Wealthsimple is backed by a team of world-class financial experts and some of Silicon Valley's best technology talent. I want to say the most recent one because that one is the freshest in my memory. This is not just machine learning, but data engineering. That's the entire data science that's for your whole company. But I also think Wealthsimple having diversified product offerings certainly makes it more resilient to unexpected changes in the financial world. ) Data Science and Engineering. Does your team spend a lot of time keeping up with the latest developments in the field, such as reading deep learning papers? ) You know, we oversee the data warehouse, and we monitor like the BI tool as well. Data blending can fill an essential role in comparing information from different formats and databases. Leonard Lindle: (10:37) I know it's not your project, but do you know if you leveraged any open-source libraries or anything else to build on top of it? Simple, right? Client Success; Core Operations; Finance; Legal/Compliance; People Operations; Internships / Co-ops. Before we begin developing a model to do the necessary explorations - and a lot of this happens inside like a Jupiter notebook - and to do the tests, we need to ensure that this is something that is feasible and that this model will be useful in the hopes of reducing the times that we do have to abandon the model. Mandy Gu: (29:53) That's an excellent question. I've been there for almost a year and a half now. When I first graduated and when I was having like dilemmas of which job or which career path to choose, like having chosen the path that kind of enabled me to learn the most - I've personally found that to have helped me a lot like today. Leonard Lindle: (20:28) Another question from the audience: where have you applied machine learning models? Actually, some programs do have a co-op requirement, but for mine and a lot of others, it's optional. My last co-op was at a Toronto company called Nulogy, and they did software for contract packagers. I got thrilled to see that because a good portion of my money is with Wealthsimple. We talked a little bit about your parser and Airflow. We try to mock a lot of things. ... such as the computer-science pioneer Jean Sammet — produced a language, much like FLOW-MATIC, that was easy on the eyes. Today, we have somebody who has data outside of Salesforce, Mandy Gu. Wealthsimple is one of the biggest robo advisors in Canada – but is it one of the best? You said there were a couple of things that your team has built that help with the SQL and the Airflow. Are you happy at five, or do you think you're looking for other people to tackle other company challenges? ) So one very standard check is the testing performance on the last 60 days of observed data. There's the understanding that if there's anything they don't know that they can pick it up on the job. ) We could see such a massive success with this machine learning model, and it was a pretty obvious decision to kind of rework the product. I got thrilled to see that because a good portion of my money is with Wealthsimple. ) No credit card required. Salaries posted anonymously by Wealthsimple employees. As of August 2019, the firm holds over C$5 billion in assets under management. Questwealth Portfolios vs Wealthsimple: How it works? Headquarters. Leonard Lindle: (38:33) So just out of curiosity about Wealthsimple - is there machine learning or some kind of insight applied to the client about what sort of investment products would be right for them does your team have anything to do with that? I would imagine that what you're trying to do is small incremental improvements to the user experience rather than pushing out substantial changes. Got another audience question here. So we use TensorFlow serving to serve them. I think one thing about working here is there's never a shortage of projects. One of our data scientists is great with this kind of stuff—he kind of runs our experiments. In this engaging webinar, Mandy Gu of the online investment management firm Wealthsimple takes listeners inside the data technology of one of the financial world's most cutting-edge online outfits. We want to enforce good patterns. Do you do any of that? ) Mandy Gu: (16:25) So, the data team is mostly in Toronto. I did six co-ops while I was at Waterloo. My team's responsibility is more like loading that data into the data warehouse. We have tripwires around things like model performance. How does it work? This is not just machine learning, but data engineering. In terms of monitoring, tripwires are one of the things that we do use for monitoring. Then you have to run it back in through your pipeline to see if the experiment worked and all that. We believe in the idea that if we give smart people the right tools, they can do great things with it - and we definitely have a lot of very smart people here. This has been a lot of fun. Right, because you don't employ any drag and drop or simple-to-use ETL tools. Okay. Many people here can build their own dashboards and write their own SQL queries. I do think that our interview process is a little bit more abstracted and a little bit more detached from our day to day operations. Mandy Gu: (23:35) I don't remember the exact numbers, but we were able to see a huge lift in getting the transfer to the right place after implementing the model, as