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Job trend analysis marks growth of data science, AI roles




The demand for these sought-after positions continues to grow in importance, according to analysis of job openings for March 2021 conducted by LHH.


Image: iStock/metamorworks

Many tech workers find themselves on the precipice of change: their organizations may decide to continue remote work for the foreseeable future, they may adopt a hybrid work schedule or they’ll return to on-premises full time. But while a recent study showed that most employees said they’ll stay in their current positions, they also said they expected a greater return from their employers, which means if they don’t get that “return,” they’ll be on the job market. 

SEE: Hiring Kit: Technical Recruiter (TechRepublic Premium)

Many organizations were hit hard by the impact of the pandemic and, coupled with the safety protocols required for workers who do go into the office, they might not be able to accommodate employees’ demands. This represents potential for the tech industry, with pros looking for new positions and companies looking for new talent. LHH, formerly Lee Hecht Harrison, global provider of talent and leadership development, career transition and coaching, analyzed job openings for March 2021, and its findings can provide insight for tech pros, whether they want to remain in their jobs or if they might consider “putting feelers out.” 

More for CXOs

While the LHH Job Bulletin (where the insights can be found) covers many industries, it does devote a portion to tech news. Here’s a look at what is covered in the report.

This week’s 10 most wanted skills based on job postings 

  1. Communication, 646,575  
  2. Dedication, 455,651
  3. Analysis, 409,243
  4. Leadership, 404,484
  5. Collaboration, 386,936
  6. Scheduling, 358,769
  7. Operations, 356,270
  8. Innovation, 350,042
  9. Written communications, 293,720  
  10. Verbal communications, 288,513

Top tech jobs in top 15 job categories

LHH noted that LinkedIn recently highlighted the top 15 job categories based on demand and growth and offered clients a look at where some of the tech-related jobs landed on that list.

No. 6 Digital marketing: COVID-19, while 99.9% awful, caused a big spike in online shopping, which in turn created a demand for marketers well versed in reaching people online. Digital marketing hires grew nearly 33% in 2020 from 2019. Job titles in this category include digital marketing specialist, social media manager, marketing representative and search engine optimization specialist. The salary range is $48,000 to $96,000 per year.

No. 9 Digital content: The demand for online entertainment went stratospheric in 2020. People sheltering at home binge watched and were hungry for content. Digital content creators were in demand, too, and grew 49% in 2020. Job titles include content coordinator, writing consultant, podcaster and blogger. The salary range is $46,000 to $62,400 per year.

No. 11 Software: With many employees sent to work remotely, being online was everything in 2020. Specialists needed to keep the online world up and running grew nearly 25% in 2020. Job titles in this category include web developer, full-stack engineer, front-end developer, and game developer. The salary range is $77,500 to $104,000 per year.

No. 13 User experience: The swift move to online everything in 2020 meant there was a need for experts who specialize in the interactions between people and the digital world, growing 20% in 2020. Job titles in this category include user-experience designer, product design consultant, user interface designer, and user experience researcher. The salary range is $41,600 to $65,000 per year.

#14 Data science: With everyone reliant on data and its capabilities, experts were in high demand. Hiring for data science positions grew nearly 46% in 2020. Job titles in this category include data scientist, data science specialist, and data management analyst. The salary range is $100,000 to $130,000 per year.

#15 Artificial intelligence: The massive, pandemic-induced employment shifts, layoffs, and business disruptions in 2020 resulted in companies looking to artificial intelligence as a way to keep up with increased demand, while safeguarding their businesses from future disruptions. AI hiring grew 32% in 2020. Some of the job titles in this category include machine learning engineer, artificial intelligence specialist, and machine learning researcher. The salary range is $124,000 to $150,000 per year.

Top 5 tech jobs with fastest-growing salaries 

Apart from the top tier of healthcare, the tech industry offers some of the highest salaries among industries. LHH analyzed popular roles and revealed the percentage of growth between 2019 and 2020.

