We recently went behind-the-scenes of this innovative company to see the office in action, meet employees and learn about the culture driving its success. How often do you have to update the financial aid data? I think that was one of the really nice things about Waterloo was getting that work experience. Do you think you would want to add anything to it? So if I'm one of your SQL programmers and I'm in charge of a data pipeline in Airflow, I just have to write a SQL statement that evaluates true or false that tells me something about my pipeline. However, the test ensures that we get well aligned with the stakeholders on things needed - and being a part of that process. For deployment, we use S3, usually, to store these model assets for our deep learning models - they're all on TensorFlow. Sometimes there will be - for example - a client application, another service that receives a prediction, and they decide what to do with those predictions. We want people to write syntactically correct SQL as well. They play an advisory role in Wealthsimple's investment management process and serve as a sounding board for Wealthsimple's … So the data science part is in Toronto. The fintech space is crowded, but we are well positioned for continued explosive growth and success. Mandy Gu: (26:58) The most time-consuming part, I find, is understanding the business problem. Leonard Lindle: (36:27) Does your team spend a lot of time keeping up with the latest developments in the field, such as reading deep learning papers? When we talked earlier, you had a couple of machine learning projects that you're working on. Leonard Lindle: (37:04) Is your work environment fast-paced? So this has historically been a huge client pain point because of just how long it takes. Is it just Excel or CSV dumps? A lot of that gets orchestrated using Airflow. I definitely think there are plans to continue to grow this team. I think we're just trying to get a feel for how well they think and how well they problem-solve. Leonard Lindle: (19:55) I think one of the other things a lot of companies do is write views for end-users. Did you take a course or a program in machine learning? Our models also add kind of a lot - our more important models are services on their own. Hello everyone! I work at Wealthsimple. It's been great speaking here and answering and engaging with the audience. ) Wealthsimple Inc. is a Canadian online investment management service focused on millennials. Integrate Your Data Today! You can manage your accounts easily on your own through our website and app. If you go on the Wealthsimple website, it does give a breakdown of how we pick out the securities for investments, and machine learning is not part of the process. Are you using a tool that makes the most of your data? Today, we have somebody who has data outside of Salesforce, Mandy Gu. 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. What do you think of Airflow in general? There is never a shortage of projects, and there is a lot of really exciting work. The firm was founded in September 2014 by Michael Katchen and is based in Toronto. We have five data scientists and a software engineer. This has been a lot of fun. Wealthsimple is one of the biggest robo advisors in Canada – but is it one of the best? Do you do technical assessments or take-home assignments? Are you happy at five, or do you think you're looking for other people to tackle other company challenges? 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 … Our models also add kind of a lot - our more important models are services on their own. Mandy is a data science scientist at Wealthsimple. I got thrilled to see that because a good portion of my money is with Wealthsimple. Leonard Lindle: (35:48) How often do you have to update the financial aid data? We give on-demand advice from real human beings. I'm pretty happy with it. It's been great speaking here and answering and engaging with the audience. Finally, Mandy talks about lessons learned with the organization and gives a personal history of her education and career. If this check fails and evaluates to false, it would trigger some type of alert to the right people. Mandy Gu: (17:28) I think one thing about working here is there's never a shortage of projects. So there are definitely a lot of very interesting projects. -Wealthsimple has extremely lofty ambitions, but because of the sheer talent here, achieving those ambitions is realistic. I would say that we don't read as many papers - at least not as part of the job. ) 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. I think one of the other things a lot of companies do is write views for end-users. Wealthsimple is the smartest and easiest way for everyone to invest their savings. 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. So we did leverage a lot of those open-source frameworks out there. Most of the machine learning models start with a business problem, and we work on it from conception. We use Airflow a lot. There isn't a co-op requirement. Well, thank you for telling us some more about Wealthsimple. There's usually a pair programming and problem-solving segment as well. 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. So you must have a production move to production workflow before you can push that into your data warehouse as one does. My team's responsibility is more like loading that data into the data warehouse. What do you do to get those reports out, and do you use any tools? Another question: What's your tech stack for deploying and monitoring machine learning models? ) I think it was really cool working there because I was also joining when they started their data team. We try to keep on top of these things. Wealthsimple makes powerful financial tools to help you grow and manage your money. Or is it just nice to have? These Data Scientists are responsible for building and maintaining the data infrastructure to support the rest of the company. ... such as the computer-science pioneer Jean Sammet — produced a language, much like FLOW-MATIC, that was easy on the eyes. Mandy Gu: (15:14) You know, we oversee the data warehouse, and we monitor like the BI tool as well. Leonard Lindle: (11:05) You didn't write your own parser from scratch. We're confident that in our testing framework; if that passes, it means this is a really good state to go. 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. That does give us the confidence to develop faster. ) 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. With the BI tool, there's a nice get integration. Do you have any tips or tricks on how to save time building your pipelines? Can you tell us where you are without giving out any Wealthsimple secrets? I then worked as a Data and Operations Associate at Wealthsimple, and here, I ... My point is this: despite not having a single data science job, I was able to learn several data science-related skills with each job building off of the previous one. 