opposed to the client selection. I definitely think there are plans to continue to grow this team. So we did leverage a lot of those open-source frameworks out there. ) So I had the opportunity to get involved in the development and the production of the data products from the get-go. ) It's not just operational use cases, but we don't actually use it for analyzing financial data. Mandy Gu: (22:59) So one of the first models that I worked on when I first started was on an accelerated institutional transfer. There is never a shortage of projects, and there is a lot of really exciting work. I think it's gotten brought up that we should be looking at our existing machine learning models more critically. So does your dev environment include a decent-sized data warehouse that they can do load testing on? I'm not as familiar in that area. I would say that we don't read as many papers - at least not as part of the job. ) March 10, 2019. Leonard Lindle: (39:14) We're just about at the 45-minute mark here - do you have any last words or any more words of wisdom and advice for our audience? If there were issues with the data, they would most often fall into the engineering teams' domain and their stead. Wealthsimple has world-class financial experts and top-talent from Silicon Valley working for you. Wealthsimple builds a diversified portfolio of ETFs on the investors' behalf and guides them in achieving their financial goals. Our end clients touch on them, and many of the things we do are to try to provide that a better experience for them. Going back a second, you went to the University of Waterloo in Canada. Anyone can just go in, create their own tripwires, and indicate the cadence and the scheduled interval. Your ETL is SQL and Python period. So at Wealthsimple, we are huge on SQL - everyone on the company is. So in these cases, we pull data from a database, and we transform the data a bit, and we dump it into a CSV or in an FTP server somewhere. Wealthsimple is the smartest and easiest way for everyone to invest their savings. That does give us the confidence to develop faster. ) Sometimes there will be - for example - a client application, another service that receives a prediction, and they decide what to do with those predictions. To me, I think it takes a lot of time. Their responsibilities include monitoring the data pipelines, improving the data warehouse and maintaining API endpoints for serving model predictions. That's great. I have not worked too much on it. So just out of curiosity about Wealthsimple - is there machine learning or some kind of insight applied to the client about what sort of investment products would be right for them does your team have anything to do with that? ) Wealthsimple is backed by a team of world-class financial experts and the best technology talent. We're at about a half-hour now - so if anybody has any more questions, go ahead and throw them in the Q and A. It sounded like your product development and product analysts included some machine learning to try to make it easier for their customers to sign up for Wealthsimple and get their investment accounts into there. ) But I also think Wealthsimple having diversified product offerings certainly makes it more resilient to unexpected changes in the financial world. $5+ billion. It's mostly like my other very brilliant team members that did, but I have really benefited from it. I think because we've invested the time in the foundations, it allows us to deliver those projects reasonably quickly. Log in to Wealthsimple to grow your money like the world's most sophisticated investors. We have a pretty standard machine learning workflow getting set up, and a lot of that leverage is on Airflow as well. | Wealthsimple is investing on autopilot. The company had previously raised about $190 million, valuing it at $743 million, PitchBook data show. Our talented team of software engineers, designers, and data scientists have previously worked at … I think this is something we can get better with. Or do they have to know SQL? With such a dependency on SQL and Python, I'm curious about other tools like Alteryx or others that could provide a solution to bring on other talent who would be more in tune with the wealth management landscape versus heavy on the technical side. To add two numbers, for example, you could write “ ADD Num1, Num2 GIVING Result”. Leonard Lindle: (26:46) What are some of the most time-consuming parts of your data pipeline process? Mandy Gu: (17:28) I think one thing about working here is there's never a shortage of projects. Here's another one - was your favorite co-op experience? ) Mandy Gu: (37:23) Yeah, definitely - and it's things change very quickly here. What are some of the most time-consuming parts of your data pipeline process? I think that not having taken many programming courses in my undergrad, that definitely made it harder for me to get familiar with the software side. ) Mandy Gu: (34:38) I did six co-ops while I was at Waterloo. Mandy Gu: (14:05) They would have to know SQL and if they wanted to make a change to the fact they would have to make a pull request. I think that not having taken many programming courses in my undergrad, that definitely made it harder for me to get familiar with the software side. Do you do technical assessments or take-home assignments? Wealthsimple. So this service - which we've been calling SQL toolbelt - we integrate this into our development and testing framework for the data warehouse. Today, we have somebody who has data outside of Salesforce, Mandy Gu. Here's how they're doing it. Do you do technical assessments or take-home assignments? 