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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 Mon Dec 02 2019
I started school as a linguistics major and eventually moved into computational linguistics on at the time. Really, data science wasn't even a term yet. The official term was coined really kind of again back in 2010. And so I started working with my tell and Sun Microsystems in their early days, and we were trying to take things like the phone system, the PBX system, and integrate that in with a thin clients or that you could take your phone and your call and your conferences as well as your session from your laptop or from your thin client anywhere you went. The technology was a little bit early, and so we never ended up watching that technology. And so I went out and started doing contracting in consulting and eventually started working with a staffing firm and recruiting firms so that I could go and help other people grow their careers. So as time went on. I really focused on that but was also able to kind of grow the consulting work and really was able to help a lot of different people. But in that process, I was also able to learn from others on how they were learning, what was the latest and greatest technologies, what were they doing from a technical process because I was doing technical interviews, you know, dozens of times a day. And so they give me a chance to kind of learn what was happening within the industry and what companies were doing and what was coming up. And that's when I discovered Hadoop as a technology on Created the first Hadoop Users group here in Utah and one of the first ones in the country. And we grew that to be one of the largest and was eventually I was invited by the White House to come back and work with the Office of Science and Technology Policy and the NSF, the National Science Foundation, to help create the National Data Science Organizer's and the big data hubs around the country as well. And so now, data science and big data started becoming a thing, and the whole world started changing pretty drastically, and eventually I decided to go out on my own and started my own companies to focus on big data and data science across multiple sectors, including government as well as academia and nonprofit and for-profit in the straits. And so really, along the way I found that that it was in learning itself and then hearing from others in the network on what the technologies were, how to focus on them and how to quickly pivot to, learn what the latest and greatest technologies were and what the state of the art was and how to deploy that to a problem in the long run. I'd love to say that my technical skills are absolutely incredible, but really my core skills around finding the right use case, the right business need and presenting that actual value to accompany to business and making sure that the technology is sound so that it actually meets that needs. So that's kind of my story, and really it's based on others.

What are the responsibilities and decisions that you handle at work? Discuss weekly hours you spend in the office, for work travel, and working from home.

Based on experience at: VP Data Science at Finicity, Finicity
Summarized By: Jeff Musk on Mon Dec 02 2019
My responsibilities include everything from building and growing the team to deploying new technologies to create new innovation. So Finicity is a fin-tech company, and we really cover everything from the finance industry to the fin-tech industries, all of the big financial institutions, like the banks and the credit unions as well a lot of, lot of government or quasi-government type institutions. So things like the GSC is that a fanny meaning Freddie Mac, we work with groups like Experian to create things like Experian, Boost or Ultra FICO with the FICO team to, help them be able to take the credit scoring models that exist today and start being more inclusive of individuals around the country and hopefully around the world as time goes on as well. So far, we've been able to influence about 13 million credit score points across the industry. So very exciting. So my hours daily. So I work somewhere around 40 hours, sometimes 50 hours a week, almost always here in the office. I can work from home and I travel fairly often. I'd say about 25% of my time is travel and my core focuses with these teams are often. First, I talked about the innovation piece. What is that we're gonna do? And how's it going to actually solve a business need or a need for the individuals and the consumers in the finance space in general, then distilling that into something that we can deploy? Data science is is still a very difficult thing to get into production in the world because many of the paradigms don't necessarily match up nicely to API development, for instance, and so we focus a lot of our court time in that productionizing of models. And then, finally, it's fine tuning models, providing additional interpretation or validation of those models and finding ways that we can going and make sure that there is accurate. It's possible we are a consumer reporting agency. And so the data and the results, the attributes, the features and flows that we create all need to be as accurate as possible. Or we could negatively impact somebody's financial life, their career, their credit in many other aspects of their lives as well. So yeah,

What tools (software programs, frameworks, models, algorithms, languages) do you use at work? Do you prefer certain tools more than the others? Why?

