zhangwins

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: [email protected]

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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 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.

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.

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?

Ah I can empathize with you here (as a former DS) -- we had incidents in the past that were data pipeline / instrumentation changes causing bad data which then caused metric drops (versus a real product issue, but they nonetheless caused a loss of confidence in data).

We think there are a number of diagnostic features that could be helpful here (to be built!). Teams today run playbooks to root cause issues when metric drops happen. We should be able to take that playbook and automate it. Say, Orbiter identifies an abnormal change in Metric X. The team is then probably analyzing sub-funnel metrics Y and Z, or looking at various dimension cuts to isolate the issue. Maybe they're also checking data quality by comparing the count of event volume vs. count of user IDs vs. count of device IDs, etc. If we run all of these diagnostic checks when Metric X drops, we could give the team insight into what we know is OK vs. not OK.

Woohoo! Thanks so much. Looking forward to getting in touch soon :D

Thanks! Looking forward to getting connected. We've heard SEO-specific use cases come up with some of the other companies we've worked with too -- you basically need to find out the exact time that your SEO ranking saw a material change cause it's usually driven by something that shipped at that time. Otherwise takes a long time to get back the traffic from GOOG