Hi and welcome to data futurology the podcast where we speak to top data science leaders out there in the field to get their lessons learned their strategies and approaches to help you take your career to the next level. My name is Felipe Flores. I am your host. Thank you so much for tuning in today. We are speaking with Angela Wilkins. She is the founder and managing director at Mercury data science. Angela also works at Mercury. Fund as Chief scientist, so she tells us about how that dual role works. And as you're here, I think she has the coolest job working with the type of companies that she does and the setup really really exciting. She has a strong background in clinical research and has ended up running a data science company. It's a great episode. I hope you enjoy it. Hi, this is Felipe today. I'm speaking with Angela Angela. Thank you so much for me. It is at the beginning of the episode. I wanted to ask him. How did you get into Data? What was it that po'trait? I always wanted to be a scientist as a kid. The only thing I ever wanted to be was a scientist and I'm particular I of course looked Albert Einstein and so that meant I like physics a lot and but it's like high school. I miss the math team the physics team and I was very good at it and that kept and going until my undergraduate. We're at that point. I decided I wanted to be a theoretical physicist. I went off to grad school and I realized pretty quickly that we don't set there at chalkboards anymore and to bribe equation that I actually have to program. So I learned to program for the first time in grad school actually, technically I took the Java class during my undergraduate, but the time Java was actually a new language my first language I'd say I really learned was Fortran 77 because that's the language that everybody uses a quantum chemistry and I took Programming I loved it. I loved physics. But I really liked I think when I got out of those, I really just solving problems and by time I finish grad school. I wanted new problems and instead of continuing and physics. I decided to go to computational biology. It's a really rich and interesting field because they have so many problems and ten years ago. When I kind of got started there really wasn't a whole lot of math to solve these problems and they were starting to head in that direction. So I first started out studying Evolution and evolution is actually a lot is very useful it. A lot of people may not realize this but it really helps us understand us. And again that's in the context of disease and in drugs and it allowed me to kind of understand the field as a whole and I over 10 years. I just kept in jumping from Project to project learning new data learning to questions. I loved it. Wow, that's so obsessed and what type of problems in water garden Working at what time so I'm spending my time doing protein engineering. So that's what it basically I would learn how to understand these things that we call proteins which are really long sleep sequences of information looks a bit of information. They fluctuate a little bit from species to species and if you look across a lot of speed, please you start being able to infer something about how this protein came about and once you kind of understand how it came about you can use this information. Ation to re-engineer it to do new things. So that was one of the things I did was basically created new proteins that didn't exist before and in some cases adjusted the function of proteins to make them do things that they didn't used to do what that blows my mind. Yeah. Okay. So you hear a lot nowadays about aging and aging is clearly a problem. We will be dealing with for the next couple of years that we want to get control of this better to prevent things like Alzheimer's and Parkinson's and he'll stay beautiful and keep our hair will be awesome. One of the things we work with the lab out of Dallas where they had figured out exactly that this protein was important in some kind of pathway, but they wanted to be smaller protein based on the original protein. And so I did a lot of analysis looking at how this protein behaves across a lots of species and said, okay. This is the important part of the protein. So this is the part we need to keep but the problem is that that part of the protein it's not going to function the way we wanted to it's going to do this thing. We called aggregate which means basically it's going to kind of fall apart and get eaten up. So we need to make it more stable. And so then basically took that 14 and change the cup just a couple things in it to make it more stable and it turned out it works really well. I basically now it's become one of the standard controls for understanding a pathway called is tapa G called attached becklund peptide, but it's actually a tool everybody in biology use day-to-day. Test so that they can understand how this pathway works. I think that was kind of how I started and like it so it's like I'm so used to using like any kind of data is try to like solve a problem Ayala cheese a large field because like we have like along with having these sequences of information. We have a language and a couple years later. I started working with which at the time was a newer thing IBM Watson at the time. They had just done the Jeopardy thing and they were looking lots of places to do proof of Concepts. The question we kind of posed was can we you use this technology. They had developed to read the biomedical literature and do discovery on it and start out as kind of R&D project. We're on about a year or as cancer biologist clinicians data Sciences Computer Sciences computational