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Do you think you would want to add anything to it? I think it's just a process of exposing yourself to more things and picking them up as you go. Wealthsimple provides you with world-class, long-term investment management… To add two numbers, for example, you could write “ ADD Num1, Num2 GIVING Result”. Mandy Gu: (16:25) So, the data team is mostly in Toronto. We have tripwires around things like model performance. We do a lot of AB testing. 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. Is it just Excel or CSV dumps? 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 a digital investment service that uses technology to make investing simpler, smarter and low-cost. I think we've helped with the analysis, the analytics, and how that process can improve, but we don't actually use machine learning to make any recommendations in that aspect. ) I think because we've invested the time in the foundations, it allows us to deliver those projects reasonably quickly. ) This would get abstracted entirely from the process. ) Is there anything else that you do that you think is cool that you want to talk about? ) Glassdoor has millions of jobs plus salary information, company reviews, and interview questions from people on the inside making it easy to find a job that’s right for you. Can you tell us where you are without giving out any Wealthsimple secrets? Their responsibilities include monitoring the data pipelines, improving the data warehouse and maintaining API endpoints for serving model predictions. Mandy Gu: (39:42) I think that's about everything. It's been great speaking here and answering and engaging with the audience. ) How do you hire? Headquarters. When we talked earlier, you had a couple of machine learning projects that you're working on. Leonard Lindle: (37:44) How do you see Wealthsimple adjusting to the new, volatile financial market that we are seeing? With the BI tool, there's a nice get integration. We just walked anyone who wants to learn SQL kind of through the basics. We're always reiterating and seeing how we can make things better. Is it still in development? I personally do really like Airflow. That does give us the confidence to develop faster. ) As of August 2019, the firm holds over C$5 billion in assets under management. I want to say the most recent one because that one is the freshest in my memory. There are a lot of things we can do more to improve the client experience, but there's also a lot of work that can kind of get done on the foundations. Leonard Lindle: (18:06) Got another audience question here. So, we're pretty involved with a lot of the data processes. Now I work as a data scientist there, but data scientists and everyone else in the data platform at Wealthsimple, we kind of pride ourselves in being like generalists. My team's responsibility is more like loading that data into the data warehouse. This has been a lot of fun. Or did you write your own SQL parser from scratch? Our end clients touch on them, and many of the things we do are to try to provide that a better experience for them. At Wealthsimple, we have a few hundred people, and many people at the company are very well versed in SQL. I can say that we do a lot of experiments. ) So in terms of our data sources, we have a bunch of internal microservices, and we also have other integrations. It is part of our testing framework. I think right now the process of deciding which investment is very much in the client's hands. We try to keep up with things. For deployment, we use S3, usually, to store these model assets for our deep learning models - they're all on TensorFlow. This is not just machine learning, but data engineering. 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. ) Are you going to be adjusting any models due to that? ) So they get treated like an operational service. For example, the payload - we pass them to predictions to make sure they're intact, and that performance is up to our standards and promises as well. ) I can say that we do a lot of experiments. To me, I think it takes a lot of time. Our Investment Advisory Committee are recognized thought leaders in the investment community. When we were talking earlier, you said you also created some kind of a SQL - I would call it a code analyzer, but you can tell me what you call it. Simple, right? Wealthsimple started out as an investment platform, which provided this nice, really easy way of investing money. Maybe you can say a little more about your BI tool and how people would use it if they're not SQL or Python programmers. Report this profile Articles by Morten Marketing Channel Attribution Modelling with Markov Chains in Python By Morten Hegewald. 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. -Wealthsimple has extremely lofty ambitions, but because of the sheer talent here, achieving those ambitions is realistic. 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. This is another X-Force webinar, one of a series on data in Salesforce and data outside of Salesforce. So, the data team is mostly in Toronto. ) Data Scientist Wealthsimple March 2019 – Present 9 months. Wealthsimple’s personnel is composed of designers, data scientists, and software engineers who have previously worked at big corporations such as Google, Apple, and Amazon. Do you find it to be flexible enough? Yeah, we do have a BI tool, and this BI tool runs on top of our Redshift data warehouse. Leonard Lindle: (29:34) Okay. Mandy Gu: (16:37) That's all over the place. That part gets sourced. I'm pretty happy with it. She's going to join us and tell us something about their data pipeline and about a couple of interesting innovations that her team has put together at her company. Salaries posted anonymously by Wealthsimple employees in Toronto, ON, Canada Area. This initiative started at the company to run SQL bootcamp, where anyone could sign up and get weekly lessons and exercises. You don’t need to be a Data Scientist to do ‘Data Science’ work. We've made tripwires really self-serve, and we've built them as a part of the