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. ) So if you're working at Wealthsimple and the data engineering team, you're going to know Python and SQL. Leonard Lindle: (29:34) Okay. What kind of a cadence are you running on in terms of putting models out? ) Take a deep dive into the tech stack of Wealthsimple with the full transcript! Next, there's a walkthrough of Wealthsimple's machine learning techniques, their model cadence, and a look at the company's upcoming projects. Do you have to have a major in machine learning there? ) However, the test ensures that we get well aligned with the stakeholders on things needed - and being a part of that process. You have locations in New York, London, and Toronto, and you're associated with the Toronto location. Wealthsimple Home Page. Mandy is a data science scientist at Wealthsimple. We have a pretty standard machine learning workflow getting set up, and a lot of that leverage is on Airflow as well. I can say that we do a lot of experiments. So one previous issue that a lot of members of the team had was it was taking too long. Go back to all job postings. 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. 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. Leonard Lindle: (34:15) Here's another one - was your favorite co-op experience? 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. For deployment, we use S3, usually, to store these model assets for our deep learning models - they're all on TensorFlow. Is that a requirement that the tool belt must get used before somebody can push something into production? Mandy Gu: (22:13) We are responsible for that as well. Yeah, we do a lot of our machine learning models. With the BI tool, there's a nice get integration. Our mission is to help everyone achieve their financial goals by making investing simple, affordable, accessible and personalised. We try to roll out Airflow not only to the data platform team but also to the broader engineering team and to whoever can benefit from using it. What's the reporting look like there? We do use DataDog and ROBAR to monitor those as well. ) I interviewed at Wealthsimple (Toronto, ON). Often, that data is not easily accessible, and that's another rabbit hole of "how can I get this data?" So do you guys work together in Toronto, or do you have remote in place? Having that certainly makes testing a lot easier and also takes away the worry that they'll break something when they test. ) 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. March 10, 2019. http://glassdoor.com/slink.htm?key=vQm3z. Wealthsimple is backed by a team of world-class financial experts and the best technology talent. In this full-day assessment, we typically do a culture assessment. I think that it's okay to be really confused at the beginning, and it's okay if you don't know everything. Ideally, this would happen before we start developing it. Your account and data is also protected by encryption and two-factor authentication. I believe the lift was actually close to 20%. And if so, how many do you have to take? ) There have definitely been a lot of vast improvements made to our CICD workflow recently. We talk to them and answer any questions they may have. As of August 2019, the firm holds over C$5 billion in assets under management. The company has raised $78 million in capital. Do you do a lot of AB testing on your website? Toronto. Did you take a course or a program in machine learning? 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. Are you pretty happy with it? 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. I focused on backend API creation, maintenance, and enhancement, with an … Powerful financial tools to help you grow and manage your money. Pre-COVID, did you have remote in place? ) If you grow your team, what would you like to tackle in the near future? ) First, there would be a call with the hiring manager, and past that call, we'd send them a technical assessment. We call it tripwires, and it's a custom Airflow plugin. We do a lot of experimental design work. We are responsible for that as well. Leonard Lindle: (39:54) Well, thank you for telling us some more about Wealthsimple. There are a lot of interesting projects that kind of have gotten prioritized for these upcoming quarters. Leonard Lindle: (22:45) 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? Mandy Gu: (13:21) Every model is different. Is it good enough? I would say that we don't read as many papers - at least not as part of the job. Wealthsimple | 40,006 followers on LinkedIn. Wealthsimple gets personal on the first screen of the account setup process. Wealthsimple is staffed by a team of software engineers, designers and data scientists, out of prominent tech companies such as Amazon, Google and Apple. So at Wealthsimple, we are huge on SQL - everyone on the company is. Whether it is through the onboarding phase or through getting money into Wealthsimple. Client Success; Core Operations; Finance; Legal/Compliance; People Operations; Internships / Co-ops. We work on a lot of new reporting and pipelines all the time. We have tripwires around things like model performance. Tell us a little bit about Wealthsimple. I think this is something we can get better with. We also use Airflow to manage a lot of our reporting jobs. We build additional facts on the dimensions table on top of this raw data that we extract and load from our sources. Wealthsimple has world-class financial experts and top-talent from Silicon Valley working for you. Do your analysts use any kind of data visualization tools like Tableau or something like that? Read our guide and learn what these concepts mean for your business. This is another X-Force webinar, one of a series on data in Salesforce and data outside of Salesforce. I think that's about everything. Management fees. So do you guys work together in Toronto, or do you have remote in place? We do a lot of AB testing. What's the interview process like? Hello everyone! Wealthsimple builds a diversified portfolio of ETFs on the investors' behalf and guides them in achieving their financial goals. What are your internal rules on that? 1 Wealthsimple Data Scientist interview questions and 1 interview reviews. So how many co-ops does a Waterloo student have to take? You don't have to know any Python to use our BI tools. Leonard Lindle: (37:44) How do you see Wealthsimple adjusting to the new, volatile financial market that we are seeing? Any advice you can give new team members, that kind of thing? We want to enforce good patterns. 