biology is a little bit of everything. So like a huge effort with about 20 people from Baylor College of Medicine and IBM and I for the first time was part of like developing a product noise science, but I'd never had done in science in a way that you know the end they work on the Read something that people use every day. They now call. This is what we worked on the IBM Watson for drug Discovery and it's still around people use it at like Pfizer for doing Discovery in the literature and trying to identify biomarkers or maybe drugs that would be useful for diseases and how would it do that? It's actually would look for relationships in the literature first. We had to kind of Define where the things we care about in this case protein drugs and diseases and then we look to see how proteins Drugs and diseases in Iraq, and so it might be that this drug inhibits these proteins or these drugs are good for these diseases and then basically creating a huge knowledge Network how everything interacts and then we use this network to identify the relationships that maybe people have messed because you don't have the ability to look at everything together. And what did your role of my drink now? My role is interesting because for the first time I would say I took a very a lead role in like a Large situation. I was kind of the team leader on the Baylor College of Medicine topside and I had the opportunity to basically kind of push postdocs and students kind of in the right direction the same time. I was the interface with IBM trying to make sure everything was going on at the right direction and going in a direction that makes sense for the biomedical field instead of just like representing a small facet. I was basically representing something larger than a small piece of science, but trying to build something that Would help many scientists exactly and how did you deal with the priorities and setting direction for the team and we say dealing with the people externally at IBM? What did you learn through that time? Oh gosh was the first time ever had to like set expectations working with Scientists working with biologists and clinicians are certain things that they really care about and trying to explain to them. It's not that we don't care about what you care about, but we need to prioritize and put this stuff in. Order and we'll work on these things first and then these parking second and then we'll do things will be out next quarter. It was the first time I ever had to think about that because usually in science. We're all pretty focused on solving our little problem. You don't really necessarily have to break things down and try to work with like 10 people and explain to them. We're going to get to it. It's phase 3. Yes. So that was our bond within your team always that externally as well as publicize their strategy. Yes. Exactly. It was working with in online. Ted came out working with IBM that was interesting because it was also the first time I had worked with people who referred to other people as clients. It was a very interesting experience, but I kind of like watch them like you just think about everything much different than we do and now looking back. It's like I Now understand their process they use a lot of language that now that I realized was basically to just allow them to communicate easier. This definition of a client is very meaningful that they have somebody that they kind of put on a pedestal You're going to try to create something that will help them and by referring that to us as the client. I thought it was initially weird, but now I understand it interesting and have any other challenges that you had to overcome during your time at Baylor. I think we'll my favorite projects was towards the end where I was working with DARPA initially. We had gotten some seed funding to work on a few projects with DARPA and we were part of a group that they called simplex. I'm really like the doctoral programs are very interesting in the context that they really are trying to build something that is when they bring people together. It's very strategic. They're putting five to ten sets of people in the room together and hoping that something organic and Innovative will come out of it and it was a really unique opportunity because it was like, I got to work with data scientists from Harvard and Stanford and but also they had a mixture of companies some of them are startup companies that were really doing Stuff around understanding language. In other cases. They were working in Material Science and trying to design new materials trying to do o we have top of the line 3D printing trying to create materials that hasn't occurred to anybody to make before that was really unique chance because I had not realized how the people in robotics can use the same algorithms as people getting Material Science really like what one of the things I got out of the darker project is that everybody is using algorithms? Rhythms that can help each other. There's people doing things in robotics in our case. We were doing biology Material Science 3D printing and we share with each other for me what I had learned a lot of the network theory is useful across many fields. I recall working with Michael Robinson. He's out of American University. He's an applied mathematician and like in collaboration with him a we came up with this algorithm for aligning protein. Across multiple species, we understand something about humans and we can represent it with this and then we also understand something about yeast that we want to represent this network and we worked on this idea of how do we align this information