I joined C3.ai’s data science team in early 2017 after graduating from Harvard’s computational science program. During recruiting season in late 2016, the market was so hungry for data scientists that many of us had multiple job offers. What attracted me to C3.ai was the unique opportunity it offered to work on problems with global impact—across a diverse array of sectors, including healthcare, energy, and finance—alongside machine learning and optimization experts.
I chose to join C3.ai’s data science team for three main reasons:
- 1. Focus on Machine Learning: Unlike many so-called "data science" positions in the Valley, it offered a genuine opportunity to work with machine learning in a nontrivial way alongside a team of experts. If you love the thrill of Kaggle competitions, reading about and experimenting with the latest techniques in deep learning and natural language processing, then C3.ai is the right place for you.
- 2. End-to-End Ownership on a Central and Critical Team: C3.ai is an AI company, which means as a data scientist, you are a valued member of the company working in a central, highly impactful team. As such, you get to own the process soup to nuts, and gain hands-on exposure to all sides of the company from model development, to software engineering deployment processes, to client management.
- 3. Exciting Customers with Global Presence: Data scientists directly interact with and see the immediate impact of their work on C3.ai’s rapidly growing list of customers including the US Air Force and Fortune 500 organizations. From my experience, I believe that what makes a data scientist valuable is not just your mathematical or coding abilities, it’s your “bilingualism” between engineering and business – the abilities to contextualize real world business needs when working on technical projects, and to communicate technical concepts to diverse audiences. With international customers from a diverse array of industries, C3.ai offers a rich set of opportunities to develop domain expertise and client relationship management skills.
1. Focus on Machine Learning
You may notice that the term “data science” means vastly different things and entails different types of work at different companies, ranging from anything from writing SQL queries, to running A/B tests, to writing data engineering pipelines for ingesting data. At C3.ai, the data science team’s primary focus is on machine learning. Period. Thanks to the C3 AI Suite technology, the majority of your time will be spent developing machine learning algorithms and applications, not data cleaning or munging. You won’t ever need to write a single SQL query either. This means that we spend a majority of our time brainstorming and experimenting with features, testing out different model classes, evaluating model performance, and developing custom algorithms when needed.
Even if you're not a deep learning or NLP expert, there’s plenty of opportunity and encouragement to develop expertise. You’ll be surrounded by graduates from top universities with diverse areas of domain expertise. For example, our chief data scientist, who is also a professor of computer science at Carnegie Mellon, gave the data science team a private 2-day lecture on the latest techniques in deep learning.
2. End-to-End Ownership on a Central and Critical Team
On the C3.ai data science team, you’re expected to be a data scientist, a software engineer, and a consultant all in one. Not only do you develop your own machine learning models, you also put your own work directly into production in client applications, and you meet with clients directly to understand their challenges and present your solutions.
At many of the companies my classmates and I visited before graduation, we noticed that the data scientists’ work was frequently several layers removed from the client. Often, the data scientist would produce some statistical analysis or prototype, and hand off their work to another stakeholder, where it might or might not be used to inform next steps or put into production. Because you own the process soup to nuts at C3.ai, you get to enjoy every bit of the sense of accomplishment that goes with putting your work directly into production, where it immediately impacts your clients.
In addition, thanks to the platform technology of C3.ai, we are able to design, develop, and deploy algorithms quickly, often starting from a raw data dump from the customer and evolving into a fully-functioning predictive analytics application within 6-12 weeks. So, fear not - your brilliant algorithms will not be sitting on a shelf for years, languishing until forgotten!
3. Exciting Customers with Global Presence
Now is a particularly exciting time to join C3.ai. The company still is and feels like a small startup but has reached a stage of product-market fit where we are rapidly acquiring well-known US and international customers in an expanding array of industries including healthcare, aerospace, financial services, utilities, and manufacturing. Since data scientists regularly meet with clients to understand their businesses and discuss solutions, my teammates often fly to visit clients around the world, in places like Australia, France, and Finland.
The three points above were by far the most important factors contributing to my decision to join C3.ai. But there are many others worth mentioning:
- Experienced team: In Silicon Valley, where 1-2 years is considered a normal tenure, all the senior members of the data science team have been with C3.ai for 3-5 years. Not only is this evidence of the compelling work that keeps us here, it also means that for every new member of the team, we have a strong pool of senior data scientists who can serve as mentors.
- Humble coworkers: Another important dimension in evaluating a job opportunity is whether you think you’ll get along with your co-workers. Given the impressiveness of my coworkers’ backgrounds, I was immediately struck by the level of humility. There’s no superstar culture on this team.
- Flat hierarchy: Because C3.ai is still very small, the hierarchy is flat, so people can focus on doing interesting work, rather than posturing for promotion.
- Internationally diverse: The data science team and broader company is very internationally diverse. I have colleagues from France, Mexico, China, Sweden, India, Iran, and Kazakhstan.
- Strong leadership: And of course, having a CEO and board of directors with a clear vision and an impressive track record of growing and guiding successful companies is important too.
I’ve been on a journey of my own – and it took me a long time to figure out what my calling was, so my past experiences include working internationally in finance, software engineering, and data science at companies such as Morgan Stanley, Google, and Airbnb, as well as smaller startups and organizations in the public and non-profit sectors. One consistent observation I took away from all those experiences is that what gets me excited to go to work every morning over the long term isn’t the pay, the perks, or the brand of a company; it is whether the work is inherently interesting and impactful. My time so far at C3.ai has completely reaffirmed my choice; I come to work every day excited to solve previously unsolvable problems – and that is what matters the most for me.