Clean Technology Meets Data Science: News and Articles

So far, we’ve been looking at the trends in the clean technology and data science sector during the pandemic in terms of   companies’ requirements ,   startup activity and acceleration of existing trends in automation, robotics and artificial intelligence . Today, we’ll wrap up our series with a look at how jobs in this sector are holding up and the skills that are increasingly in demand.   First, how has hiring in the sector been impacted?   Like many sectors, hiring in the clean technology and data science sector has slowed as companies and organizations evaluate their status and determine what will be needed in the year ahead. Hiring in many traditional roles in organizations and companies (e.g. environmental consultants, power plant engineers, wastewater treatment scientists, city sustainability officers) has been put on pause or eliminated at this time. These companies are essential organizations and are open, but they are typically operating with a skeleton staff and are still ev

Last time , we looked at how Covid and the restrictions due to the pandemic are impacting the clean tech sector and accelerating existing trends in automation, robotics and artificial intelligence.   Today, let’s take a look at how the pandemic is impacting startups and funding.   For a long time, VC funding has been synonymous with innovation. Think of any of the large companies today - Google, Facebook, Uber, Tesla - they’re all products of the venture capital system. Clean technology saw a spike in VC funding in 2008-2010 and then interest and funding dollars waned after that - mainly because of a large number of bankruptcies and losses to the VC firms involved. Also, VC funding is often referred to as “impatient money” because returns on investments are expected within a relatively short timeframe, usually the life of the fund which is about 7-10 years. However, clean technology firms that rely heavily on infrastructure and hardware often take longer to exit and the returns are not

  As Covid-19 sweeps around the world, we’re beginning to see the economic impacts from shutting down the economy in different countries. We’ve seen recessions and slowdowns before, most recently in 2008 and 2001, but this one feels different in several ways. First, the cause of the downturn is not a result of human antics, but a natural agent - a virus. Second, since we can’t really fix the economic issues unless the pandemic and infection rates are brought under control, the regular economic tools have limited impact. Third, this is probably the first time that the whole world is being impacted within a very short time, unlike the other downturns. This means that there’s no country or region that can act as a buffer or an economic engine for the rest of the world. Finally, it’s the sheer uncertainty in this situation - we’re still learning about the virus and there are a lot of open questions being researched now. For example, how it’s impacting different societies and populations, h

  As the Covid-19 pandemic heats up, many of us are now sitting in our homes because of quarantine and enforced social distance and isolation curfews from local governments. If you’re like me, you’re probably talking to colleagues through Zoom, trying to make meetings work online, missing in-person interactions and doing our best to get work done under really challenging circumstances.   While it’s hard enough managing teams and people with different skills and backgrounds in general, doing so remotely makes it even more challenging! So today, I thought I’d talk about working with teams and professionals from two very different fields - clean technology and data science - and discuss what makes the working relationships between them effective and smooth.   The joy and the challenge of working at the intersection of very different fields is that often professionals from each field clash - world views seem so different that translators are needed to bridge the gap! That makes it difficul

  Have you looked at our  jobs portal  recently? Or explored openings and roles at the intersection of clean technology and data science?   Something strange has been happening over the last quarter - something that we really haven’t seen for the last couple of years. Many positions in large companies and startups that were posted late last year have been reposted again this quarter. And not just that, we’re seeing several openings posted on job sites and go unfilled despite repeated postings and connections with different networks.     So, this month, let’s go a little deeper and see what’s happening in the market.     For the last couple of years, we’ve been seeing a steady growth in roles for data scientists, modelers, program managers, C-level positions in companies in agriculture, energy, sustainability and water sectors. Initially, as we would have expected, we saw a lot of positions in newer companies - companies like Climate Corporation, Opower, Blue River Technology and so on.

  Here's wishing all our readers a very happy new year!  Last year, we started our journey to making data science accessible for people interested in clean technology and solving the problems facing our planet.  We had the pleasure of conducting several workshops and online webinars on different aspects of data science in clean technology. We covered a wide range of topics in our in-person workshops where we discussed data sources in different clean tech sectors, how to build effective algorithms and models including deep learning, and presented uncertainty analyses and business use cases. We also began conducting online sessions in the latter half of the year where we introduced folks to careers and tools at the intersection of data science and clean tech.  As part of our expansion plans in 2020, we're creating an online education platform that focuses on applying data science effectively in clean tech sectors. We're in beta this month and are building our content which wi

  Is data science infallible? If all we had to go on were the breathlessly excited articles published in business magazines and the highly polished releases from startups and large tech companies, it would certainly seem so. Think of the articles that have been published this year with titles like -     “Artificial Intelligence (AI) to replace all jobs by such and such year”, “Machine learning solves problem faster than humans”,     “ Data science shows promise to end world hunger soon” - and so on and so on….  Data science is a relatively new field, but one that combines elements from disciplines that have been around for a while - computer science, statistics, and algebra for example. The difference right now is the sheer power and availability of computational resources like the cloud that allow people to build and run different models and experiment on a scale that we haven’t seen before. And in high-tech companies, we’re also seeing an explosion in the availability of data that al