so that we can learn more because you don't know ever know anything about a species, but if you look at everything at the same time, maybe you'll learn something new now, the algorithm that we worked on was actually really useful to him in a different context. Hey were used it for tracking cars and So you can imagine you have a car going into a tunnel and then you want to pick up that car again after it leaves the tunnel and what he found that he could use this algorithm to do that. But what he would do is create a network based on the structure of the car and when it comes out of the tunnel, it has a similar but different network so you can think that it's network has kind of shifted it to a slightly different angle and this same algorithm could be used to do the exact same thing interesting. I love this stuff. So Yeah, it's raising and you are in danger for quite a while. And then why did you decide to move elsewhere? I think a lot of it was I needed new problems. I did a lot of different things in biology in it about the same time. I left I made a good friend who is at this thing policy Think Tank called the center of science law really great data son named Pablo and I got kind of infatuated with the problems that he works on right after Baylor. I did it. A short stint with his nonprofit where we were creating large data sets to better understand crime, but it's a lot of projects. I work on were trying to better understand the patterns that correlate with repeat offenders ahead to projects where one was the team have created a tablet game that recreated various tasks that they give people to assess things like Risk and empathy in the kind of Behavioral area. But they created it for the gamified it and we have this behavioral data of people playing games and it was something that they would take in and when they came in to do their parole interview or something like that and which track what would happen to them before and after they took the game and we were using see which of the behaviors most correlated with people who ended up a reoffending. Okay. Well where there's basically one of the big processes which is probably designing the test set that was very much a challenge because I think the time we had 600 to 700 pieces. We actually had 600 to 700 pieces of data. They are thing is we don't know if they committed a crime again. Yeah, we know that they they got caught for committing a crime. So it's like carefully designing the experiment in the training set that development set and the test set but the in the day you had to really think about lower my expectations here how right am I ever going to be here? Because it's clearly never something to be like 95% and the in the day we did make a power and it's something like you know 70% .7 area of the curve but it was a good method I would say for scoring things but it wasn't I don't think it was ever good enough to really be used and production okay and what type of improvements to get it I think we would have needed ten times the number of people Tested and at the same time. I actually looking back on it. I don't think it should have been test used as something to test pervy offense. The probably the data science questions should have been asked is are there things that can be done for intervention that trying to better connect these test results with things that can actually help people know it's like do they need help with anger issues or risky Behavior or empathy? It's I don't think move. Afford it should have been looked at it differently. Yes, really interesting. And that was one of the projects that you did before. Yeah, that's one of the things I worked on for a period of time the center of science a lot, but I like it there but like about the time I kind of just became fascinated with using data science to make policy. I quickly kind of realized. It wasn't really the thing I wanted to do forever. I ended up meeting a VC at the time. I didn't really know what to BC was. I had to look it up and I found out they were called a venture capitalist and that they invest in companies we would meet for lunch everyone once a long key to ask what I'm working on and I would On our side up what I thought of the latest algorithms or the latest company or what's going on in the biomedical field and one day he asked me. Hey, do you want to help me out with a few things and it turned into helping out a little bit with some due diligence coming in giving some seminars to his team on machine learning and data science and telling them a little bit about the you know, the latest technology. I enjoyed it immensely, but look at the world very differently. It really is. Is about finding things that will work they will work quickly and they will have a lot of impact and it was a new way of looking at the world it but I hadn't, you know in Academia. You can take your time a little bit you write a grant you care about six months later and you're like, oh I got my grind. Okay. So this is what I'm going to work on the next couple of years. The venture capitalist is the early stage startup companies work much more differently. They work very fast and they're trying to have as much impact as they Look, like I ended up basically transitioning going from helping with the seminars to due diligence to actually talking with companies that the Venture capitalists by the way. The venture capitalist is actually currently restart that Albert. Okay, the DC I was working with is a group out of Houston, Texas Mercury fun. There was I think one of the larger Reese's in Texas with not they typically invest with companies and around the around and not let me