Interview with a Data Scientist: Noelle Saldana

We can all agree that ‘Big Data’ is overused, right?

One of the most common situations when I’ve encountered this phrase has been in client meetings during my time as a consultant. The customer will say sheepishly, ‘We don’t have Big Data yet, but…’ and will proceed to rattle off concerns about scalability or ideas for downstream analytics or smart features they want to include in their product.

These are all valid discussion points that should be addressed, regardless of the current size of their data. If a company is growing, their data will eventually (hopefully) be “Big”. It is always better to be proactive!

On the other hand, I’ve also seen companies do the opposite; they assume they do not need to have any substantial data strategy because their current data is small and they do not see an immediate need for analytics. I think this is foolish. Eventually that data technical debt is going to catch up with them.

I love seeing how much of our day-to-day lives are touched and improved by Data Science. It’s not just advertising; healthcare, transportation, food, education and more are all seeing data-driven innovations happen every single day. It’s so mainstream that I see TV commercials talking about Artificial Intelligence on a regular basis.

I also enjoy that this has made it ever so slightly cool to be a Math nerd. If not actually cool, then at least it has made Mathematicians more hirable than it was when I graduated from school. There are now many job descriptions that have “Mathematics” listed as a desired degree, when that was definitely not the case 10-15 years ago. The other day, I drove by a billboard sign advertising a Masters in Data Analytics at the local university. Times have definitely changed!

Every project I’ve ever worked on has had an estimated timeline and a goal set from the beginning. The timelines have ranged anywhere from a few days to several months, some with reasonable goals and others with downright impossible ones. Getting everyone to agree on what is both feasible in the time frame and what counts as success upfront is absolutely critical.

Some projects follow a waterfall paradigm with fixed milestones based on timeline, and other projects that follow the agile methodology. In both cases, communicating progress between the stakeholders and the project team with a regular cadence is key to managing expectations. Stakeholders should not show up at the beginning of a project and then disappear until the polished, final results are revealed. Instead, they should have a general idea how the team is progressing at any given check-in, and what incremental change they expect to see by the next one.

Time management can be tricky for Data Scientists. Exploring data and experimenting is a lot of fun, but you can easily get stuck in that step of a project. You have to ask yourself and/or your team, ‘Do you know enough that you can meaningfully work on the step?’ If you do, then it’s likely time to move on. Staying in that step may lead to diminishing returns. Data Science is an iterative process, so you can always revisit additional ideas you had for a previous step if you have time.

What’s ‘good enough’ for a model depends on how you defined success for the project. Does it meet the accuracy expectations for whatever decisions or tools it has to help? Does it fit into the technical workflow?

We still have a long way to go. In my personal experience, the field of Data Science has felt a bit more diverse than the tech industry at large. This may be partially because industry Data Scientists come from such a wide range of academic and professional backgrounds. I’ve seen more diversity in both the speakers and audiences at conferences over time, which is encouraging.

I’m also excited to see conferences like Women in Data Science and Women in Analytics growing and thriving.

When I attended my first big tech conference nearly 10 years ago, there were over 10,000 attendees. I remember keeping count of all the other women I had seen that week and it was barely in the 100s. The same could have been said for any underrepresented minority group if I was keeping count. A few years later, I was standing in line at the bathroom during a break at a different conference when all of us in line realized, “There are finally enough women at this conference that there’s actually a line!”

It’s tough being the odd one out. It’s tough when a job cannot accommodate for life happening. As a woman of color and a parent, I am reminded about this truth on a regular basis. However, I’m not any less valuable an employee simply because I may be different from my peers.

I am so pleased to see that diversity and inclusion has become a more common theme across the industry. It is not just grassroots efforts, but also full time positions being created specifically to lead these efforts. Two years ago, Pivotal hired its first Diversity and Inclusion leader. They now have a growing number of Employee Resource Groups, various internal training programs, commitment to hiring more diverse candidates, all which is having a positive impact there. I recently joined Salesforce and learned that they have a Chief Equality Officer, and a commitment for equality in both the workplace and the wider community.

My hope is that this trend continues. In the next 10-20 years when the kids of today eventually replace us all, maybe the tech surrounding our lives will all be built by teams that are a representative sample of the world.