  • Cybersecurity analyst–16.3% growth
  • Data scientist–12.8% growth
  • DevOps engineer–12.2% growth
  • Technical support engineer–8.2% growth 
  • Cloud architect/engineer–6.3% growth

Companies with tech job openings, according to LHH

  • Novacoast, a cybersecurity company, will expand in Wichita, Kansas. The company will open a Security Operations Center initially hiring 60 employees with plans to expand over the next few years.
  • Pendo, a startup in North Carolina, prevailed during a pandemic-stricken year. The company plans to hire 400 more employees this year to fuel that growth as it invests heavily in its presence overseas and looks to nab more large customers to its platform. There are currently 169 open roles.
  • Infosys will hire 300 workers in Pennsylvania as part of its local hiring strategy in the U.S. Infosys will recruit for a range of opportunities across technology and digital services, client administration and operations.
  • Zones, an IT company, is hiring for advanced technology executives across the U.S.
  • HCL Technologies specializes in IT services and consulting. It delivers innovative technology solutions built around digital, Internet of Things, cloud, automation, cybersecurity, analytics, infrastructure management and engineering.
  • TEKsystems has opportunities for technical PM, product manager, SW developer, QA, mobile developer, Python developer, DevOps engineer, site reliability engineer, video streaming engineer, cloud engineer, security engineer, data center technician, and more.

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Australian FinTech company profile #122 – Unhedged




1. Company Name: Unhedged

2. Website:

3. Key Staff & Titles: Peter Bakker – Founder & CEO, Mike Cohen – Co-Founder & COO, Glen VanBavinckhove – CTO, Jeremy Beasley – Growth, Jeremy Machet – Growth, and 6 others who are building like crazy

4. Location(s): Melbourne and Sydney

5. In one sentence, what does your fintech do?: Unhedged uses AI to deliver algorithmic returns to the everyday investor

6. How / why did you start your fintech company?: Being an Algotrader and working with rich people I got annoyed that these advanced tools were not available to my friends. When I looked up the returns of robo-investors I got really annoyed and thought: there must be a better way

7. What is the best thing your company has achieved or learnt along the way (this can include awards, capital raising etc)?: Raised 500K in 3 days which was faster then I ever raised before.

8. What’s some advice you’d give to an aspiring start-up?: Watch your cashflow: companies die of lack of cash, not lack of ideas

9. What’s next for your company? And are you looking to expand overseas or stay focussed on Australia?: Lauchinh the fund in April/May, a crowd fund raise in June and launching the retail product in July….

10. What other fintechs or companies do you admire?: Finserv (most stable earnings and growth), Blackrock: amazing money machine. Ellevest: a narrow target markets that works. CacheInvest: fundmanager as a service

11. What’s the most interesting or funniest moment that’s happened in your company’s lifetime?:
An investor transferring 100K without any documentation nor live fund (we returned the cash). We are still wondering how he knew where to transfer to.

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Artificial Intelligence

Flawed data is putting people with disabilities at risk




Data isn’t abstract — it has a direct impact on people’s lives.

In 2019, an AI-powered delivery robot momentarily blocked a wheelchair user from safely accessing the curb when crossing a busy road. Speaking about the incident, the person noted, “It’s important that the development of technologies [doesn’t put] disabled people on the line as collateral.”

Alongside other minority groups, people with disabilities have long been harmed by flawed data and data tools. Disabilities are diverse, nuanced and dynamic; they don’t fit within the formulaic structure of AI, which is programmed to find patterns and form groups. Because AI treats any outlier data as “noise” and disregards it, too often people with disabilities are excluded from its conclusions.

Disabilities are diverse, nuanced and dynamic; they don’t fit within the formulaic structure of AI, which is programmed to find patterns and form groups.

Take for example the case of Elaine Herzberg, who was struck and killed by a self-driving Uber SUV in 2018. At the time of the collision, Herzberg was pushing a bicycle, which meant Uber’s system struggled to categorize her and flitted between labeling her as a “vehicle,” “bicycle,” and “other.” The tragedy raised many questions for people with disabilities; would a person in a wheelchair or a scooter be at risk of the same fatal misclassification?

