In addition to building a great company, this is also an important personal mission. That’s where we specialize and where we can provide the highest quality and most rigorously-vetted talent anywhere. Factored can adequately serve any company that’s growing their AI capabilities by providing them data scientists, machine learning engineers, data engineers, and data analysts. We solve complex data science problems for a wide range of industries like Fintech, insurance, mobile telecommunications, healthcare, retail, manufacturing, consumer marketing, transportation/logistics, and a few others. They just happen to be outside of Silicon Valley, but are still in U.S. companies access to some of the most brilliant engineers and analysts in the world. This shortage of qualified engineers delays projects, delays revenue initiatives, and prevents businesses from growing. IN: It is increasingly difficult in Silicon Valley-and the rest of the U.S.-to hire and retain high-caliber engineers, especially if you’re not a FAANG company. Our engineers are factors that work together and multiply to produce greater results on behalf of our clients and communities. IN: In mathematics, a factor is a combination of numbers that when multiplied together form a greater joint result and product. For every cohort we bring on, the acceptance rate is around 2-3% so it’s actually harder to get into Factored than some Ivy League universities. We rebranded the company as Factored, pivoted away from the training emphasis in our marketing, and refocused around the high caliber excellence of our engineers and the rigor of our technical vetting. More importantly, we and our client companies quickly realized that the quality of our engineers in LATAM was on par, and sometimes above the average for Silicon Valley engineers, and they were capable of solving very complex problems in industries like Fintech and Telecommunications, where we landed our first customers. Our advanced training program was very different, as it was focused on advanced practitioners, real-world production and deployment-level machine learning, and resulted in high levels of proficiency. After listening to the market, we realized that positioning the company as a training school was very limiting as there was a lower perception of “bootcamp” programs at the time. It was still a hypothesis based around training engineers and placing them with clients to do basic data science work. It wasn’t yet a full-fledged company at that point. By serendipity, or fate, I read that the AI Fund was in the early stages of building a company that combined not just my interest in ML, but also my business experience, and lifelong passion for helping Latin America develop beyond natural resources. That led to learning Python and applied machine learning, where I ran into Andrew’s courses. A few years ago, I had an epiphany and realized I didn’t want to be the typical MBA type who didn’t understand technology, so I started learning to code. Israel Niezen: For most of my career in tech, I’ve been on the business side. Israel Niezen, CEO and co-founder of Factored
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