Earlywork #11: How To Break Into Data Science
Featuring roles from Evergen, UpGuard, Secure Code Warrior & more + interviews with Tristan Frizza (Atlassian), Alex Bunn (Westpac), Jacky Wong (Vector AI) & Jacky Koh (Vector AI)
It’s ya boi Dan serving up Chapter 11 of Earlywork, a (usually) weekly newsletter that gives you:
A shortlist of the best roles for students & recent graduates across tech & startups in Sydney (+ remote roles).
Free career resources for young people looking to break into tech & startups.
Interviews with young startup founders & employees.
I know, I know, it’s been a hot minute since the last Earlywork newsletter!
The reason why? I’ve spent the past month volunteering as a farmer (with no WiFi and limited reception) at Kennedys Lane Farm, an organic agroecological veggie farm in Ewingsdale, Byron Shire. I managed to find the opportunity through a pretty neat platform called HelpX that allows travellers to volunteer on farms and homestays in exchange for accommodation & food.
Super insightful and grateful to get involved hands-on with where our food comes from & how to source food sustainably; feel free to shoot me a message if you’re ever interested in learning more about how to get involved with permaculture and sustainable farming 🌱🌱🌱
But now, we are back in full swing in with the tech & startup shindig, and excited to share a lil’ update on the Earlywork journey!
I’ve partnered up with two great humans, Jono & Clint, to test out a career coaching service for students & recent graduates over the next couple of months.
We’ve collectively had a pretty diverse array of internship and full-time work experience across mature tech companies, scaleups, early-stage startups, venture capital, and startup accelerators, and share a common passion for helping fellow young professionals who are looking to break into a meaningful role in tech & startups.
If this sounds like you, stay tuned for next week’s newsletter to book in a free 30 minute introductory coaching session with the Earlywork team. 😄
And if you’re not already part of the crew, subscribe now to keep a pulse on our latest stories and conversations:
Let’s hop in and hear from THREE different interviewees in this week’s…
💡 Weekly Cheeky Tip:
How to Break Into Data Science
We’re revisiting the How to Break Into X theme from our earlier edition on How to Break into Product Management to clarify another nebulous field with buzzword sex appeal but a lot of misconceptions.
Data science is a broad interdisciplinary web of statistics, programming, data analysis and mathematics, the goal of which is to extract insights from structured and unstructured data.
There’s a ton of distinct roles and specialties within this field, including data scientists, data analysts, data engineers, machine learning engineers, etc. and it can get hella confusing trying to work what you need to learn in order to break into this space.
Here are some simple definitions extracted from Chandra Reddy’s article on data careers to help break down the buzzwords:
Data Analyst: Engages in data inspection, cleaning, transformation and modelling, and communicates the result of the analysed data with their team.
Data Scientist: Analyses data using machine learning algorithms to gain future insights that could propel a company,
Data Engineer: Builds and optimizes a platform that ensures accurate data for data scientists and analysts to work with.
Machine Learning Engineer: Trains the exiting system to make it learn and predict the trend or outputs if the dataset is given.
But in helping clarify how to land these sort of roles, we spoke to 3 young guns in the data science space for their top tips on what skills and experiences to build in order to land a data-aligned role, from what sort of skills to prioritise, to what sort of side projects to focus on.
You can check out personal insights from Tristan Frizza (Associate Data Scientist @ Atlassian), Alex Bunn (Senior Data Scientist @ Westpac) & Jacky Wong (Founding Scientist/Engineer @ Vector AI + Founder @ GALAT AI) on how to break into data science on the Earlywork website here.
Associate Data Scientist @ Atlassian
Code lots. Usually SWE skills aren't prioritised in analytics/DS roles as an actual developer, but in my opinion, coding is such a ubiquitous skill and it really helps to write clean, modular code.
Do side projects that you actually care about. If you're just doing it for the resume stack, then you won't put that much effort in. It really stands out in interviews when candidates demo something unique that's not just some basic Titanic dataset model from Kaggle (that is a huge red flag, but maybe less of a red flag for intern roles because they're no doubt inexperienced).