We need a new way of collecting and processing data. “Data” ranges from personal information, user feedback, resumes, multimedia, user metrics and much more, and it’s constantly being used to optimize our software. However, it’s not done so with the understanding of the spectrum of nefarious ways that it can and is used in the wrong hands, or when principles are not applied to each touchpoint of building.

Our products are long overdue for a new, fairer data framework to ensure that data is managed with people with disabilities in mind. If it isn’t, people with disabilities will face more friction, and dangers, in a day-to-day life that is increasingly dependent on digital tools.

Misinformed data hampers the building of good tools

Products that lack accessibility might not stop people with disabilities from leaving their homes, but they can stop them from accessing pivot points of life like quality healthcare, education and on-demand deliveries.

Our tools are a product of their environment. They reflect their creators’ worldview and subjective lens. For too long, the same groups of people have been overseeing faulty data systems. It’s a closed loop, where underlying biases are perpetuated and groups that were already invisible remain unseen. But as data progresses, that loop becomes a snowball. We’re dealing with machine-learning models — if they’re taught long enough that “not being X” (read: white, able-bodied, cisgendered) means not being “normal,” they will evolve by building on that foundation.

Data is interlinked in ways that are invisible to us. It’s not enough to say that your algorithm won’t exclude people with registered disabilities. Biases are present in other sets of data. For example, in the United States it’s illegal to refuse someone a mortgage loan because they’re Black. But by basing the process heavily on credit scores — which have inherent biases detrimental to people of color — banks indirectly exclude that segment of society.

For people with disabilities, indirectly biased data could potentially be frequency of physical activity or number of hours commuted per week. Here’s a concrete example of how indirect bias translates to software: If a hiring algorithm studies candidates’ facial movements during a video interview, a person with a cognitive disability or mobility impairment will experience different barriers than a fully able-bodied applicant.

The problem also stems from people with disabilities not being viewed as part of businesses’ target market. When companies are in the early stage of brainstorming their ideal users, people’s disabilities often don’t figure, especially when they’re less noticeable — like mental health illness. That means the initial user data used to iterate products or services doesn’t come from these individuals. In fact, 56% of organizations still don’t routinely test their digital products among people with disabilities.

If tech companies proactively included individuals with disabilities on their teams, it’s far more likely that their target market would be more representative. In addition, all tech workers need to be aware of and factor in the visible and invisible exclusions in their data. It’s no simple task, and we need to collaborate on this. Ideally, we’ll have more frequent conversations, forums and knowledge-sharing on how to eliminate indirect bias from the data we use daily.

We need an ethical stress test for data

We test our products all the time — on usability, engagement and even logo preferences. We know which colors perform better to convert paying customers, and the words that resonate most with people, so why aren’t we setting a bar for data ethics?

Ultimately, the responsibility of creating ethical tech does not just lie at the top. Those laying the brickwork for a product day after day are also liable. It was the Volkswagen engineer (not the company CEO) who was sent to jail for developing a device that enabled cars to evade U.S. pollution rules.

Engineers, designers, product managers; we all have to acknowledge the data in front of us and think about why we collect it and how we collect it. That means dissecting the data we’re requesting and analyzing what our motivations are. Does it always make sense to ask about someone’s disabilities, sex or race? How does having this information benefit the end user?

At Stark, we’ve developed a five-point framework to run when designing and building any kind of software, service or tech. We have to address:

  1. What data we’re collecting.
  2. Why we’re collecting it.
  3. How it will be used (and how it can be misused).
  4. Simulate IFTTT: “If this, then that.” Explain possible scenarios in which the data can be used nefariously, and alternate solutions. For instance, how users can be impacted by an at-scale data breach? What happens if this private information becomes public to their family and friends?
  5. Ship or trash the idea.

If we can only explain our data using vague terminology and unclear expectations, or by stretching the truth, we shouldn’t be allowed to have that data. The framework forces us to break down data in the most simple manner. If we can’t, it’s because we’re not yet equipped to handle it responsibly.

