Hi HN, I created Sunvoice.me based on my experiences as part of the early team at Venmo, plus as the product manager for accounts receivable software at a B2B cannabis distributor. Looking forward to hearing your feedback!
RankScience (YC W17) | San Francisco, CA | Full Time | On-site only | Salary + Equity + Benefits + 15 day minimum vacation policy
We're hiring for an engineer focused on product at RankScience! Junior- or entry-level experience okay. On-site at RankScience HQ in San Francisco (no remote). Our office is at 17th & Valencia in the Mission!
Why this role matters to RankScience:
- Scalability: This role enables our company to scale to 50 customers, and then to 500 customers, and then 5000 customers.
- Bleeding-edge business model: This role enables our unique business model to have the scalability of a product company while also charging at the price point of an agency.
- Software eating the world: We want to change the way people think about acquiring search traffic, and this role will help our software eat the search marketing industry.
What you will gain from this role:
- Breadth of product exposure: We have lots of software to build to interface with many different types of users or other services. We think that 1-2 years at RankScience right now will offer more learning than 1-2 years at most other companies.
- Distributed systems expertise: This role will work with many if not all of the services that we have in our infrastructure. We expect this role to provide plenty of opportunity to develop broad distributed systems skills.
- Create outsized impact on a growing startup: We’re looking for team members who want to play a huge role in a startup pursuing hypergrowth.
- The ability to create bleeding-edge technology: We have some unique tech that has never been built before, and we want to continue building even more unique tech. After your time at RankScience, you’ll be able to build anything you want.
Responsibilities: Build software to interface with all of the following...
- Our CDN servicing 1,000 requests per second
- Our SEO managers to implement thousands of concurrent A/B tests and automated SEO changes
- Our customers to gain visibility into our tech
- Other services within the RankScience stack
About RankScience: We sell SEO software. We're funded by Y Combinator. We're almost 10 people now and are focusing on building out our product!
Hey 0bfus, we're working on a fancy performance metrics dashboard and status page to answer these questions! Also, we've done extensive research on pageload speed's effect on page rank, and we found that <25ms of incremental latency does not have any impact. We're working on a case study for the latter!
It's a true honor to speak with an SEO A/B testing OG! :)
> How does RankScience play into this?
That's a really great point. Right now, our software either focuses on only pages which are good for SEO, or it runs less frequent experiments on the "bad" pages. We've had a few success stories of turning "bad" pages into good ones and having them become big revenue generators for our customers, but that's not common as I'm sure you can imagine.
We focus on being automated! Our software never stops working to grow your search traffic, even when you're asleep or if you don't want to think about SEO at all.
> Does it integrate with web master tools and google analytics?
Yep!
> Does it help identify page populations that are stable and similar enough to be compared?
Yep!
> How are the different test versions sent to you guys?
For the most part, we own the design of treatments. Some of our customers have their own experiments, but that's a small minority of our customers. Happy to talk more in depth about this if you're interested! [email protected]
> Can we use our own CDN on top of your CDN?
Yes, we encourage this too!
> What about dynamic pages that change over time?
We address this on a case-by-case basis, but our software is designed to be as un-invasive as possible in use cases that we do not expect.
> How big of a sample size is needed to get statistically significant results?
We have to consider several variables here. Sample size is a function of Google's crawl rate on your site, the number of pages you have, and the amount of traffic you receive.
We test everything on-page. This usually includes CTR stuff, like titles and meta descriptions and paragraph text that Google extracts for the meta descript, headers, images, calls to action, even conversion rates.
Correct, we continually iterate based on feedback like rankings and clicks, and we're working on smarter ways to monitor Google's crawler too!
Users are as hands-on or hands-off as they'd like. We usually own the experiments, but some of our customers design their own too.
The number of tests depends on the number of pages and your traffic. Our largest customers have full factorial experiments with thousands of concurrent split tests every day.
Google's algorithm is to reward high quality websites which add value to their users. Our approach to SEO is to help our customers define "high quality" in Google's ever-changing definition and serve their quality to Google's users. This is exactly what Google wants.
(1) We use both humans and software to generate experiments. For customers, it's completely automated.
(2) We look at all primary search metrics (clicks, impressions, CTR, and rankings), with clicks being our main metric. Search results get updated at a pace determined by Google's crawl rate, which varies per site depending on multiple factors including domain authority. We use bayesian structural time series and negative binomial regression models to measure impact and statistical significance to power our data-driven SEO recommendations.