Based on experience at: VP Data Science at Finicity, Finicity
Summarized By: Jeff Musk on Mon Dec 02 2019
So we use Python as a core technology. We do occasionally use R as well, and we started getting more into Java script as a team. And so I really prefer javascript as a tool. JavaScript is much closer to the native types of things that you would see in the browser such as JSON or Binary JSON, BSON or Jason B, and then from a full technology stack, we are a native US customer. And so, almost everything that we do is cloud-native, so we work with the Kubernetes stack quite a bit with Docker and other containerization type technologies and tools. We work with a lot of queuing tools or a lot of tools for subscribing in publishing to core technologies, and we're working with Cube flow now to help with that productionizing of our models, you're able to help go in and deploy everything in a key native, in a Kubernetes native type world, so that we can get in, have those models in and running as quickly as possible on go through the process. Not every wish JavaScript had the rich technologies that python does, and so a lot of times we are taking the core python code that we used to, get through our modeling, get through our interpretation and validation, get through before process and then report the actual model. After we've persisted it or saved it. We poured everything over into Java script or into another technology that will help us launch that quickly. Oftentimes, we do still use flask or other core technologies within Python to be able to create those APIs, but more often than on it's pushing it to Java script from a model perspective or even on the frameworks side, we do focus a lot in the TensorFlow ecosystem. We do occasionally work with others like PyTorch or paddle or other things that can help with specific algorithms or have different types of advantages, or sometimes working directly with just curious or caress and then with actual models. More often than not, we're focusing in the tensor to tensor libraries. So within Google, with intensive flow, Google has released a lot of open source code for models like Floyd or BIRD. So BIRD is a bi-directional algorithm that has both BIRD and Mini BIRD that allows us to apply a lot of directing coding or embedding on, then be able to use that for large representational transformations so that we can do large attention networks or other things as well on those same lines. When it's appropriate and we don't need quite as much speed, we'll use, other states of the art technologies like Excel Net. It's extra-large. It's very, very large, very powerful, but it's not necessarily very fast. And without something like you floor, whatever routes, it could be pretty. It's not necessarily pragmatic to use in the ecosystem. So along those lines, I'd like to step back and say, like, Why do we or go to the why question? So with a why for us, of course, we're always looking for accuracy and for being as correct and as close to the truth as we possibly can with any other models in our algorithms. We work to really take anything that is still Castaic or is random and try to push that through to being as deterministic or as true and as 1 to 1 relationship as we possibly can or decreasing the question around certainty and entropy. We really want to minimize. It is partly as much as possible so that we're not concerned about whether there is, you know, a bunch of things that we just can't explain or a bunch of things that are way too probabilistic. This is taking a certain quantum of a thing. And that's where a lot of quantum mechanics starts playing into the world of data and data science today. So once we've determined that something is as certain as we need it in order to answer a question, then it becomes a question of actually creating the value that the end-user and businesses going to use. So for Finicity, where business to business company but more often than not are and clients work directly with a consumer or an individual, And so because of that, they need things to be as close to real-time or near real-time as they can on so often, times are questions. Once we've determined how to answer a question, now have to be phrased in the terms of Can we do it in a time period? That actually matters, because if we can't provide the answer when it matters, and it doesn't really matter if the answer is correct or not. And so we have to reframe it often have to choose different models because of that. And then we have to figure out what is still the best thing that we can possibly provide while still getting it down in a time frame that will help somebody get access to a mortgage, will help somebody could access to a car loan or whatever else it is to help them be successful in their own financial lives.I'd say about 20 to 30% of our modeling is not a deep learning, very heavy black box style model. And we used those on to, well, really free potential areas so that the core two areas that we use them are our first prepping something. We're having something that could be done very quickly and that we need an answer on. But it's not an overly complex question. So it's something that we've already narrowed down, and we spend a lot more time on our data engineering and our feature engineering to ensure that those models are performing accurately, and then they're providing the results very quickly. And so oftentimes we really have to shrink down the data set to make sure that they're going to work under that scenario. Then, on the other side of the question, or the other side that we typically focus on is taking the results of the deep learning models or of something within that entire workflow when we're doing multiple ensembles and then we take something like an extra boost, or or we will get into like an SPM or even just other types of core regressions, for instance, or other classification algorithms to then really make sure that the outputs that we're receiving from the neural networks or from a deep learning model are still correct. And they're accurate. So we almost used them just to predict the accuracy and to try to understand or provide further interpretability or really annotations or other things of the models themselves. Then the third use case is where we do use it and end on these air typically for things or situations where we do need to have a really strong explainability. And we need to be able to provide coefficients that we know we can map directly back to the actual data that was used in the model and. We use it from the beginning all the way through the end of the process. But, you know, those 1st 2 probably makeup, you know, 20% maybe 25% of what we do. And the last one only makes up 12 maybe 5% of what we do here.

What things do you like about your job? Were there any pleasant surprises?