start that over again. I'm doing everything weird. I'm sorry for like I think you're doing really well. Yeah. Thank you. Okay, so I continued to work with this venture capitalist and I went from helping with the due diligence in the seminars to actually starting with talking to date companies that they find in the talking with the companies that they were going they were concerned upon and it was really interesting hearing about the problems one thing it kind of stood out to me was after being a scientist for 15 years. I did actually no to how to help them with their problems. I did not initially expect this, you know, because either they're In business, right? So they have business problems, but algorithms are useful everywhere and like they would tell me they're trying to understand they're trying to identify anomalies in the behavior of their utilities that I'd be like. Oh I know how to identify anomalies, but I turned out to not just be kind of good at it, but I actually really enjoyed it and at that point I kind of started to transition full-time out of Baylor out of the other day of policy and shifted towards this. World and it really like it started as just me helping AVC with their companies. We started a little company so that I could do that and it's been going for about a year and a half now amazing seminars. We want to know what they want to know what deep learning was they wanted to know what the next big things were on some level. I think I think there's a hope that there would be like, you know, you can learn everything on a couple of hours. and I think one of the things that came out of it is they realized it'd be easier if they just caught him fired me you just either it goes on and on and on I remember going through the process of teaching everybody regression and you started out linear regression going into Ridge and lasso and elastic laughter like guess basically everybody's just sitting there going like yeah, there's a lot here because I wanted to understand the details, but it's like you spend 15 years for the details and it's a complex field, correct and if you Huge and it changes so fast so fast. It is insane. That's really to thing. And then when you started looking at companies with them, I guess how were you involved in the process and what type of things were you looking at? I think a lot of times one case. I was helping them with their business model the area that they wanted. I was very familiar with the area that they were in the biomedical area and if they had all the data that they said they could have what could they do with it? And I helped advise on Next steps because really the first phase of their company was let's collect the data and now it's like well, okay. So now we have all data where we going to do with it. And that's kind of where I was helping was with the data vision for the future steps. It's a lot of fun because like what they pump you'd have someone come to you and be like, okay. So this is what I want to do and this is the data I have and you're like, okay, let's like for this but in trying to help develop the vision of how going to take what they have and make the thing that They want it's a lot of fun. I mean usually you pay have to go get some more data and you got to be some creative but it's a very rarely have come out of those things going. No. No. No, you can't do this. There's always like okay. This is going to be easy. But if we do this in this we can totally make this product. Yes, that's the work that you're doing directly with the starters evasive. Yes, and I would say that that actually turned into starting a company with my And my partner is actually Dan Watkins who is the victim of the beasties very fun. And basic part of this company started like we just kind of me running around talking to companies and now there's like 15 of us. That is incredible. I know it's awesome, but it went from basically helping people on the phone to now running around and helping not just for free phone companies, but helping all kinds of companies with their problems. Oh really great and still focus on startups or across the board. Typically a company that we're working with either just got funded for about they're usually Venture Venture funded companies. So they may have raised somewhere between 5 million and a hundred million and maybe for the first time it's like we have let's do this. And what we usually do is we kind of come in and help the companies get started. It could be coming in developing the strategy around the potential product that they want to build. It could be just going back to Dean fixing databases and bring some of the raw data engineering but even more It's like one of the things we do is we really do partner with the companies to make the product that will be the data scientist. And I'd say there's several companies that we act as a data science team for them. Yes, that makes sense. Are there any examples that you could share a run how you work with the company and how the data science carried in their organization? I think that definitely is when we talk about your friend hike has any I know I can talk about trying five try. Kite is a company that does machine learning and data science around be our data. So if you look up trying to type you basically put the right data in front of the pr person someone's talked about your company today. It was in a positive or negative way or something like that and they push that information there every day. They basically scrape the internet for blogs articles anything a public relations person would want to know is being written about their company this you kick. Up six