Innovation has to include people with disabilities

Complex data technology is entering new sectors all the time, from vaccine development to robotaxis. Any bias against individuals with disabilities in these sectors stops them from accessing the most cutting-edge products and services. As we become more dependent on tech in every niche of our lives, there’s greater room for exclusion in how we carry out everyday activities.

This is all about forward thinking and baking inclusion into your product at the start. Money and/or experience aren’t limiting factors here — changing your thought process and development journey is free; it’s just a conscious pivot in a better direction. While the upfront cost may be a heavy lift, the profits you’d lose from not tapping into these markets, or because you end up retrofitting your product down the line, far outweigh that initial expense. This is especially true for enterprise-level companies that won’t be able to access academia or governmental contracts without being compliant.

So early-stage companies, integrate accessibility principles into your product development and gather user data to constantly reinforce those principles. Sharing data across your onboarding, sales and design teams will give you a more complete picture of where your users are experiencing difficulties. Later-stage companies should carry out a self-assessment to determine where those principles are lacking in their product, and harness historical data and new user feedback to generate a fix.

An overhaul of AI and data isn’t just about adapting businesses’ framework. We still need the people at the helm to be more diverse. The fields remain overwhelmingly male and white, and in tech, there are numerous firsthand accounts of exclusion and bias toward people with disabilities. Until the teams curating data tools are themselves more diverse, nations’ growth will continue to be stifled, and people with disabilities will be some of the hardest-hit casualties.

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giniPredict launches in ANZ for planning and forecasting in small businesses




giniPredict has recently launched as a new solution to give small businesses in Australia and New Zealand faster, more accurate and more powerful planning and forecasting capabilities.

An intuitive, no-code technology that runs on top of familiar software products, giniPredict unlocks the patterns hidden in business data. Making the power of enterprise data analytics accessible to SMBs, it helps them to assess the commercial impact of individual variables, identify the best outcomes, and prioritise investment and resources.

Fung Lim, giniPredict’s General Manager for Australia and New Zealand, said, “Most financial software focuses on measuring the past. But growing a profitable, sustainable business is based on understanding the future. giniPredict brings the speed, simplicity and convenience of consumer apps to business forecasting to make better, faster, more informed decisions.”

Previously, capitalising on the potential of artificial intelligence and machine learning required specialist in-house technical capabilities and the deployment of substantial resources. This effectively excluded most small and medium-sized businesses, which account for 98.5 percent of businesses in Australia, 97 percent in New Zealand and more than half of employment worldwide. giniPredict changes that by putting powerful data modelling technology within reach of every business.

giniPredict integrates with Xero, and with just a few clicks, small business leaders can model a variety of future commercial scenarios based on their historical data. As a special incentive for businesses to trial giniPredict, the first 200 licences in Australia and New Zealand will be provided free of charge for a period of six months. Otherwise, a subscription will cost $19.99 per licence per month.

Up until now, accessing the potential of machine learning to do predictive modelling required resources and technical capabilities most companies simply don’t have. That means the world’s small businesses have been unable to leverage this tech.

giniPredict was built so that snaller organisations can understand their business better through the use of analytics, and run accurate forecasts, model scenarios, and assess the impact of a range of variables to identify the most productive outcomes.

giniPredict plugs into familiar interfaces – integrating with familiar tools and workflows, such as Xero, Google Data Studio and Google sheets.

Fung Lim continues, ‘Growing businesses in Australia and New Zealand are at the head of the pack globally when it comes to adopting cloud business technology, and giniPredict effectively gives them access to a data scientist. It plugs straight into Xero and immediately enables non-technical and non-specialist users to model scenarios, explore options and select better commercial futures.”

gini chose to launch their product in the A/NZ region first, due to high levels of uptake for cloud solutions, the relatively advanced adoption of technology in general and maturity of the digital market.

Businesses interested in the service can find out more at

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Artificial Intelligence

Why data and analytics are critical on the farm




From the introduction of the steel plow in 1837 to the adoption of advanced technologies like GPS, IoT, and AI, farmers have always looked at how technology and innovation can enhance the work they’re doing. This is incredibly important as they are tasked with an enormous job: to create the food, fuel, and fiber for 7.7 billion people worldwide.