Based on experience at: VP Data Science at Finicity, Finicity
Summarized By: Jeff Musk on Mon Dec 02 2019
The pleasant surprises are that I came here really from that entrepreneurial type background, you know, had grown several different companies and invested in others, like storage or Big Squid. Other really cool companies that are growing very quickly. And although that was a lot of fun and they're a lot of great things there and for those core cos, I'm still very involved and really want to help them driven to succeed. For others, it was entrepreneurial type work. So it was stressful. There was a lot of anxiety to it and the pleasant surprise that was here at Finicity, I have that same excitement, and I have that same ability to grow and innovate and really do cool, exciting things that really do feel like we're a startup that's growing very quickly, but a startup. And so it's pleasantly surprising to come in, be able to really, I have everything that I love about growing and succeeding in business while still having the stability of working with a company that's been around for 20 years Now. Andi is continuing to grow and scale, things that I like about the job or the team and the people that are around all the time. You know, it's a very great company of love working here. And I think that's important? You know, if you try to find the right opportunities out there, Yes, You want to have the right technologies. You want to have the right tools, but you really need to work with the right people as well. And make sure that the people that you get along with that you compare ugly holiday sweaters around that, you know that you're going to have fun and what you do day in, day out. And that's not always the easiest thing to do. And so make sure in that interview process that you're finding the right ways to connect with people and to understand who they are and what their culture is, what it looks like. I additionally you know, uh, I get to make a lot of the decisions around what I do and what the team does. And so it's really hard to not love everything about that because I know what I'm focusing on. I know where we're heading. I know how to take that vision and be able to distill it down into the day to day or into the weeks, the months of the quarters that we're working on, a core project on to see that actually influence and consumers lives or the lives of the businesses and the people that work within them is really a great thing as well. I've always been very motivated by helping others and helping really unlock or solve for people's potential.

What are the job titles of people you routinely work with inside and outside of your organization? What approaches do you find to be effective in working with them?

Based on experience at: VP Data Science at Finicity, Finicity
Summarized By: Jeff Musk on Mon Dec 02 2019
I work here with the presidents of the company and the CEO. I do occasionally work with, like, the CEO or the CISO, the Chief Information Security officer, chief legal and other groups as well. I have a VP of products that we worked together very closely on our core goals are often about growing and scaling that product on that platform and then and, of course, work with the my core team members as well on then work across the organization with other, VPS of product or the directors and other developers and other groups here in the organization. Outside of the organization on more often than not, working with that same type of team the CEO of the presidents of the groups and then was somebody that's like a VP of data science or director of data science within the financial institutions or within Fs in the tech company, for instance. So, the approaches on working to be effective with them kind of ties back to that culture as well. So really trying to understand who people are, what they like to do, the way that they communicate. A lot of that you can pick up from body language, or you can pick up in the way that they're speaking with each other. So often times when you're talking to people at that C level or VP level or director level way, have a lot on their plate. And so they really want you to get to the point quickly, which I haven't done much in this interview. But they want you to very quickly say, Should I do this thing yes or no and very quickly. Tell me why once you're able to provide that value and help them understand how to go from the why toe what needs to be done. Oftentimes the how is not really what they're interested in. That's what they want others to be able to execute on. So making sure that you understand enough about how that you can then turn around and provide that and make sure that they can turn around and actually execute on that as a team or as a core group, eyes really key to success. But being able to abstract that and generalize and bring it up to the actual lies and what is actually important eyes, really, where big success. Talking with those individuals is going to come. The same thing applies, just a final note, with employees and with really anybody at any level. But oftentimes it could be difficult when you are not directly tied to an organization to know what your why is to know what motivates you day in and day out, whether it's mastery or sense of purpose or autonomy, and so trying to help people understand that I think, is his core for any of us to be successful.

What major challenges do you face in your job and how do you handle them? Can you discuss a few accomplishments?

