Alicia FrameMy name is Alicia Frame, and I am Neo4j‘s Lead Product Manager and Data Scientist.

I work in our Product Management team to set the roadmap and strategy for developing graph-based Machine Learning tools.

I have a Doctorate in Computational Biology from the University of North Carolina at Chapel Hill, and a first degree in Biology and Mathematics from the College of William and Mary in Virginia. I have over eight years of experience in enterprise Data Science at places like Benevolent AI, Dow AgroSciences, the US EPA, and now Neo4j.

I think Data Science can seem a bit like alchemy, or a Dark Art… people get advanced degrees in interesting but esoteric stuff, they use complicated and obtuse methods to transform your data into black box predictions, and you’re supposed to trust the results. I see my mission as to democratise AI — taking rigorous science and making it possible for anyone who downloads the software to run highly predictive, powerful algorithms. We’re taking all these incredibly academic science methods and making it possible for anyone who downloads our software to run algorithms with a few commands. We want to get it to the place where a user doesn’t need to have completed a dissertation in Network Theory or Deep Learning to have access to highly predictive, powerful methods; she just needs to know the question the business wants to answer, and the data to use.

I am super-excited about some of the things we have planned for our next release of the Neo4j graph database to help do that. My team is working on prototyping several different implementations of graph ‘embeddings’, and we’ll be the first software available that offers them on an enterprise scale. Embedding is a way of representing data in lower dimensional space (think of a short summary of a complicated concept) that is machine-readable. It’s a way of taking all the information about nodes in a graph (or a word in a document) and summarising the important parts, and you can use that summary to predict outcomes, properties, or classes.

It’s a very powerful machine learning technique, but normally you usually need to code something from scratch, and you can really struggle with big data sizes. We’re democratising the technique so anyone can calculate an embedding for their graph data with a few simple commands, which is, I think, a great innovation.

Did you ever sit down and plan your career? 

Not really! If you had asked me when I was in college if I expected to be a Data Scientist for a graph database company, I would have had no idea what that meant, and laughed at you. I was always good at maths and computer science, but I had no confidence in myself, and thought of them as dorky hobbies. I originally wanted to be a biologist, but I was a miserable bench scientist and terrible in the field… I never had the patience or precision for that kind of slow, meticulous work.

What I was good at, though, was writing code to analyse my data and building mathematical models to describe the patterns I expected to find. While I was getting my degrees, I’d managed to pick up concentrations in Maths and Computer Science based on coursework I was taking to satisfy my curiosity. About halfway through my Doctorate, I realised I could do the things I loved full time as a career.

My first job in graduate school was as a database administrator for a Federal database of toxicological data. I didn’t know SQL until I qualified for the phone interview and found out I would be evaluated on my skills with it. The night before, I went through an entire ‘Teach Yourself SQL in 24 Hours’ book and somehow got the job, I’m relieved to say.

Ever since that first opportunity, data, and databases, have defined my career.

Have you faced any career challenges along the way and how did you overcome these?

I struggled for years with imposter syndrome. I was afraid someone would suddenly realise I wasn’t really qualified or wasn’t as smart as I was pretending to be. What I know now: If something isn’t working for you in your job — you’re not challenged or respected, there’s no growth trajectory for your role, your workload is insane, whatever it is — you need to speak up. You’ve got highly in-demand skills, and there are plenty of places looking to hire, so don’t waste your time if a company or position isn’t the right fit for what you want to pursue.

What has been your biggest career achievement to date?

I’ve had a lot of different jobs, and I don’t think any-one’s role is a bigger achievement than another. What motivates me is building tools and programs for people to address their own problems: for example, building tools so people can use graphs to cure cancer, or writing policy to keep children from eating lead paint, or building models to predict and screen out pesticides that can cause cancer, it’s about doing good, regardless of your situation.

To me, it’s the driving force behind why I love building tools is to enable others to do amazing things. I’m good at it, and that allows others to shine. It’s also about more personal matters, such as taking the time to be kind or mentor someone, or just listen and make them feel valued. To me, these are just as or more important as getting a flashy award or big promotion.