With researchers projecting that our population will grow to nearly 10 billion by 2050, the ability to increase crop yields while lowering traditional inputs’ levels is more important than ever. In a typical year, farmers experience many challenges out of their control, such as unpredictable weather, varying soil types, and fluctuating markets. To better manage such variables, farmers rely on data to make timely, informed, and precise decisions.

The Value of Data in Agriculture Today

Nearly every farmer in the world relies on a mix of historical and real-time data to make informed decisions in their fields. A 2020 study by Purdue University of 800 farmers highlights that only a small minority of 7% do not collect any data related to their yield, soil sampling, or satellite imagery. Data collected through AI, IoT, and advanced robotics is vital for farmers to know what’s happening with each seed, each plant, and each machine.

Through various data points captured on the field and in their equipment, farmers continuously explore data for all variable conditions on the farm to ensure operations run seamlessly at a speed and scale unattainable through manual labor. Every year machines are deployed throughout each step of the farming cycle, and as they perform their jobs, they’re gathering a vast amount of information–from planting conditions all the way to crop success. These insights are gathered year-over-year, and that historical data is working in tandem with real-time data to help a farmer make the most informed decision possible. This is incredibly important as agriculture is one of the most unpredictable industries.

Adopting new technologies is also imperative for enabling interoperability between different software and hardware agriculture solutions to process large volumes of data while making businesses more efficient, sustainable, and profitable.

Capturing Data at Every Stage

Data collection on a farm is more complicated than data collection in a strictly office-based business because key data on a farm can come from places humans aren’t physically able to go to or conditions where a human can’t see. That’s why data and analytics capabilities on a farm have had to become so advanced. For example, a farmer must do multiple jobs simultaneously, so through connected machines and screens that provide real-time data, they can get insight into the most critical functions at all times. Additionally, AI helps farmers “see” beyond human capacity, monitor what’s happening in real-time, and gather data that is used to create insights at any point throughout the growing season.

Farmers leverage data to make smart decisions throughout the life of a single crop. Farmers know exactly where each seed goes into the ground, and overall conditions during planting as data-driven planters can vary the rate at which they plant seeds to eliminate overuse – from the target rate and depth of the seed to how hard it to push the seed into the soil. These machines are also able to self-steer themselves, precisely place seeds and develop accurate geospatial data insights. In the next stage, advanced spraying technology treats each plant individually, applying the exact amount of nutrients needed to protect the plant and surrounding plants and soil.

Farmers are then able to monitor the growth of their planted seeds remotely continuously. Once crops are ready to be harvested, the powerful combination of data and technology enables the equipment to precisely separate grains from the rest of the plant without damage to the kernel. With more and more data being collected for each seed in each stage, farmers leverage AI to combine more data points and better understand the impact of each independent decision taken on or off the field.

Additionally, predictive and preventative maintenance is made possible by collecting and monitoring machine data from any farmer opting into that. This, in turn, enables dealers to detect any issues proactively and from faraway locations, providing support in many cases before a farmer even knows there’s a problem. Many updates and fixes can be done over the air, and with these proactive alerts, downtime can be kept to a minimum.

Access to real-time and historical data and advanced automation transform the smart farming industry as more farmers embrace technology like AI and machine learning to aggregate trends, boost innovation, manage risks, save costs, and enhance supply chain management. The availability of smart sensor data enables farmers to make more informed decisions and comprehend in-the-moment conditions that can impact their operations. Sensors also continuously collect more data with time so that farmers can identify recurring patterns, predict future trends, and prioritize necessary changes.

Data’s Role in Farming’s Bright Future

Big data is key to producing quality food sustainably that can feed today’s growing population. The U.S. Department of Agriculture estimated that farms could add $47 to $65 billion annually to the domestic gross economy by implementing “broadband e-connectivity and next-generation precision agriculture technology.” Technology and analytics, combined with a farmer’s experience and determination, have the power to transform a network of fields into an efficient and highly profitable business. Thanks to data and analytics, the future of farming have never looked so bright.

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