Based on experience at: VP Data Science at Finicity, Finicity
Summarized By: Jeff Musk on Mon Dec 02 2019
Sometimes the challenges in finance, in general, are related to security or related to compliance. And so my challenges arere often trying to figure out how I get everybody on board, how we document everything correctly, how we make sure that we've gone through full security reviews and compliance, and then you'll be able to deploy something on time and in the way that actually makes sense for and consumers and helps us generate revenue. So, a few accomplishments would be things like, you know, earlier this year, as we're getting used to deploying processes as a team, we had to really work with a lot of our core tools that we have here for compliance and auditing to be able to get out and deploy any of our products products. And oftentimes that would take days or even weeks in order to go through the process and get an approval done for something like a change request, and if you have heard about the continues in vote continuous development or integration type process, you should be deploying multiple times every single day and parts of the organization here. Finicity had done that, and we're deploying 345 even quite a bit more times every single day. And so I needed to go and work with their teams to discover how they did it. How they were able to get through the process is quickly what they did to essentially cross the teas and dot the i's and make sure that everything was deployed as soundly as possible so that we could start getting on that same pace. And now our team is able to deploy really daily. And if we wanted to, we could actually deploy every hour. And so that was a really great accomplishment. It was really exciting. Some of the really big ones are that Finicity believes at its core that consumers should have access and control to their own data on that. It not only should be their own data, but they should know exactly what happens with it and why that happens. And so, from a security perspective, you could imagine when you're talking about financial data across the industry that everybody is used to their own paradigms. There are financial institutions that it has essentially been their data for decades. There are governments. There are all of these quasi-government agencies in data brokers and bureaus and many other groups as well that you know they have existed under one paradigm of data as time has gone. And so Finicity has really been leading that at the forefront and saying that we need to have an identity in and data directly in the hands of the consumer of the individual, and that's really starting to grow on the scale across the world. In Europe, you'll hear about open banking on a regular basis, and that's coming here to us as well. So we let a lot of the core goals and a lot of the core team, and we actually created the Financial Data exchange. And now that FDX protocol is starting to really take over the entire industry and identity and a lot of the core things that we do here Finicity is starting to become the industry standard in the industry norm. And so a lot of the work that they did with House White House tied back to GDPR. And now, as California's bringing in C. C. P. A in Nevada and New York and many other states are starting to launch their identity and their privacy laws. That is an area that Finicity has been core, too. And that we, as a team really feel like we've been instrumental in making action an actual reality, so okay.

What was the hiring process like for your job? What were the roles of people who interviewed you? What questions were asked and how did you answer them?

Based on experience at: VP Data Science at Finicity, Finicity
Summarized By: Jeff Musk on Mon Dec 02 2019
Nick Thomas and I, who's the president of the company, have known each other for years. On end early on, when they first started working with FEI Co. Two Great, the old ultra Fico score, he and I talked and I was able to come in and do a little bit consultant on. Then we continue to talk over the years, and he's been trying to recruit me for quite and then, early in the spring, we started talking about a lot of things that I was doing with identity and that he was doing this identity on. We decided that it would be a really good time to kind of join on, have become here, to work and grow that. So that process was really Nick and me talking and then me talking to Steve, the CEO, to make sure that this was the right opportunity and that this would be a good thing for both me and for them as a company. Right? so very minimal questions, you know, again, we knew each other very well in the industry. I think this is often of something that happens, especially as you get more senior in your career, I really would comes about the networking rather than an interview process. Now here internally for other people that we've hired at this same kind of level or just in general for positions. The rules are often tried to the core business unit or will create our own vertical teams here, where we're focusing on the product line. And so the old interview more often than not with somebody who is leading that from a technical perspective as well as the person that's leading all the product related stuff. Then it moves up and will have people at a VP level. And even Nick and Steve or Andy here, who is also a president, will make sure that they talk to the core team members as well, so that we do understand that that culture is the right fit, that the technologies are the right fit and that this is the work that somebody's going to want to do and be passionate about.

What qualities does your team look for while hiring? How does your team interview candidates?

Based on experience at: VP Data Science at Finicity, Finicity
Summarized By: Jeff Musk on Mon Dec 02 2019
See it's it's often around first what motivates them. So how do I know if somebody is really highly motivated by mastering something? And if so, how did they use that in their life and in their career to make sure that they become the best? That's something that they do or if they're highly motivated by something like a sense of purpose like they want to like me, really help others succeed and be able to unlock their potential? Well, how did they do that? And how did they make that practical day in, day out? Or finally, if it's around autonomy and they really want to have the freedom to do what they want to do, then I want to know, like again, How did they do that in their career? And how did they make sure that that autonomy is something that benefits the organization, their career and the things that they're trying to develop and be successful? So knowing that somebody understands themselves is really core to what we do when it comes to the machine learning deep learning and just data science side of the world? I want to know if people really focus on obtaining data, describing the data, cleaning the data of the engineering side and really making sure that you have the right data or if they're really good and really want to focus at the machine, learning the deep learning, really that modeling side, How deep do they go? How wide is their experience? They worked with many different model types and methodologies and why and then finally on the enter pied. But they're really good at the interpretation, the validation, or even the productizing of that data. How did they go in and make sure that that's something that could be deployed, almost something that actually scales to what the end consumer, end-user or business wants to be able to use?