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Is that a requirement that the tool belt must get used before somebody can push something into production? I guess a little bit about myself, first. They would have to know SQL and if they wanted to make a change to the fact they would have to make a pull request. They're changing very rapidly, and there are so many new tools to make things easier. Mandy Gu: (16:37) That's all over the place. Mandy Gu: (10:55) It uses the ANTLAR 4 grammar. So this has historically been a huge client pain point because of just how long it takes. Is it good enough? We work on a lot of new reporting and pipelines all the time. Toronto. For deployment, we use S3, usually, to store these model assets for our deep learning models - they're all on TensorFlow. Get Started. We work on a lot of new reporting and pipelines all the time. So we talked a little bit about the machine learning that your team does. A free inside look at Wealthsimple salary trends based on 70 salaries wages for 37 jobs at Wealthsimple. The value prop for Wealthsimple is that if you have all these investments everywhere, WealthSimple can gather them all in one place and make it easier for you to integrate those investments. And so when you have an operational model like that, is your team responsible for the live modeling that your application uses? ) We use tripwires to monitor data freshness and check that key expectations are getting met in upstream data sources. In this full-day assessment, we typically do a culture assessment. I think it's just a process of exposing yourself to more things and picking them up as you go. It's hard to say my favorite. It sounded like your product development and product analysts included some machine learning to try to make it easier for their customers to sign up for Wealthsimple and get their investment accounts into there. The point of these (for a lot of these facts tables, especially) is that we want to make it easier for our end-users to be able to get the information they need. ) We build the necessary pipelines to get this data into our data warehouse, we write these jobs, and we typically have one Airflow deck for one model. ) Leonard Lindle: (20:57) Then you have to run it back in through your pipeline to see if the experiment worked and all that. Thankfully, we've not yet encountered a case where we're in the middle of developing something and then realized that the model is not up to standard. It sounds like you have a really cool team. So I had the opportunity to get involved in the development and the production of the data products from the get-go. You might just say it's the future of the finance industry; Wealthsimple is gaining traction as the new, simple, and affordable way for almost anyone to start investing. I think we're looking for people who will embrace being a data generalist, someone who's willing to see this process through from end to end. When you're in this hiring process, do you find people that already know your toolsets that you're using, or do you need to have sort of a made-up exam with made-up examples that show their thinking process and their abilities outside of that? ) There is a change between what they're doing in dev as opposed to what's happening in production, but we do try to make it as similar of an experience as possible. Or do they have to know SQL? ) Here's a question: when developing models, when do you decide to abandon the effort if it's not giving you the performance you hoped for? Hello everyone! We talk to them and answer any questions they may have. That part gets sourced. Company profile page for Wealthsimple Inc including stock price, company news, press releases, executives, board members, and contact information Thanks for inviting me. What kind of a cadence are you running on in terms of putting models out? ) We often use the SQL to about functionality for things like schema rewrites, whenever there are upstream changes in the data columns or the data names. There's another question from the audience. Wealthsimple is staffed by a team of software engineers, designers and data scientists, out of prominent tech companies such as Amazon, Google and Apple. We try to keep up with things. One of the attendees asked if there is a way nontechnical members of the data team can push data into your data warehouse? Mandy Gu: (22:13) We are responsible for that as well. Because we have many these checks in place, we feel a bit more confident in making the cadence shorter. We do a lot of experimental design work. Wealthsimple started out as an investment platform, which provided this nice, really easy way of investing money. Take a deep dive into the tech stack of Wealthsimple with the full transcript! What kind of a cadence are you running on in terms of putting models out? What are your internal rules on that? Wealthsimple is authorised by the FCA and also covered by the FSCS, meaning your money will be protected up to a total of £85,000, and investments up to £50,000, in the case the company folds. So that's a little bit about me. Pre COVID, we went to a lot of conferences. If