Airflow webserver. Because we have many these checks in place, we feel a bit more confident in making the cadence shorter. Take a deep dive into the tech stack of Wealthsimple with the full transcript! We're confident that in our testing framework; if that passes, it means this is a really good state to go. How does it work? Go back to all job postings. Data Scientist @ Wealthsimple Toronto, Canada Area 500+ connections. How often do you have to update the financial aid data? I think because we've invested the time in the foundations, it allows us to deliver those projects reasonably quickly. My last co-op was at a Toronto company called Nulogy, and they did software for contract packagers. Does Wealthsimple use machine learning for analyzing financial market data, or just for operational use cases? ) I think that I really like the idea of the different hooks and the different operators and how the logic is relatively clear. But I also think Wealthsimple having diversified product offerings certainly makes it more resilient to unexpected changes in the financial world. 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. We try to mock a lot of things. In this full-day assessment, we typically do a culture assessment. Data blending can fill an essential role in comparing information from different formats and databases. We also have a series of checks that we enforce before deploying a new version of the model. Mandy Gu: (24:59) First, there would be a call with the hiring manager, and past that call, we'd send them a technical assessment. We want to enforce good SQL practices. 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? It uses the ANTLAR 4 grammar. Leonard Lindle: (11:05) You didn't write your own parser from scratch. Wealthsimple is perfect for: Beginner investors; Anyone who’s comfortable managing their funds online; Socially responsible investors There, I would say there needs to be some understanding of SQL to use this tool correctly. For example, the payload - we pass them to predictions to make sure they're intact, and that performance is up to our standards and promises as well. You said there were a couple of things that your team has built that help with the SQL and the Airflow. I think this is something we can get better with. Mandy Gu: (20:08) We build additional facts on the dimensions table on top of this raw data that we extract and load from our sources. We definitely use a lot of Python and a lot of SQL. So this has historically been a huge client pain point because of just how long it takes. If it goes through your pipe, through your QA checks, it's not going to break anything. Wealthsimple | 40,006 followers on LinkedIn. Are you sure you want to replace it? Powerful financial tools to help you grow and manage your money. I think it was really cool working there because I was also joining when they started their data team. So nobody's sitting there just worried about breaking the build of the software engineer. I think it's gotten brought up that we should be looking at our existing machine learning models more critically. 0.25% on up to $99,999; 0.2% on over $100,000; 0.50% on up to $100,000 ; 0.40% on over $100,000; Management expense ratio. Additionally, the company’s team of software engineers, designers, and data scientists are from companies like Amazon, Google, and Apple. There isn't a co-op requirement. But I also think Wealthsimple having diversified product offerings certainly makes it more resilient to unexpected changes in the financial world. ) Are you happy at five, or do you think you're looking for other people to tackle other company challenges? They can run this pipeline from end to end. So our BI tool actually does support like a Python functionality if you want to import the data as a data frame and work with it. That's the entire data science that's for your whole company. There are a lot of things we can do more to improve the client experience, but there's also a lot of work that can kind of get done on the foundations. Sometimes there will be - for example - a client application, another service that receives a prediction, and they decide what to do with those predictions. If there were issues with the data, they would most often fall into the engineering teams' domain and their stead. Product Manager with love for design & data, graduated in Computer Science/Economics from UWaterloo. Anybody who's dealt with the transferring process knows that's often an elaborate multistep process with a lot of opportunities to fail or opportunities for the customer to drop off and not continue. Join to Connect Wealthsimple. We do use DataDog and ROBAR to monitor those as well. Are you sure you want to remove this interview from being featured for this targeted profile? So the tests have to get passed before changes get made. We talked a little bit about your parser and Airflow. What kind of a cadence are you running on in terms of putting models out? ) I would say that we don't read as many papers - at least not as part of the job. ) Cool, cool. There's the understanding that if there's anything they don't know that they can pick it up on the job. Mandy Gu: (10:55) It uses the ANTLAR 4 grammar. Some of these products include a commission for your trading platform and a high-interest savings account. Mandy Gu: (32:53) It's not just operational use cases, but we don't actually use it for analyzing financial data. So if you're working at Wealthsimple and the data engineering team, you're going to know Python and SQL. Leonard Lindle: (28:06) Right? Then, we have other engineering teams responsible for maintaining those services. 