What are some future career path(s) for you? What skills, certificates, or experiences do you plan on acquiring?

Based on experience at: VP Data Science at Finicity, Finicity
Summarized By: Jeff Musk on Mon Dec 02 2019
I think they are going to be about growing and scaling the company. Really taking us to those next levels where you know we're able to do additional raises were able to do maybe potential exits or something else in the future as well. But for me, it's about ticking and growing scaling teams in the same one that I would models are algorithms. So how do we have to take an opportunity or a whole new industry or a whole new market and be able to understand that, consume it and then be able to scale? That thing has as big and as quickly as possible. And so from a certificate perspective, I'd love to find one that specific around taking over entire markets or really trying to, um, you know, scale and grow haven't seen anything like that yet, but but that's really kind of the core area for both skills and experience, where I wanna be able to say OK, look, you know we're great in the mortgage industry, but as we scale and expand into a new market, how do we go in and make sure that we're meeting all the needs of the underwriters for that market or the chief risk officer for that market. How do we change the landscape around identity? Really, it's these market shifting, our market changing type experiences that I want to really be able to cane and capitalize on.

What are the various starting positions and salaries in your domain? What are the typical career paths after these starting positions?

Based on experience at: VP Data Science at Finicity, Finicity
Summarized By: Jeff Musk on Mon Dec 02 2019
Unfortunately, it's kind of all over the place. I know a lot of people that that work for what I would consider a pretty minimal salary, oftentimes, even below the average salaries for their particular states. Often times this is because somebody is transitioning out of school and they're trying to get the core experience that they want. And so it's almost like a paid internship, even though unfortunately, people can kind of get stuck in that mindset. Or in that mentality, On the flip side, I know of multiple people that have made well over a $1,000,000 even over a 1,000,000 1/2 dollars their first-year salary. So it's completely one into the when we talk about core Utah salaries. Typically, you are talking about a six-figure type salary, or even up to $200,000 a year with little experience. But you either need to have the right core skills from school or from the software development side of the world and be able to apply those to the machine, learning deep learning and really be able to understand the process as much as possible from beginning then and really in that date engineering and really being able to deploy a model this well, so typical career pass data science. That one point was all considered one thing, and now it's really moved all the way across all things. Data, Really. And so starting positions may be a data scientist or a data analyst type role, a business analyst or business intelligence type role. Often times you're going to see data engineering or database development or database administration as a potential role. Um, and then there are many other positions there as well. That all kind of function is that analyst or is that engineer that's really going to work with the data, manipulate the data or get into the modeling to try to understand either from the stats or from a machine learning a deep learning type perspective? What's going on within this data? And how do we start scaling that in automating? Um, I see in the future with people like Andrei party and honoring and others talking about software to 0.0. R. Or a situation where we can start coating on models and really the weights behind them to be able to create covered itself, Um, and So really, we start shifting to a point where models are packages on libraries and the relatively easy to deploy if you know how to do core software development in court coating. But you were able to understand why that model might be able to help create something else, whether it's code or whether it's an actual decision that you're trying to make or some other core function. really, from that descriptive to predictive, too prescriptive type process as well.

How did the program prepare you for your career? Think about faculty, resources, alumni, exposure & networking. What were the best parts?

Based on experience at: University of Utah
Summarized By: Jeff Musk on Mon Dec 02 2019
I'm so been part of the MSBA on and before that, the MSIS program on the board there for six years now, I think for five and 1/2 years or so and so getting to know, a lot of the core people they're getting to know people like you and others has been really helpful. Oftentimes just from knowing and understanding, like what's going on not only within academia, but within industry. At the same time, on men working with a lot of the students as well as people has have graduated and have kind of worked and gone beyond. Many of the alumni have either work directly with me, or we worked together from, like, a consulting type perspective as well. And so that's been super helpful. From a faculty perspective, many of the faculty have actually also done consulting projects with me on. We've also helped build curriculum or we've helped work to trade about, understand what's going on within the business and all the takes within data science within information systems and big data type technologies on. So all of those were super helpful. I would say that obviously the classes on some of the things that I learned in class or are beneficial. But the most beneficial thing really for me was was getting out of understanding. What are all of the other people doing? And so it was kind of out of class time. That was really the most helpful overall.