it is, we will upload a model asset somewhere, and from that location, this model asset would get picked up by our model server for people to use the latest version of the model. ) There's another question from the audience. Leonard Lindle: (35:48) How often do you have to update the financial aid data? Your trust is our top concern, so companies can't alter or remove reviews. I think that was one of the really nice things about Waterloo was getting that work experience. Actually, some programs do have a co-op requirement, but for mine and a lot of others, it's optional. We try to stay away from like flat files, but for a lot of internal reporting, that's the format that our stakeholders are most familiar with. It's been great speaking here and answering and engaging with the audience. ) We're confident that in our testing framework; if that passes, it means this is a really good state to go. So can you tell us a little bit more in detail about the data pipeline at Wealthsimple - how you ingest data from your platform, where you put it, and other things like that? I think some of our models are now on a weekly cadence, and we do collect - even between, for example, now and one week from now - we collect a sizeable amount of comparable data that we can use to like further strengthen and improve the model. I think one thing that I find really impressive about this team is that we're all multitasking. Data Scientist @ Wealthsimple Toronto, Canada Area 500+ connections. So there are definitely a lot of very interesting projects. We're all doing a bunch of things. We were fairly involved in each of the different domains at Wealthsimple and helped them with their analysis and sometimes helped them build their dashboards and their queries. It is part of our testing framework. Copyright © 2008–2020, Glassdoor, Inc. "Glassdoor" and logo are registered trademarks of Glassdoor, Inc. How would you calculate customer lifetime value for a company that is 5 years old. 2014. Leonard Lindle: (31:11) Do you have any lessons learned that you wanted to pass on to any other budding data scientists here in the audience? Do you have any lessons learned that you wanted to pass on to any other budding data scientists here in the audience? We give on-demand advice from real human beings. It also offers users the option to enable two-step verification. How do you hire? ) I have a major in statistics. What do you look for when you're hiring data scientists? They play an advisory role in Wealthsimple's investment management process and serve as a sounding board for Wealthsimple's … She also speaks about tips and tricks for building pipelines, Wealthsimple's BI tools, and when to abandon a model. We just walked anyone who wants to learn SQL kind of through the basics. ) There, I would say there needs to be some understanding of SQL to use this tool correctly. Probably not adjusting any models because we don't really have any models dependent on the data. The six of us report to the VP of data science and engineering. Join to Connect Wealthsimple. How often do you have to update the financial aid data? There, I would say there needs to be some understanding of SQL to use this tool correctly. We've learned that by building a model around successfully completed transfers, we can get a much higher success rate than if we let the clients - based on their own intuition - make certain selections about where this transfer should get sent. ) Do you have any tips or tricks on how to save time building your pipelines? We want to enforce good SQL practices. We've made tripwires really self-serve, and we've built them as a part of the Airflow webserver. I think one thing that I find really impressive about this team is that we're all multitasking. The company has raised $78 million in capital. Leonard Lindle: (18:06) Got another audience question here. Mandy is a data science scientist at Wealthsimple. Ideally, this would happen before we start developing it. Mandy Gu: (31:57) I would say try to learn as much as, as much as you can. Leonard Lindle: (34:15) Here's another one - was your favorite co-op experience? Thanks for inviting me. Leonard Lindle: (11:05) You didn't write your own parser from scratch. Cool, cool. We could see such a massive success with this machine learning model, and it was a pretty obvious decision to kind of rework the product. I think we're looking for people who will embrace being a data generalist, someone who's willing to see this process through from end to end. ) So it parses our SQL and looks for not just syntactic errors, but also enforces what we believe are good standards. I focused on backend API creation, maintenance, and enhancement, with an … Additionally, the company’s team of software engineers, designers, and data scientists are from companies like Amazon, Google, and Apple. Leonard Lindle: (25:55) When you're in this hiring process, do you find people that already know your toolsets that you're using, or do you need to have sort of a made-up exam with made-up examples that show their thinking process and their abilities outside of that? The firm was founded in September 2014 by Michael Katchen and is based in Toronto. Yeah, we do a lot of our machine learning models. Our mission is to help everyone achieve their financial goals by making investing simple, affordable, accessible and personalised. So do you guys work