trekking101

Koffie Labs | Software Engineer | REMOTE (US/Can) |getkoffie.com

Trucking insurance for the 21st century! This is a relatively antiquated industry with a ton of manual and heuristically-driven processes. Underwriting (assessing risk) is literally done in a spreadsheet (at best). We bring insurance experience, Wall St data science, startup ethos all blended together in the greatest insurtech there is!

Currently looking for 1 full time role with expert Python skills. Ideally Plotly Dash, GCP, flask. We pride ourselves on ETL/data quality.

Role is $130-$160 + benies/options

Pls apply at https://getkoffie.com/

Koffie Labs | Software Engineer | Remote (US or Can) | Full Time

We're an InsurTech focused on trucking insurance. This line of business (like most of insurance in general!) is anachronistic in every way. We bring an experienced team and strong industry relationships. Intersects with logistics, autonomous vehicles and shipping. We're getting our commercial licenses. Currently looking for someone with 2-5 years experience who will intersect with engineering and data science daily. Read more about us the position and apply at https://apply.workable.com/koffie-labs/ and say HN sent you!

Koffie Labs | Data Engineer | NYC (remote in USA/Canada) | Full Time

This is a unique role that offers broad exposure to software engineering and data science. We are eager to find underrepresented candidates who are early in their career and don’t want to choose between engineering or data science.

Working with data science and engineering, you will be deeply enmeshed in the critical nuances of disambiguation, normalization, standardization and other ETL skills. You will also have the opportunity to work in all stages of our software process from researching data sources to developing data pipelines and building business intelligence visualization tools.

Koffie is an insurtech company purpose built for the autonomous vehicle era. We are taking trucking and transportation insurance out of the dark ages, using modern technology to deliver instant policies and capture the use of advanced safety and autonomous technology. Free from legacy systems and inefficient processes, Koffie uses AI-driven predictive models to deliver a reimagined insurance experience for fleets while more accurately pricing its risk.

Learn more/apply: https://apply.workable.com/koffie-labs/

Please say HN referred you!

Koffie Labs | Lead Software Engineer | Brooklyn, NY (Borough Hal) | Full-time, Onsite | $130k-$150k +equity | https://getkoffie.com Koffie is an insurance company purpose built for the autonomous vehicle era. We are taking transportation insurance out of the dark ages by using modern technology to deliver instant policies based on advanced safety and autonomous technology. Free from legacy systems and inefficient processes, our AI-driven predictive models deliver a reimagined insurance experience for fleets.

Our vision is to align incentives across fleets, technology providers, brokers and the automotive industry. By catalyzing the adoption of safety technology, we positively impact road safety and facilitate a more efficient supply chain. We’re backed by top-tier VCs in the fintech and mobility sectors. If you're ready to work obsessively with us to make insurance better, faster, more efficient and build products for the next 100 years of mobility, we want to hear from you. We offer a competitive salary, stock options, unlimited vacation, 100% employer paid health, vision and dental plans, discounted CitiBike/MTA/commuter rail and discounted fitness classes. We believe strongly in diversity of thought which comes from different backgrounds and experiences.

We are looking for savvy Lead Software Engineer to help pioneer a new approach to insurance underwriting. Taking insurance out of the dark ages means a willingness to challenge assumptions and reinvent processes. You will be central to this mission by developing data pipelines and integrating systems responsible for the operations of a modern insurance company.

Lead Software Engineer The ideal candidate is excited at the prospect of building maintainable systems from scratch. You will support our data science and underwriting team and ensure optimal data delivery architecture is consistent throughout ongoing projects. You must be self-directed and comfortable supporting the data needs of multiple teams, systems and products.

More info and to apply, pls visit SO: https://stackoverflow.com/jobs/288504/senior-software-engine...

One person's metadata is another's data! You (may be unintentionally) characterise it as a market where you check boxes on "datasets" to buy, but it's far from that. Monetizing by licensing/selling some kind of log data isn't really interesting--but understanding what signal can be derived and how it can be applied to an opportunity (arbitrage, value added analysis, financial product/insurance etc.... is where it gets interesting.

I've written a bit about derivative uses of data if you are interested: https://post-employment.com/category/business/

GBNST (guilt by non-scientific thinking): Radio waves = sound = speed of sound, therefore wtf sound = light, but now I 'see the light.'

After having a second cup of coffee I did a doh! and realized conflating 'radio' with sound is non-sensical, but I wonder if I'm in the minority thinking this way. Or maybe it's just my non-tech background!

Somebody please explain this line from the post:

Radio waves travel at the speed of light 299 792 458 m/s.

One person's metadata is another's data

What inspired you to start this project? There are numerous open source geocoders and the core problem isn't the software anyway, it's data. I'm often perplexed by people spending time building new things thinking they've uncovered/solved something when researching/talking with others who have domain experience would quickly reveal there may not be a there there.

But why? Forget about the tech. What does this accomplish? What3words is nonsensical for the same reasons- Amazon Prime is not coming to Mongolia anytime soon and it has a lot more to do with little disposable income/economics than logistics.

