Cega | Smart Contract Engineer / Solana Engineer | Remote | Full-time | https://angel.co/company/cegafi/jobs/2002748-solana-engineer

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Cega is building the next evolution in DeFi derivatives by bringing exotic derivative capabilities to crypto. Come build with our team of former options traders, experienced developers, and ex-Y Combinator founders. We just launched out of stealth and are backed by top crypto-native VCs.

We are looking for a self-driven engineer to join our core engineering team and build out significant components of our backend including buy-side and sell-side management, pricing, settlement, and clearing processes.

Re: skills, we're looking for an engineer with 3+ years of commercial Rust expertise and ideally 1+ years of blockchain experience (personal or commercial). We're also looking for skilled engineers with C/C++/Golang experience who believe they can learn Rust and Solana development.

We're really excited to share more about Cega with you! If any of this sounds interesting and you want to learn more, please shoot me a note at: win@cega.fi

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Keywords: crypto, web3, defi, derivatives, Solana, options

Orbiter anomaly detection is for any DB (e.g. Postgres, Snowflake) and metrics that business/product teams tend to track such as transaction conversion %, user growth, add item to basket %, etc.

Amazon Cloudwatch anomaly detection is for AWS resources & apps, and covers infra metrics like resource utilization, app performance, ops health.

In terms of the anomaly detection capabilities -- both are using similar machine learning processes to detect metric issues automatically!

P.S. If you get curious about the details of our solution, we have a 2 minute video demo ;) Cheers! https://www.youtube.com/watch?v=R7P_M6j0P2A

Thanks for replying. That's a good demo. However, I don't necessarily agree Cloudwatch is only for infra metrics. Theoretically, you could send any metrics to CW and leverage the anomaly detection feature. Given it aggregates data over time and you could lost granularities of your data, that's probably not a good idea for business centric data. Then I found AWS QuickSight (https://aws.amazon.com/quicksight/features-ml/?nc=sn&loc=2&d...) which seems to have a similar feature parity?

Thanks for the tip - haven't heard of Maxly before but will do some research

Thanks! We'll rolling out slowly (kinda Superhuman onboarding style back in their old days) so definitely hope to get in touch with you soon :)

Also re: narrowing down what's causing the drop, that's definitely on the roadmap. We know teams have playbooks of things to check when they know something looks wrong, so we should be able to productize & automate this

Was this ML attribution model output explainable / deterministic? I've seen some really complicated marketing attribution models in the past and hear it was something of a never-ending battle to understand and arrive at the "right" model.

I believe it is explainable as I didn’t hear anything fancy about the model being built. It’s been tested and proven to cut marketing spend quite a bit while delivering the same results. A patent has also been filed.

You are spot on that sometimes we just overcomplicate models and sometimes it’s best to go with something explainable and deterministic but less accurate as opposed to more accuracy but complicated.

Thank you! There's definitely a lot of growth and potential in this space and we're really excited too. We're focused on intelligent monitoring and alerting for metrics that the user cares about & defines. We also automate the diagnostic playbooks that teams use today after detecting an issue (eg check data, check user segments, check geographies, etc.) Outlier seems to focus on insights and less on monitoring/alerting. They comb through data to surface 4-5 "unexpected insights" about your customers or business every day in a FB feed-type product.

Hi! (Founder of Outlier.ai here) You are right, our platform is designed to produce the most important insights from massive amounts of data, without requiring human supervision/configuration. It is most useful in applications when there is too much data to set up guardrails, or the teams don't know what guardrails to create. Our typical customers are very large consumer businesses who have data spread across dozens of systems and need to ensure they never miss important emerging trends or problems.

We are not an alerting or monitoring system, so I don't think you'd use us for the same applications as Orbital. The typical users of Outlier are the business users ranging from executives to business operations who want to make sure they are asking the right questions about the business.

Orbital looks like a great product, good luck in building your business!

Thank you! We're actually a team of Canadians too (but have been living/working in SF Bay Area) :D Always great to see more applications for data science - best of wishes to you too!

Woah this is awesome. How did you guys resolve the false alarm issue wrt power plants?

Hey generatorguy - this is a really interesting use case so thanks for sharing. I imagine our modeling / monitoring / alerting capabilities can extend to power plants but will need to understand the data better. The common types of business and product metrics that our customers look for include user growth, cancellation rates, call failure %s, all of the above by different geos, etc. Happy to chat more if you'd like to shoot me an email (I'm winston[at]getorbiter.com)

Ah very interesting, and agree on the immaturity of alerting/time-series changes for current BI reporting tools. Would be great if you could send me more info about what you're thinking about tracking & also hear more about how the PoC you guys are thinking of. Would you mind sending me a note to winston[at]getorbiter.com?