million you articles every day. So it's quite a prop one of the things that we did early on to help them was when they're getting these six million articles. They don't actually know what they're about. So we designed a set of algorithms to basically Define what they're about. Is it about technology it's about biotechnology. Is it about car automation but one of the challenges was to make an amine topic modeling it's a very common problem, but there are unique challenge was the vastness of the Problem this point it's not you know, we're going to do these small touch sets of topics. We had to figure out all the topics that all their customers work care about and then kind of creating a structure around that and then somehow quickly annotating 6 million articles for me. It was a very very large big data problem because we had to be very clever because we couldn't use these tender way you could go about this problem because it would take too long and it wouldn't cover all the vertices it wouldn't cover unnecessarily Oliver. One of the things that bringing in this it's like we had the opportunity to basically teach their team everything that we learned in the process. We didn't just product and leaf. We worked very closely with their team. So their team understood what we were doing and why we are doing things and so when we leave they can take it over that's amazing. And how was this company doing their work before you guys were in this case you're relying on technology that there were licensing. That was the only picked up like a cup. Hundred key topics, and we were able to start picking up hundreds of thousands of key topics for them. It was very helpful because it was an example of like them actually creating a novel machine learning technology. That was for them. That is so interesting and then once you work with them originally and after that, do you either way typically we continue the relationship with them in this case Trend kite actually was sold. To station a couple of months ago. So we have some okay that's interesting ending had an example of a company where that a science was not part of their core offering before you work with them and that you had to help them include data science into their strategic plan. Yes, we do have examples of that in one case. We've worked with a company that actually was developing novel cancer Therapeutics and so really they're experimental biology. Platform what they do is they have lots of interesting discoveries that could be used in all kinds of different cancers, but it's really hard to tell which Jesus will enter staying than other and so we did was built and natural language processing pipeline that also couples some, you know, database sources to give you immediately tell you how interesting your prediction is and you know and by interesting I mean, you know, it's like okay. It's been already been associated with cancer. Maybe this is already a well-known drug. Agate or someone is actually ordered patented has already done lots of patterns around this technology. We've already done several patents around this therapeutic. It may not be dismayed make it more interesting and make it less interesting but it's something that they didn't have beforehand at all. Yes, that's not something they had for the other is that doesn't that space where they didn't have data science before because they were not using their designs at all that they were basically, okay. I'm sorry cause I think the quiet answer the Right 35, but it makes there were no this must be fascinating to be working with PC and doing they're helping their companies. Be more successful data science. Amazing. How did you feel going from biomass into business? How was that process for you? As you said like realizing that you could and that was my far away from business. How's that going from Academia industry was huge jump. I was funny because like I think of her Liana started in physics and I wasn't really feeling the impact. I wanted to feel you know, it's like in physics Everything feels so far away, you know, it's like maybe this will be useful in 20 years. Maybe it'll be useful in 30 years in the biomedical field. I went there thinking. Okay. So human health. I'm going to have impact here. And even though I do feel like there's a couple things I've done that was helpful, but I didn't ever feel the As an academic did not reach the rest of the World by impact how I work his students and how I worked with postdocs and how you know, I helped put better scientist in the world going in straight. It's the first time I really felt like I do something and it really helps not just a few people it helps lots of people because if this works, this is a cure for cancer if this works more people eat, you know, if this works this makes a lot of other people's lives easier, it's I would be glad I made this transition. Yeah, yeah, that's her. Did you ever see yourself? Besides, no, not at all never saw myself writing it. The funny thing is I think twice before I had funds during my academic days suggest to me and hey, let's go start a company together and I was like, yeah, I don't know why you'd want to do get it and I think it was basically meeting my current partner Dan and he's just one of the smartest people I've ever met and I think having this wonderful collaboration and ship we're getting the opportunity to see what his world is like and how to use what he's learned and what I've learned to make this thing. I want to call impact for other people has I think it's the only way I would have ever done it is I get to have the best of both worlds. I get to have all the experience that