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. The process took 4 weeks. Toronto. Are you sure you want to replace it? Computational Linguist Kiite August 2018 – March 2019 8 months. I think it's gotten brought up that we should be looking at our existing machine learning models more critically. It's mostly like my other very brilliant team members that did, but I have really benefited from it. So the tests have to get passed before changes get made. Sometimes there will be - for example - a client application, another service that receives a prediction, and they decide what to do with those predictions. Assets. Okay. You didn't write your own parser from scratch. Do you have any tips or tricks on how to save time building your pipelines? ) Data Science and Engineering. This is an extra level of security to keep your information and investments safe. Do you run into any issues with updating that data? ) So I think everyone on the team is responsible for the end-to-end development and deployment of these models. It uses the ANTLAR 4 grammar. Is most of your operation run out of Toronto, or is that all over the place? ) 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. Are you going to be adjusting any models due to that? Every model is different. If it goes through your pipe, through your QA checks, it's not going to break anything. Right, because you don't employ any drag and drop or simple-to-use ETL tools. Mandy also focuses on the company's expansion techniques, exploring how the Wealthsimple team grows and its hiring practices. Here's how they're doing it. I assume that's one of the things that your team works on: trying to make the Wealthsimple experience easier for your end-users. We call it the SQL tool belt. 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. Wealthsimple is a digital investment service that uses technology to make investing simpler, smarter and low-cost. I've been there for almost a year and a half now. Data Scientist @ Wealthsimple Toronto, Canada Area 500+ connections. Mandy Gu: (36:03) My team's responsibility is more like loading that data into the data warehouse. Does your team spend a lot of time keeping up with the latest developments in the field, such as reading deep learning papers? ) So this service - which we've been calling SQL toolbelt - we integrate this into our development and testing framework for the data warehouse. We definitely use a lot of Python and a lot of SQL. So there's no end of work. Your feedback has been sent to the team and we'll look into it. 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. ) 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. ) I think this is something we can get better with. I think it's four or five being the minimum and six being the maximum. Is it good enough? Are you happy at five, or do you think you're looking for other people to tackle other company challenges? ) So we talked a little bit about the machine learning that your team does. Wealthsimple started out as an investment platform, which provided this nice, really easy way of investing money. Do you do technical assessments or take-home assignments? Log in to Wealthsimple to grow your money like the world's most sophisticated investors. So we extract and load data from these data sources into our Redshift data warehouse, and we build some additional facts and dimension tables on top of this data in our data warehouse. Actually, some programs do have a co-op requirement, but for mine and a lot of others, it's optional. I think it's just a process of exposing yourself to more things and picking them up as you go. So I think everyone on the team is responsible for the end-to-end development and deployment of these models. Mandy Gu: (26:14) We certainly don't expect that they would be familiar with our entire tech stack or everything that we use. There's usually a pair programming and problem-solving segment as well. Mandy Gu: (16:53) I think there are plans definitely to grow the team, and people recognize that the team does good work, and there's a need for more. You know, we oversee the data warehouse, and we monitor like the BI tool as well. Going back a second, you went to the University of Waterloo in Canada. Our talented team of software engineers, designers, and data scientists have previously worked at … I like the state we have today. They're changing very rapidly, and there are so many new tools to make things easier. We certainly don't expect that they would be familiar with our entire tech stack or everything that we use. Basically, it's not going to bring anything down so you can go faster. Their responsibilities include monitoring the data pipelines, improving the data warehouse and maintaining API endpoints for serving model predictions. Mandy Gu: (32:53) It's not just operational use cases, but we don't actually use it for analyzing financial data. 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. ) What you've developed as a functionality around the tripwire to handle that Airflow alerting and things like that. Is there a co-op requirement? Leonard Lindle: (32:44) Does Wealthsimple use machine learning for analyzing financial market data, or just for operational use cases? Ready to get an inside look at a game-changing company's advanced tech stack? 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. Structured data vs unstructured data – what are the key differences? There's the understanding that if there's anything they don't know that they can pick it up on the job. ) We talked a little bit about your parser and Airflow. I think one thing that I find really impressive about this team is that we're all multitasking. 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 did six co-ops while I was at Waterloo. We try to keep up with things. What do you do at Wealthsimple, Nick? 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. So in terms of our data sources, we have a bunch of internal microservices, and we also have other integrations. Mandy Gu: (15:58) We have five data scientists and a software engineer. Do you have any lessons learned that you wanted to pass on to any other budding data scientists here in the audience? This would get abstracted entirely from the process. Or did you write your own SQL parser from scratch? Data Scientist Wealthsimple March 2019 – Present 9 months. Join to Connect Wealthsimple. And so when you have an operational model like that, is your team responsible for the live modeling that your application uses? ) Questwealth Portfolios vs Wealthsimple: How it works? Many people here can build their own dashboards and write their own SQL queries. So nobody's sitting there just worried about breaking the build of the software engineer. So this has historically been a huge client pain point because of just how long it takes. Mandy Gu: (12:27) This deck would orchestrate, pulling the data from where it needs to get pulled from running the training script. 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