed on a prior idea or were referred to me from people I passed him. So I think that there's kind of something where where people see that in 10 and are kind of willing to look past what often is a really horrible outcome to an experience. E think always kind of push forward with intent. Even if your execution is squad and good things tend to happen, you know?

What starting job (after internship) would you recommend to students who hope to grow professionally like you? What other parting advice, dos, and don'ts would you give?

Summarized By: Jeff Musk on Fri Sep 11 2020
um I mean, the great thing about the growth of data sciences at this point, joining a data team is, ah, lot easier, A lot more straightforward than it was kind of. When I was coming out, I think my first title was like computational algorithms engineering. I could have back doors in the job I wanted. So I encourage students to start working. Um, try to join like an entry level data science team. And if you like early stage companies or you think that you've got kind of this risk taking sort of vibe and you want to do early company encourage you to do it sooner rather than later. You know the stereotype that as you get older, you have more constraints on your life and time, and you've got family and kids, maybe in the mortgage to pay your ability to take these sort of big, crazy risks goes down. So, um, lots of people, it's not for them or they think it's for them. And it isn't, um, but But I encourage you like if you if you think it's a maybe, try it and there's no shame in doing it for a year or two and then say, you know what? Like this isn't for me. I got to do something different with my life. Um, data science is so new, you know, like one of things I love about it as a leader in the space. Two weeks that I am a leader is like nobody does data science because it's like there family profession, you know, like, I remember people like I'm a doctor because my father was a doctor and his father was a doctor, like they didn't exist a generation ago. Everybody in the space, like, kind of decided at some point they were doing something else, and they pivoted into this and there there's, ah, high tolerance for sort of reinventing yourself and trying things out. Like so, money of people in the space were themselves product of reinvention. So try it. Take a risk. I absolutely focused on this. Um, you know, in the other thing I'd say is you don't let a skill set gap this wage you from pursuing the space. I know lots of people like I don't know how the program or I know how the program, but I struggle with, like, I don't know that I'm, like, as good at machine learning as I want to be. One third your hat in the ring. You rarely are people accurate ancestors of the room skill set. Um, too, You know, even if you don't end up on the data team, you know, I know lots of people who are who became data scientists because they started out as back in software engineers. There were pure software engineers, and they just kind of hung out with the data group. And then they became the person kind of help the data time just ship their Conan production and learns a memo along the way. There were data analyst were like a PH. D and e con showed up and were like started by writing dashboards and sort of like working papers and then taught themselves python over, You know, like through coursera or something like again, you can reinvent yourself. You could learn some of these skills on the job. I think you ultimately want to achieve that place where you're kind of like a PhD in computer science and a PhD in in sort of machine learning. But very few people start that way from day one