he has and he has all this experience as a VC and as a business person and I get to merge it with my you know, science and academic background to kind of create this new entity and it's a lot of That's great. And once the future or rectory Tennyson. I think in the next week or two were raising money basically allow us to scale up and build out the team a bit more. I am expecting us to grow bigger and get to help, you know work on new problems and help new people and can help me companies and I expect us to kind of escalate a little bit. I am so excited for you. So great. And what type of I think we have a love for biotech. There's several companies in that area. We like Comes around human behavior. I think what are you buying patterns or just I think try and kites it also a good example, like what compared to the things that we care about and making those making that data more accessible. I like those kind of problems. We started to expand into manufacturing. We really enjoy those. I guess it's a new type of data It's Time series data is very interesting and you have the ability to really change people's processes, but that type of data. I don't think we're really limited by By limited by a certain industry at this point at this point we've worked in about eight different Industries and it's really just a matter of coming at the problem correctly and working very close with the company that needs help amazing. Let's change tack to some of the questions that are more rapid fire questions. What are you most excited about in this field? Why do you love it? I love it because it's changing so much every day is different. I think even if you read a science at a certain company every day, It's still the same technology is changing so much you were having a lot of access to better technology every day. And we're able to solve problems that five years ago getting feel possible. I love be having the opportunity to work with lots of different data scientist specially the ones that very different than me because I get to learn from them. I'm sure it's like this a lot of different areas, but I feel like we're kind of in the middle of like almost making it up as we go along and so we have to basically it Really does have to be a collaborative effort. We need to checks and balances. We need each other to make sure we're doing the right thing by, you know, everybody else so interesting and what Problems and firstly proud of the fact that I as this company that we that I started with my partner's gone in as far as it has it's nice getting to work with like 15 people who really like their job and want to come to work every day. I am really proud of the fact that like, there's people who depend on me to pay their bills, I think for a lot of the over the last year there's three people on my team who have bought houses and the idea that it's their hard work. Can they could go anywhere and find a job and do it themselves, but I get to be part of that. I'm really proud of that. I know that's not data science related, but it's not. Only the direct people to work with you but also their families and their future really proud of the fact that we also mean it's right. Now when people hire data scientist we take on a lot of people that you would consider raw, you know, they just got a grad school will not necessarily got a PhD in machine learning. We have the potential to be a data scientist. And so we've created an environment that lets people that challenges people allows them to grow that's fantastic and with that. By chance, I like it big was by chance, but I think it's still left over from my academic life. I really enjoy being a teacher. I really like having students. I liked being part of people's research and part of their science their transition to being a scientist. I think that will never go away that I'll always want to be part of somebody's Journey helping them be who they want to be. It's really nice. Tell me what challenges or problems. One of the challenges that we're dealing with Now setting expectations. It's so important to the data science world trying to explain to somebody that you're not always sure how long this will take. This is where we would start but once we get into the data Maybe we don't have that have is not enough data how much maybe we have too much data. You don't really know until you start and then why an estimate I'm trying to think about how to explain this to people in a way that they really a that they understand but be You know, I explained it to them where I want to be careful about making sure that they under your nose. I said these expectations right? So sometimes they have a nice people have an idea that something will take two weeks, but it actually takes three months but it's very challenging to get those points across. Yes, that's page tips or techniques do this more effectively. I remember being repetitive that yes, I have to say it over and over and over again. I think that's why. Probably the most important one being honest just continually just say exactly what is on your mind about sugar-coated think there's this tendency to you know, tell people what they want to hear and people happy and in the data Science World, you have to be really careful because sometimes something turns out to be easy. Sometimes it turns out to be really hard and that's like setting those expectations around accuracy. Is it? Okay if this is 90% correct, or do you need to be 98% I don't think we have patience. Because there's a lot of things that just because you can do it doesn't mean it works. Well almost anything is possible, but does it really help you if it's only accurate 