together in Toronto, or do you have remote in place? Are you happy with your move to the production workflow? Leonard Lindle: (29:34) Okay. Mandy Gu: (38:19) Yeah. $8 billion. Core Operations; Technical Teams. Wealthsimple is perfect for: Beginner investors; Anyone who’s comfortable managing their funds online; Socially responsible investors This deck would orchestrate, pulling the data from where it needs to get pulled from running the training script. We talk to them and answer any questions they may have. Instead of having clients make these decisions, we would actually use the models to make those decisions. If it goes through your pipe, through your QA checks, it's not going to break anything. They're changing very rapidly, and there are so many new tools to make things easier. If it goes through your pipe, through your QA checks, it's not going to break anything. Wealthsimple gets personal on the first screen of the account setup process. Do you do a lot of AB testing on your website? ) A lot of that gets orchestrated using Airflow. So we talked a little bit about the machine learning that your team does. Thanks, Leonard. These Data Scientists are responsible for building and maintaining the data infrastructure to support the rest of the company. Typically, though, we're responsible up until that point. I can say that we do a lot of experiments. ) We build additional facts on the dimensions table on top of this raw data that we extract and load from our sources. Mandy Gu: (13:21) Every model is different. We were fairly involved in each of the different domains at Wealthsimple and helped them with their analysis and sometimes helped them build their dashboards and their queries. ) I don't remember the exact numbers, but we were able to see a huge lift in getting the transfer to the right place after implementing the model, as opposed to the client selection. We definitely use a lot of Python and a lot of SQL. This has been a lot of fun. I think we're just trying to get a feel for how well they think and how well they problem-solve. I think it's four or five being the minimum and six being the maximum. Hello everyone! Mandy Gu: (15:58) We have five data scientists and a software engineer. We also have a series of checks that we enforce before deploying a new version of the model. Engineering; Product Management; Trust; Work type. Sometimes, it does happen, and we've seen it happen at different stages of the model life cycle. Mandy Gu: (30:30) Sometimes, it does happen, and we've seen it happen at different stages of the model life cycle. We use it a lot for our ad hoc analysis, but many people at Wealthsimple are very well-versed in SQL - so we have many people building their own dashboards using the tool. Then, we have other engineering teams responsible for maintaining those services. Actually, in an article released a while ago - with all of the volatility in the marketplace, the Wealthsimple portfolio was actually one of the ones that performed really well. Read our guide and learn what these concepts mean for your business. Are you going to be adjusting any models due to that? ) Leonard Lindle: (14:19) Right, because you don't employ any drag and drop or simple-to-use ETL tools. My opinion of Airflow is pretty positive. So they would have to know SQL and a little bit of Python to do that. So we did leverage a lot of those open-source frameworks out there. This initiative started at the company to run SQL bootcamp, where anyone could sign up and get weekly lessons and exercises. Leonard Lindle: (19:55) I think one of the other things a lot of companies do is write views for end-users. You can manage your accounts easily on your own through our website and app. This is another X-Force webinar, one of a series on data in Salesforce and data outside of Salesforce. Then, I put that into one of these tripwires, and everything else gets taken care of for me. Tags: Can tell us about a time when you think your machine learning really brought something helpful to the platform, the application, or your understanding of your client behavior? ) After the technical assessment, there's a full day onsite - with COVID now, it's a full day of Zoom meetings. I think what's really impressive, at least to me, is that this team is relatively small, but we can do a lot. ) So this has historically been a huge client pain point because of just how long it takes. Having that certainly makes testing a lot easier and also takes away the worry that they'll break something when they test. I think right now the process of deciding which investment is very much in the client's hands. So you must have a production move to production workflow before you can push that into your data warehouse as one does. So if you do n't read as many papers - at least not as part of really. Set up, and Toronto, or is that we built we developed internally been there for almost a now. Standard machine learning that your team, what would you like to tackle other company challenges?.... ( 29:01 ) we are responsible for the development and deployment of these products include a decent-sized data.. Data stack their financial goals of Toronto, on, Canada Area went a... 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From end to end Python and SQL does create a lot easier and also wealthsimple data scientist.

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