What one thing do you believe has been a major factor in you achieving success?  

My biggest break came from mentoring from a super strong all-female team. I had spent most of my career working with men and learning how to mask my opinions. I’d internalised fear of being seen as too abrasive or direct, but that changed that. After I was in the office for a week or two, no one told me to be less me. I remember one of these women mentors coming to my desk and telling me to just spit it out in meetings and say what I meant, instead of hiding behind lots of platitudes.

They never told me to be quiet or keep my opinions to myself. They made it clear that they knew I was smart, and they trusted me to make decisions.

What top tips would you give to an individual who is trying to excel in their career in technology? 

Cut through the noise, and only ever focus on what’s really important. Ignore MOOCs, Kaggle competitions, and the social media Data Science influencer echo chamber. No-one was hired on the basis of online credentials or the number of retweets on a post. Also, look for opportunities to take on Data Science projects and tasks in any role

Don’t be afraid to make moves or shift your career in a horizontal direction. If something isn’t working, speak up. You’ve got in-demand skills, and there are plenty of places looking to hire, so don’t waste your time if a company or position isn’t the right fit.

Always come prepared. Make sure to build a portfolio of projects to showcase your experience. Be able to explain the question or problem at hand, how you solved it (both in plain language and technically), what the business value was, and what your specific contributions were.

Sharpen your soft skills — they will take you farther than you think. Given the nature of the software industry, many individuals forget that it’s important to work on and value capacities for active listening and compassion. Plus, being great at networking and building relationships with others is a key element to building a successful career path.

Do you believe there are still barriers for success for women working in tech, if so, how can these barriers be overcome?

There are certainly barriers for women to overcome: I’m cognizant of the fact that while I am a successful female data scientist, I’m often the only woman in the room. I am always aware of how what I say will be perceived; I know that, as a woman, when I express disagreement, it’s more likely that I’ll be perceived as ‘bitchy’ or angry, instead of raising a valid point, or providing constructive criticism.

I think it’s important to take women at face value. Don’t apply gendered assumptions to what I say. I don’t have to be the nurturer on the team and I can disagree with you without it being personal. Unaddressed expectations about emotional labour, or how women should present themselves, are one of the most difficult things to overcome.

What do you think companies can do to support and progress the careers of women working in technology?

  • Don’t have strong/rigid requirements on years of experience or specific skill sets. Women are less likely to exaggerate their skills or experience than men, and I think this can lead to them being screened out
  • Invest in women: we’re less likely to speak up and demand resources like external coursework or coaching, but it doesn’t mean we won’t benefit.

There are currently only 17% of women working in tech, if you could wave a magic wand, what is the one thing you would do to accelerate the pace of change for women in the industry? 

Get girls involved in maths and computer science early. Part of the problem with women in tech is that there aren’t enough of us. Women aren’t majoring in science, technology, engineering, and mathematics (STEM) courses, which leaves us underrepresented professionally. It wasn’t always this way; originally, programming was seen as a woman’s job, but today, girls get discouraged from technical fields early on (it’s too geeky, they don’t have the background) and it’s an uphill battle from there.

If I could, I would make every secondary school girl take a course on the basics of computer programming, in a supportive environment with female mentors and instructors. I think this would lead to massive changes in the industry. Change only happens when you demand it, and we need more female voices in the industry calling for equity and representation — and education is where we start.

What resources do you recommend for women working in tech? 

It’s worthwhile to have a social media presence on LinkedIn, Twitter, Medium, etc – but don’t let that distract you from building a solid CV. I’m a big fan of reading original peer-reviewed publications and finding code from the authors, to learn more about new techniques and the state of the science. For conferences, I usually follow the ‘gold standard’ machine learning conferences (NeurIPs and ICML), but I also would recommend following something specific to your field of interest (I’m a big fan of the American Chemical Society and the Royal Society of Chemistry).


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