Data is an unreliable friend, and hardly anything about it is actually scientific. So what, Data Science?
Over the past 5 years, I have interviewed more than 1,000 candidate data scientists for an apparently highly coveted set of jobs at Evo Pricing. In the process I have learned that the media are portraying a fundamental lie about this profession: throwing data at off-the-shelf algorithms is really not the point.
A fundamental rethink would be appropriate, and it is likely overdue.
At its heart, data science is a noble name for a broad set of number crunching activities that were mostly invented long ago, but recently received a new lease of life from being applied with greatly enhanced technical devices: more data, more processing power, more reasonable outcomes at a cheaper price. …
I’m proud to officially share with you today that Evo is an Open Loop participating company.
What does that mean?
Over the final quarter of 2020, Evo was a part of a team of leading AI companies in Europe brought together by Facebook that dedicated itself to developing a data-driven framework to manage AI risk while encouraging innovation and growth. This project allowed us to assess the risks in our own AI applications more deliberately and then work together to create and test an AI risk assessment framework.
If your eyes are glazing over just thinking about AI policy, you’re not alone. Legal governance and risk management are not the most exciting topics for most data scientists. But if we care about developing innovative AI models and better technology for the future, all data scientists need to prioritise these analyses. Responsible AI requires active engagement with risk issues by all stakeholders, especially those of us working on AI every day. …
My company Evo is expanding, which means we’ve been through a few hiring cycles in recent months, something that will continue throughout 2021. We are looking for data scientists that can hit the ground running and quickly integrate into the team.
And yet, we continuously struggle to find the right people. It’s not that the technical skills aren’t out there. We see hundreds of applications with years of advanced math, statistical modelling experience, coding skills, and comfort with Python libraries like Pandas and Numpy. Unfortunately, many applicants fail to highlight the non-technical skills critical to success as a data scientist.
Yes, you obviously need technical skills to carry out tasks, but you can learn technical skills quickly on the job. If you have a basic understanding of how to code, you can apply those skills to new languages and switch to other environments and libraries without too much trouble. …
It’s the holiday season, and while festivities may be muted publicly, I’ve still been enjoying lighting the tree, drinking mulled wine, and celebrating with loved ones at home.
As Christmas draws near, it’s impossible not to think of Santa — and as the CEO of an AI company that optimizes supply chain, it’s impossible to ignore his looming logistical challenge. Delivering billions of packages around the entire world in one night? That kind of task would challenge even the most sophisticated AI.
This time of year, a lot of AI companies come out with articles explaining how Santa has used their tools to save Christmas or get his presents to good little girls and boys faster. It’s fun to read, but obviously silly. …
2020 was a difficult year for every business. The coronavirus changed every aspect of our operations. One of the biggest trends in data science caused by the Covid-19 pandemic was an acceleration of the integration of data scientists and data-driven analysis into greater company operations. Data was vital to survive and thrive in the chaos caused by Covid.
Because of the increasing importance of data science — or business science as I think it’s more appropriately considered — in retail, data-driven trends in the sector matter to every data scientist. It’s not just for those of us focused on the supply chain, pricing or other retail-adjacent applications. Data science trends in retail portend trends for the field as a whole in the post-Covid world. …
My predictions for Black Friday weren’t good. I warned that the usual draws for Black Friday had disappeared. Deep discounting had already been the primary strategy that companies had been using to try to recover from Covid-19, which meant the massive sales had become normal. Plus, 70% of American adults report struggling to pay bills, leaving less disposable income for Black Friday and Cyber Monday. It looked like price-sensitive customers had temporarily disappeared, making cutting prices less effective than in the past.
I wrote an article forecasting that Black Friday and Cyber Monday were poised to disappoint. After all, over 60% of consumers had made no plans to shop on Thanksgiving weekend in the days before the holiday. …
Today, all software comprises complex systems of code working together in a series of operations to accomplish a wide range of tasks. The way developers define, choose, write, integrate and allow code to interact forms what we call software architecture. Architectures have repeating and distinct code patterns that solve specific tasks within certain constraints.
Think of each group of code that accomplishes a single task as a brick. Historically, each brick depends on those around it and especially those acting as its foundation, as you build up the code into your final masterpiece: the software.
Historically, millions of Americans took advantage of Black Friday and Cyber Monday sales. This made Thanksgiving one of the most important times of the year for retailers. As the internet proliferated Black Friday and Cyber Monday deals abroad, maximising profit this time of year became vital for companies everywhere.
This year, Black Friday sales have become even more fraught. According to McKinsey, deep discounting has been a primary survival mechanism for companies in the wake of the Covid-19 pandemic. Retailers have already slashed prices to Black Friday and Cyber Monday levels with mixed results. On the oft-proclaimed biggest shopping weekend of the year, no one can afford to lose money. …
A friend of mine from business school recently joked that the aftermath of the U.S. election is more likely to give them an ulcer than the months of campaigning. Why? All the news of vote audits. Just the appearance of audit in headlines is causing her anxiety.
As a former management consultant, I know that no word causes executives as much annoyance and stress as audit. There’s a unique set of headaches that this kind of process brings to a business.
Yet audit shouldn’t be a dirty word. An audit is a chance to tackle critical business challenges head-on and reap the benefits of resulting improvements. This is especially true when it comes to data audits. …
Being data-driven is a must. Informed decision-making, especially when powered by automated business intelligence, delivers higher ROI and better business outcomes.
From AI engineering to hyperautomation, more efficient ways to use data across all business areas are at the heart of each of Gartner’s Top Strategic Technology Trends for 2021.
Data-centricity isn’t anymore optional to thrive Post-Covid. It’s a requirement.
Despite this, the Harvard Business Review reports that 77% of executives consider Big Data and AI initiatives their biggest challenge. Even worse, this percentage has grown exponentially over the past few years.
Is this trend likely to continue in the next decade? As companies attempt to convert to a data-driven mindset, they struggle to use their data effectively. A Catch-22 most businesses don’t know how to get out of. …