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. You didn't write your own parser from scratch. So without further ado, here's Mandy. I've been there for almost a year and a half now. She also speaks about tips and tricks for building pipelines, Wealthsimple's BI tools, and when to abandon a model. We do a lot of experimental design work. That's pretty cool. Overall, it is pretty similar to what they would expect to see in production if they were running this pipeline. ) We have scrapped a lot of models in the exploration phase, and we have scrapped models in postproduction when we realized that changes in the business have made it obsolete. Anyone can just go in, create their own tripwires, and indicate the cadence and the scheduled interval. Ideally, this would happen before we start developing it. I think we've helped with the analysis, the analytics, and how that process can improve, but we don't actually use machine learning to make any recommendations in that aspect. Pre-COVID, did you have remote in place? ) However, the test ensures that we get well aligned with the stakeholders on things needed - and being a part of that process. Typically, though, we're responsible up until that point. When we talked earlier, you had a couple of machine learning projects that you're working on. Mandy Gu: (15:14) You know, we oversee the data warehouse, and we monitor like the BI tool as well. We use tripwires to monitor data freshness and check that key expectations are getting met in upstream data sources. From there, we learn about the advanced data pipeline at Wealthsimple and their extensive use of Airflow within their data stack. Did you take a course or a program in machine learning? March 10, 2019. 1 Wealthsimple Data Scientist interview questions and 1 interview reviews. Here's another one - was your favorite co-op experience? ) Huh? Instead of having clients make these decisions, we would actually use the models to make those decisions. It's a pretty easy decision just to deprecate the model and revert a lot of the aspects. Mandy Gu: (30:30) Sometimes, it does happen, and we've seen it happen at different stages of the model life cycle. Leonard Lindle: (24:14) Anybody who's dealt with the transferring process knows that's often an elaborate multistep process with a lot of opportunities to fail or opportunities for the customer to drop off and not continue. 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. I think we're just trying to get a feel for how well they think and how well they problem-solve. Just knowing SQL, you can write your own queries with the BI tool, and the BI tool can help visualize and perform straightforward analytics on the SQL output. We try to mock a lot of things. Are you using a tool that makes the most of your data? I'm really excited to be here. Wealthsimple takes the guesswork out of investing and get investors on the right track. Having that certainly makes testing a lot easier and also takes away the worry that they'll break something when they test. ) Mandy Gu: (18:57) You don't have to know any Python to use our BI tools. 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. Typically, though, we're responsible up until that point. We use Airflow a lot. Mandy is a data science scientist at Wealthsimple. It also offers users the option to enable two-step verification. So our BI tool actually does support like a Python functionality if you want to import the data as a data frame and work with it. To me, I think it takes a lot of time. Often, that data is not easily accessible, and that's another rabbit hole of "how can I get this data?" I think that it's okay to be really confused at the beginning, and it's okay if you don't know everything. So you have a real complex joint or something fancy going on. Just knowing SQL, you can write your own queries with the BI tool, and the BI tool can help visualize and perform straightforward analytics on the SQL output. Maintaining all of these different dashboards and SQL does create a lot of overhead for the team. 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. ) Want to learn more? Wealthsimple is backed by a team of world-class financial experts and the best technology talent. Mandy is a data science scientist at Wealthsimple. So, "easy" is one of your value profits - "simple" is right in your name. To begin the podcast, Mandy introduces us to Wealthsimple, going over the basics of this intriguing, millennial-focused startup. Computational Linguist Kiite August 2018 – March 2019 8 months. Is Wealthsimple safe? 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. Wealthsimple adheres to the highest industry standards in protecting your data. I like the state we have today. Wealthsimple Salaries trends. We do a little bit of everything, from data science to data engineering to a little bit of software stuff. Free interview details posted anonymously by Wealthsimple interview candidates. 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. We talked a little bit about your parser and Airflow. I want to say the most recent one because that one is the freshest in my memory. Another question from the audience: where have you applied machine learning models? I have a major in statistics. Wealthsimple makes powerful financial tools to help you grow and manage your money. Leonard Lindle: (37:04) Is your work environment fast-paced? The most time-consuming part, I find, is understanding the business problem. Are you planning on growing your team? This is an extra level of security to keep your information and investments safe. Sometimes there will be - for example - a client application, another service that receives a prediction, and they decide what to do with those predictions. This deck would orchestrate, pulling the data from where it needs to get pulled from running the training script. Do you have any lessons learned that you wanted to pass on to any other budding data scientists here in the audience? So do you guys work together in Toronto, or do you have remote in place? Learn how to enable cookies. So, we're pretty involved with a lot of the data processes. Toronto. $5+ billion. So at Wealthsimple, we are huge on SQL - everyone on the company is. 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That one is the freshest in my memory get passed before changes get made applied learning... Our data scientists and a software engineer models also add kind of runs experiments... Airflow plugin are huge on SQL - everyone on the last 60 of!

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