Fascinating. On my wish list is which SDKs use location in some way. A real pain on iOS to understand permissions (super easy in Android)

My point is I would expect to find a heck of a lot more "journalists" working for a traditional media outlet than in the tech press

Anybody care to run the numbers on j-school grads at 'traditional' publishers v techcrunch et al? I stopped reading techcrunch years ago when it became clear they largely copied & pasted PR. Rarely do they ask competitors/industry pundits to comment, a standard journalistic practice.

You know this is from a hum-drum conference producer, right? The last thing they care about is saving the planet. Unless that gets more people to attend their conferences.

Which by the way are terrible.

Thasos Group / Senior Quantitative Researcher / NYC Based / Full Time ONSITE

WHAT: Want to help redefine macro-economic forecasting for the 21st century? It ends up location data is a pretty good indicator of economic activity. We source and combine billions of geolocation events daily from mobile devices world-wide.

By measuring real-time, aggregate human mobility, we estimate changing fundamentals for companies, industries, and key macroeconomic indicators. The team is world class and founders include Greg Skibiski, Founder of Sense Networks, and Alex "Sandy" Pentland, Head of Human Dynamics Research at MIT.

We don't have any institutional VCs and we're excited to own our destiny. The business is growing and we need more curious and capable minds!

THE ROLE: * Build efficient, scalable models to extract real-time economic insights from novel, large-scale data. * Enhance and develop techniques for normalization, noise reduction, and error identification and correction across many disparate data sources with years of historical data. * Work with portfolio managers to assess financial applications for signals, which may include asset selection, investment timing, and risk control.

Requirements: * Masters degree or PhD in a quantitative field. * 3+ years experience in fields related to financial markets. * 5+ years experience in writing code for data analysis (Python experience is preferred) and applying advanced methods from statistics, machine learning, or related fields. * Self-starter with a demonstrated ability to devise and build end-to-end solutions with minimal oversight.

For more information, please send cv/LinkedIn, GitHub etc... to careers@thasosgroup.com

Thasos Group / Senior Quantitative Researcher / NYC Based / Full Time ONSITE

What: Want to help redefine macro-economic forecasting for the 21st century? It ends up location data is a pretty good indicator of economic activity. We source and combine billions of geolocation events daily from mobile devices world-wide.

By measuring real-time, aggregate human mobility, we estimate changing fundamentals for companies, industries, and key macroeconomic indicators. The team is world class and founders include Greg Skibiski, Founder of Sense Networks, and Alex "Sandy" Pentland, Head of Human Dynamics Research at MIT.

We don't have any institutional VCs and we're excited to own our destiny. The business is growing and we need more curious and capable minds!

The role: * Build efficient, scalable models to extract real-time economic insights from novel, large-scale data. * Enhance and develop techniques for normalization, noise reduction, and error identification and correction across many disparate data sources with years of historical data. * Work with portfolio managers to assess financial applications for signals, which may include asset selection, investment timing, and risk control.

Requirements: * Masters degree or PhD in a quantitative field. * 3+ years experience in fields related to financial markets. * 5+ years experience in writing code for data analysis (Python experience is preferred) and applying advanced methods from statistics, machine learning, or related fields. * Self-starter with a demonstrated ability to devise and build end-to-end solutions with minimal oversight.

For more information, please send cv/LinkedIn, GitHub etc... to careers@thasosgroup.com

I was in the audience and he gave a pretty clear hint that commerce (a la tencent) would be the path forward for monetization

Thasos Group / Senior Quantitative Researcher / NYC Based / Full Time ONSITE

What: Want to help redefine macro-economic forecasting for the 21st century? It ends up location data is a pretty good indicator of economic activity. We source and combine billions of geolocation events daily from mobile devices world-wide. By measuring real-time, aggregate human mobility, we estimate changing fundamentals for companies, industries, and key macroeconomic indicators. The team is world class and founders include Greg Skibiski, Founder of Sense Networks, and Alex "Sandy" Pentland, Head of Human Dynamics Research at MIT. We don't have any institutional VCs and we're excited to own our destiny. The business is growing and we need more curious and capable minds!

The role: * Build efficient, scalable models to extract real-time economic insights from novel, large-scale data. Enhance and develop techniques for normalization, noise reduction, and error identification and correction across many disparate data sources with years of historical data. * Work with portfolio managers to assess financial applications for signals, which may include asset selection, investment timing, and risk control.

Requirements: * Masters degree or PhD in a quantitative field. * 3+ years experience in fields related to financial markets. * 5+ years experience in writing code for data analysis (Python experience is preferred) and applying advanced methods from statistics, machine learning, or related fields. * Self-starter with a demonstrated ability to devise and build end-to-end solutions with minimal oversight.

For more information, please send cv/LinkedIn, GitHub etc... to careers@thasosgroup.com

Really? Nobody wants to comment on this thread?

Ha! There are no general purpose tools--lots are a) overkill or b) not robust enough given your task but two general ones that have served me well (and of course lot of Python scripting for my domain-specific issues):

Refine (formally from ITA/Google, now open source) IBM Watson Analytics - Don't knock it until you try it. Forget the 'analytics' bit but the ETL bit is pretty decent for getting through the basics