— How to break into Data Science
— Trends in Data Science in 2023
— Machine Learning
— Data Science & Gaming
Ravit kicks off his first guest of 2023 by talking with Carly Taylor, a Senior Manager of Franchise Security Strategy for Call of Duty at Activision. Carly started off her professional career as a chemist, so she is the best person to talk to about transitioning into data science. Her dream job was in gaming but she never saw herself in this industry. She even thought it was a joke when she applied to her first position with Activision, telling her friends ‘Wouldn’t it be funny if I got a job working on this game that we can’t stop playing?’. About 2.5 years later, she is still loving it and sharing her story with others.
I am particularly interested in this session because I have a background in behavior design and am interested to learn about the overlap between the gaming industry (gaming being a very heavy behavior-based activity) and data science. Carly’s stories did not disappoint! 🤓
Given the potential slowdown in the economy and possible reduction of teams, how can someone address this when breaking-in Data Science?
Carly paused as there is no kind way to put this, she said that for those junior level and newbies out there, it is not going to get any easier in the next year. We have all noticed this slowdown and observed that teams are downsizing. However, she does see opportunities to learn things such as systems design and operational efficiency. Companies need to be wise in how they are spending their money so these skills will be favored. She advised others to tailor your resumes to include such items as saving money and driving efficiency to make yourself stand out from other applicants.
Ravit shared this article discussing why we should be worried about the volume of tech layoffs lately. Carly shares that Companies are being rewarded for showing that they can tighten their belts and be willing to make labor reductions. She sees a cycle happening here in that if the stock market rewards this kind of behavior, you are going to see more of it.
What type of data projects should someone work on portfolio-wise with the intent to move into the gaming industry in order to stand out from other candidates who have prior industry experience?
Carly starts by explaining that the gaming industry is not that different from other big tech companies and the problems they face. One area she notices is still very interesting for people is deep learning. Here you can create a diffusion model to help with content generation. Another area she believes is a huge opportunity is in reinforcement learning. All games can benefit from an RL expert to build bots to run through the game to do things like test for bugs, or anything that requires man hours. One last area is with recommendation systems. Anything you see recommended to you as a player comes from this and is an opportunity for growth.
What do you see are the most important skills in the gaming industry?
- SQL: This is not going anywhere anytime folks. Start to learn it, learn to love it!
- Python: This is still king. There are so many free online courses available to learn, start today
- Statistics: This cannot be overlooked. My be hard to learn but once you get it, it is totally worth it
- Communication: Storytelling is important but also being able to communicate to non-technical people is critical. Please learn to be a good communicator
- Cloud Infrastructure: Almost every industry has switched / will switch to cloud providers. Also, this is a skill that you cannot fake. You will have to get certifications and/or on-the-job training here.
I imagine creating study guides and understanding pros and cons of each statistical technique is necessary for interview prep. I should also think being able to break down complex processes to laymen’s terms is also measured during the interview, as is speaking the language of business stakeholders. What else should we be mindful of as we prepare for interviews?
The question itself includes about all you need, but Carly did share a few additional tips here. She mentioned that interviewing is one of those skills that you just get better when you do more of it. The biggest advice is to be yourself, feel comfortable in your skin and remember, there is nothing wrong with saying ‘I don’t know’. Try something like “I am not sure, but this is how I would figure it out…”.
When playing the computer, how do they program the computer to play currently?
Carly didn’t know and referred us to researching the internet for this one. However, she did mention that there are some interesting skill methodologies to determine the skill of an AI and its abilities.
Does anyone know some good resources on this topic?
Given the rise in low/code no code systems, how do you think this will affect the Data Science industry/roles in the future?
Carly only had positive predictions here. She said if you don’t have to manually code things like the test/train split, you have more time to focus on the areas where computers still can’t contribute. Where the humans still need to step in 😉
How to find job opportunities, tips for standing out?
- Carly stressed the importance of finding a network of people you trust. She admits that this can be hard and awkward at first, but it totally pays off. Also, if you are part of a community or mentorship, make sure to give back, don’t just take. It’s all about helping people!
- Apply quickly to jobs once you find them. Consider setting parameters in your search for the latest time posted.
- One particularly interesting concept Carly shared was mirroring. We as humans are meant to be social so we tend to mirror other people we admire and those we hang out with. So take this concept and apply it to apply for jobs. Look at the language of the job posting and mirror that language. If they use fun language in the job posting, mirror that in your resume and also be fun.
If you were stranded on a desert island and could only bring three people with you, which ones would you choose and why?
- Zach Wilson: He helped her a lot with LinkedIn as a mentor
- Vin Vashishta: She thinks he is a great strategic data leader and she often repeats his advice to others (she should be a Vin spokesperson)
- Ravit: He built up such a great community, he is really authentic and awesome!
- + Kate: She is an inspiration for women in the data field
This wasn’t a straightforward list, I made it look easy. Carly said this was hard (like picking a favorite kid) 😂