Be an independent learner. Take courses on e.g. Coursera, watch videos on Youtube, read papers, do Kaggle comps etc. (not all of the above but at least 1 or 2). Since it's a new field, there are so many new things to learn, and methods and techniques change pretty quickly. There's also not as established a curriculum for it in uni yet; I pretty much self-taught everything DS related, and continue to do it daily on the job.
Tristan's also working on a dope machine learning side project. You can check it out on Github here.
Senior Data Scientist @ Westpac
Expand your connections on LinkedIn and talk to some people in the industry. You learn about techniques and technology from the people you meet and then you can self teach yourself the techniques and methods.
Sign up to some article publishers like Medium or Towards Data Science to keep on top of new models and techniques that are being used. This is important to build a breadth of knowledge.
Start some projects of your own using new tools and techniques. Don't rely on the same method for everything, try something new.
Collaborate on open source projects and make some contributions
Build a capable skill set. Cover data engineering, analysis and data science. An employable data scientist is one that can ingest, analyse, model and build visualisations. Software to be familiar with: sklearn, Tableau, PowerBI, Tensorflow/PyTorch, SQL, PySpark, pandas.
Here are the skills you need to get a DS job (in order of importance and difficulty):
LEARN PYTHON. No one in industry uses R. If they do, you’re looking more at not so up to date data science firms/actuarial firms. But it’s also important to understand that python is far more versatile than R for projects. Project-based learning works well and is usually good enough to talk about in interviews. Bonus if this is a tangible product that you have metrics for (downloads/views/stats)
Open Source projects. You should look to contribute to open source GitHub projects where possible (our project at https://github.com/vector-ai/vectorhub is open source and welcoming PRs! It’s also easy to contribute!) VectorHub is our project for maintaining state of the art vectors which form the base for AI search/AI recommendations. Feel free to check out at hub.vctr.ai for what this looks like!
Winning data science competitions. If you are interested in a competition, definitely participate in them and try your best to win. Kaggle has a constant stream but the technical bar is high (don’t be afraid though - tonnes of resources to help out newbies). I recommend winning or ranking in University or national ones first. This greatly demonstrates interest and intelligence if you do end up doing well - otherwise, you simply learn something new to help you win the next competition. For university students, the annual EY one/UNSW DataSoc one should be an easier game.
Leave a cheeky comment to let us know what career path breakdowns you’d like to see next!
⛅ Intern & Part-Time Roles
🌞 Graduate & Full-Time Roles
🌏 = remote role
1️⃣ 🕐 💪 One Minute Hustle
We are back once again with One Minute Hustle, a bite-sized interview with an emerging Australian young startup founder or operator. Today, let’s dive deeper on our data science theme and get inside the noggin of someone who combines deep technical expertise and entrepreneurial flair…
What are you working on? We are working a platform called Vector AI (https://getvectorai.com) to make the end to end process of utilising vectors magical. Vectors are meaningful numerical representations of rich data in multi-dimensional space, it can be used to represent any kind of data such as image, text, audio, users, etc. By making it easy and magical to work with, we want to enable more companies and developers to utilise vectors to improve their processes and create new products.
How'd you get started? The technology is already powering products we use every day like google search, youtube recommendations, Pinterest homepage, TikTok fyp, etc. However, this is only accessible to the biggest companies because building a productionisable pipeline for vectors require large teams of data scientists and engineers. I'm pretty obsessed with vectors and strongly believe that there is immense value and new creative products that can be built if vectors were in the hands of more companies and developers, so that’s why I am doing this I want to make this accessible to more and more people.
Why do you do what you do? I got started on vectors when I was frustrated with search experience on almost every website other than Google, for example, if you go on a eCommerce store like Kogan or JB Hi-Fi and search “big tv” what it returns is not tv but “big bang theory tv shows”. Because of that, I started looking into what makes Google’s search experience so special compared to others, that is when I discovered Vector Search and BERT and how it is a core part of what makes their search engine understand the query and get you the most relevant results. Then more and more I researched and worked with machine learning, I realised that searching with vectors was really powerful and actually powering a lot of the products we use every day, YouTube recommendations, Spotify discovery, Pinterest, reverse image search, etc. However a lot of the companies and developers I talked either never heard of it or didn’t know much about it, so that’s when I started Vector AI to help this already proven technology to be adopted more widely by making it as magical and easy to use.
It’s 2021 and we are done!
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