57 karma · joined June 2, 2014
Looks like normal operations after departing container berth. Concerning is the speed ramping up.
*Company* FreightFlows is a fully remote, well-funded data and analytics startup based in Boston, MA. We track and predict ship movements and cargo flows for the 30,000 largest shipping vessels in the world, a $300bn industry. The world of global shipping is immense, unwieldy, and is in the early stages of undergoing a digital overhaul.
*Role* FreightFlows is growing fast and we're looking for an infrastructure engineer to grow with us. This is a high impact position. You'll be responsible for building the infrastructure that powers our data science platform, and ensuring a seamless process for developers and data scientists.
Our stack includes AWS, Airflow, Spark, and Docker on ECS. We've just started exploring Terraform/CloudFormation, Kafka and possible Kubernetes for future projects a well.
*Requirements* - Strong overall programming skills: able to write modular, maintainable code, preferably in Python - Expertise building code-driven infrastructure on public cloud platforms (preferably AWS with Terraform or CloudFormation) - Expertise building CI/CD pipelines - Experience with Docker and Linux - Experience with SQL - Experience configuring and maintaining relational databases
*Apply* via Linkedin: https://www.linkedin.com/jobs/view/2834330196
via email with resume: hackernews@freightflows.com subject: Infrastructure-Engineer
_What we do_
Maritime transportation accounts for nearly 4% of all global climate change emissions. 77% of tonnage moved on the water happens in bulk ships (ie tankers) and not container ships. These ships operate like taxis, and their idle time, and empty voyages reveal a very inefficient transportation system.
FreightFlows tracks every boat on the water in real-time and predicts what they'll do next. We leverage unique access to hundreds of millions of live data points/day to analyze and optimize seaborne bulk trade (such as oil, gas, chemicals, aggregates, ore and coal).
We're helping shippers and vessel operators to forecast freight partners, helping to secure favorable market pricing and reducing idle time and empty legs for ships.
Our goal is to significantly reduce the impact of climate change emissions in the maritime transportation industry by driving down the cost of trade and maximizing utilization.
_What we need_
We're a small but experienced team, with experience in maritime tech, commodity analytics and machine learning.
We are looking to add a colleague to our Analytics team, who will develop models and build the associated data pipelines to put them into production. This is an excellent opportunity for an engineer who wants to focus on data science or a data scientist who wants to focus more on software development. The team is small and you will have responsibility to create, test, and implement improvements from a roadmap you will help author.
You will learn the nuances of our AWS backend and will work on modeling -across structured historical vessel & port data, geospatial data, and scraped industry data- using primarily python and some SQL. You will be responsible for implementing client-facing databases and visualizations, and will need to stay up to date with various modeling research, as it pertains to our problem set. We are creating a unique solution in our industry, and we value creativity, dedication, and honest skepticism in our development teams. Our market is global and we welcome applicants from anywhere in the world; you will work remotely but must be available for some meetings in US Eastern time.
You are among the first hires after the founding team, so you need to be comfortable with an uncertain future, fast paced development, risk taking, and the excitement that comes with seeing real world problems fixed with what you do every day. Diverse applicants very welcome - if you think you can help, then please get in contact!
_What we do_
Maritime transportation accounts for nearly 4% of all global climate change emissions. 77% of tonnage moved on the water happens in bulk ships (ie tankers) and not container ships. These ships operate like taxis, and their idle time, and empty voyages reveal a very inefficient transportation system.
FreightFlows tracks every boat on the water in real-time and predicts what they'll do next. We leverage unique access to hundreds of millions of live data points/day to analyze and optimize seaborne bulk trade (such as oil, gas, chemicals, aggregates, ore and coal).
We're helping shippers and vessel operators to forecast freight partners, helping to secure favorable market pricing and reducing idle time and empty legs for ships.
Our goal is to significantly reduce the impact of climate change emissions in the maritime transportation industry by driving down the cost of trade and maximizing utilization.
_What we need_
We're a small but experienced team, with experience in maritime tech, commodity analytics and machine learning.
Data Engineer: We need you to be a leader in developing the data processing and analytics pipeline. You need to have knowledge of python, experience with cloud architecture, big data, data structures, core data science algorithms and distributed systems. Ideally, you'll advise and steer the backend data development toward high availability, optimized processing and data delivery.
Software Engineer: front-end software development, React, Django.
You are among the first hires after the founding team, so you need to be comfortable with an uncertain future, fast paced development, risk taking, and the excitement that comes with seeing real world problems fixed with what you do every day. Diverse applicants very welcome - if you think you can help, then please get in contact!
hello@freightflows.com Subject: Hacker News
All in, the larger AIS data aggregators collect about 500 million messages a day from around 150,000 vessels through terrestrial and satellite sources. Access to the full feed, with no delay, is sold for up to $500k/year, though typical deal sizes are around $75k/year. Access to online map-based products usually cost around $10k/year.
<== Used to work for one of the large AIS companies
Map of vessels passing through from Aug 18 - Aug 21 https://imgur.com/a/yLedgc6
Left track passed through Aug 18, right two tracks passed through Aug 20.
Vessel Hex Callsign DJ3767 MMSI 211191450 Vessel SY Malika Callsign DK7486 MMSI 211634590
We're a new startup from Boston focused on the $20 Trillion dollar global seaborne trade market.
FreightFlows provides aggregated, predictive analytics for global trade. While maritime data continues to grow in volume and complexity, actionable insights continue to be elusive. The industry instead deals with inaccurate, stale and biased data from many fragmented sources. The complexity of bringing together trusted, timely information means companies have had to rely on dubious, incomplete market insight and therefore make decisions on anecdotal evidence. FreightFlows uses proprietary data science algorithms to synthesize data into trade truth, predictive analytics and actionable intelligence for traders, shippers, owners and brokers.
Ask me anything.. Matt Morgan, Founder hello@freightflows.com
We're a new startup from Boston (< 6 months old) focused on the $20 Trillion dollar global seaborne trade market.
FreightFlows provides aggregated, predictive analytics for global trade. While maritime data continues to grow in volume and complexity, actionable insights continue to be elusive. The industry instead deals with inaccurate, stale and biased data from many fragmented sources. The complexity of bringing together trusted, timely information means companies have had to rely on dubious, incomplete market insight and therefore make decisions on anecdotal evidence. FreightFlows uses proprietary data science algorithms to synthesize data into trade truth, predictive analytics and actionable intelligence for traders, shippers, owners and brokers.
Ask me anything.. Matt Morgan, Founder hello@freightflows.com
/endrant
I don't care how good a chef at a restaurant is, if it takes me 2 hours to order and wait for food, the silverware is dirty, and the surroundings are miserable, I'm never coming back.