75% of the time good action point it depends what you're doing and how do you A lot of times they get very nervous because you can't give them a answer that says I'm a hundred percent sure. We'll be done in a month 95 for that. We're going to be done in a month and they're like, well what happened to the other five percent and you're like, well, it could take three months that makes them nervous. It makes them worried and it's all the seems a lot. It's hard to trust the black box. But at the same time, you know, you just probably try to be straight and honest and tell them exactly what I think will happen and how let us all cross our fingers together. Very true. - has their future challenges in our space. You are a lot about what our challenges are going to be and it's like recruiting talent. I actually think we're to creating Talent is going to get easier. I think training Talent. We just need to come up with a better system there. I feel like a lot of things that we're going to do is just easier and easier because I mean want some days AWS just feels like magic and all of the things that I could do that could never do five years. I mean, it's like I feel so lucky. See we trying to do this at this time. What do people usually say their challenges are because I feel really lucky to be at where we're at. I feel like I'm better and better. Yeah, I completely agree. It is. All we have is a lot of things that were doing or actually because like there's so many algorithms now and like they have this realization at some point that these five things are actually the same thing. We have are pretty lucky. Yeah, so like Angela this has been amazing. I only have one last question for you. And that is what is the takeaway that you would like to leave the audience with or a piece of advice you to share with them. I would say do as many things as you can I feel like every time I get introduced to a node obeying or work for a new company or work with a new type of data. I'm better for it. I'm better at my job. I am more helpful to other people. I think I not too. To get stuck at the same thing do as many things as you can as many many problems as many domains as many types of analysis and many algorithms. There's so much to learn to go for the variety. Yeah, I would be an expert on as many things as you can because every time I enter a new domain or subdomain I get something out of it that helps me somewhere else. You know, it's like I working on buying patterns helps me in biotech biotech helps me in manufacturing and Manufacturing helps me it all relates to each other and it helped me better understand real estate. All these things are opportunities. There are all different ways to think and they the more you know, you really can figure out problems quicker. That is also is a fantastic and Angela. Thank you so much for your time. Share your journey toward Lessons Learned your perspective. It's been fantastic. Thank you so much. Thank you so much for inviting me. Of course. This has been great. I wanted to tell you about the rmit online masters of data science strategy and Leadership. I was one of the industry advisors for this program. It's an online Master's program and it covers both a design strategy and Leadership and it has also a technical component highly highly recommended for people wanting to get ahead with the program. You can gain this Advanced strategic leadership and data science capabilities required to influence executive leadership teams and deliver. ization wide solutions for more information visit online at rmit dot edu a you that brings this episode to conclusion. Thank you so much for listening. Please find us on data futurology.com or on Facebook Twitter LinkedIn or Instagram as that of utility. Also go to data futurology.com forward size podcast to find the show notes for this and any other episodes. If you liked this episode, it would mean a lot to us. If you could leave us a review wherever you listen to our podcast. 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Angela Wilkins is the founder and Chief Scientist at Mercury Data Science (MDS) where she works with Mercury's portfolio companies to identify solutions for complex data problems. Prior to MDS, Angela was a member of faculty research at Baylor College of Medicine and led projects at the policy think tank, Center of Science and Law. She developed her machine learning knowledge in the biomedical field as part of IBM's Watson AI and DARPA Simplex Project. Angela received her M.S and Ph.D. from Lehigh University, all in Theoretical Physics. Enjoy the show! We speak about:   [01:10] How Angela started in the data space   [03:45] Using proteins to understand aging    [07:35] Setting team expectations    [09:15] Challenges at Baylor    [15:15] Using data science to make policy    [18:30] Leading seminars on machine learning and data science    [22:05] TrendKite – putting the right data in front of the PR person    [26:40] The transition from academia into business    [30:15] Rapid fire questions    [35:00] Future data science challenges    [36:10] Do as many things as you can   Resources: Angela’s LinkedIn: https://www.linkedin.com/in/adwilkins Mercury Fund: https://mercuryfund.com TrendKite: https://www.trendkite.com  Quotes:    “We look to see how proteins, drugs, and diseases interact.”    “We came up with an algorithm for aligning protein networks along multiple species.”    “I left Baylor because I needed new problems.”    “Algorithms are useful everywhere.”   Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information  And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Enjoy the show! 