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Posts from the ‘machine learning security’ Category

Machine Learning Engineer Is The Best Job In The U.S. According To Indeed

  • Machine Learning Engineer job openings grew 344% between 2015 to 2018, and have an average base salary of $146,085.
  • At $158,303, Computer Vision Engineers earn among the highest salaries in tech
  • The average base salary of the 25 best jobs in the U.S. according to Indeed is $104,825, and the median base salary is $99,007.
  • Agile Coach is the highest paying job with an average base salary of $161,377.
  • 9 of the top 25 jobs in the U.S. this year are in tech fields according to Indeed.
  • Five jobs are heavily dependent on applicants’ Artificial Intelligence (AI) skills and expertise.

These and many other insights are from this Indeed’s The Best Jobs in the U.S.: 2019 study released this week. Indeed defined the best jobs as those experiencing the fastest growth measured by the increase in job postings between 2015 and 2018, in conjunction with those offering the highest pay using a baseline salary of $75,000. Indeed’s best jobs of 2019’s data set is available here in Microsoft Excel.

Key insights from Indeed’s ranking of the best jobs of 2019 include the following:

  • At $158,303, Computer Vision Engineers earn among the highest salaries in tech according to Indeed, followed Machine Learning Engineers with a base salary of $146,085. The average base pay of the nine tech-related jobs that made Indeed’s list is $122,761, above the median salary of $99,007 for the entire group of the top 25 jobs. Indeed’s top 25 jobs for 2019 are illustrated below in descending salary order with the median salary providing a benchmark across the group. Please click on the graphic to expand for easier reading.

  • Three of the top 10 fastest growing jobs as measured by percentage growth in the number of job postings are in tech. From 2015 to 2018, job postings for Machine Learning Engineers grew 344%, followed by Full-stack developers (206%) and Salesforce developers (129%). In aggregate, all nine technology-related job postings increased by 146% between 2015 and 2018. The graphic below illustrates the percentage of growth in the number of postings between 2015 and 2018. Please click on the graphic to expand for easier reading.

  • Comparing average base salary to percentage growth in job postings underscores the exceptionally high demand for Machine Learning Engineers in 2019. Technical professionals with machine learning expertise today are in an excellent position to bargain for the average base salary of at least $146,085 or more. Full-stack developers and Salesforce developers are in such high demand, technical professionals with skills on these areas combined with experience can command a higher salary than the average base salary. The following graphic compares the average base salary to percentage growth in job postings for the years 2015 – 2018. Please click on the graphic to expand for easier reading.

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10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning to fine-tune their selling and service strategies. Source: iStock

Bottom Line: AI and machine learning are enabling omnichannel strategies to scale by providing insights into the changing needs and preferences of customers, creating customer journeys that scale, delivering consistent experiences.

For any omnichannel strategy to succeed, each customer touchpoint needs to be orchestrated as part of an overarching customer journey. That’s the only way to reduce and eventually eliminate customers’ perceptions of using one channel versus another. What makes omnichannel so challenging to excel at is the need to scale a variety of customer journeys in real-time as customers are also changing.

89% of customers used at least one digital channel to interact with their favorite brands and just 13% found the digital-physical experiences well aligned according to Accenture’s omnichannel study. AI and machine learning are being used to close these gaps with greater intelligence and knowledge. Omnichannel strategists are fine-tuning customer personas, measuring how customer journeys change over time, and more precisely define service strategies using AI and machine learning. Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning for example.

Omnichannel leaders including Amazon use AI and machine learning to anticipate which customer personas prefer to speak with a live agent versus using self-service for example. McKinsey also found omnichannel customer care expectations fall into the three categories of speed and flexibility, reliability and transparency, and interaction and care. Omnichannel customer journeys designed deliver on each of these three categories excel and scale between automated systems and live agents as the following example from the McKinsey article, How to capture what the customer wants illustrate:

The foundation all great omnichannel strategies are based on precise customer personas, insight into how they are changing, and how supply chains and IT need to flex and change too. AI and machine learning are revolutionizing omnichannel on these three core dimensions with greater insight and contextual intelligence than ever before.

10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

The following are 10 ways AI & machine learning are revolutionizing omnichannel strategies starting with customer personas, their expectations, and how customer care, IT infrastructure and supply chains need to stay responsive to grow.

  1. AI and machine learning are enabling brands, retailers and manufacturers to more precisely define customer personas, their buying preferences, and journeys. Leading omnichannel retailers are successfully using AI and machine learning today to personalize customer experiences to the persona level. They’re combining brand, event and product preferences, location data, content viewed, transaction histories and most of all, channel and communication preferences to create precise personas of each of their key customer segments.
  2. Achieving price optimization by persona is now possible using AI and machine learning, factoring in brand and channel preferences, previous purchase history, and price sensitivity. Brands, retailers, and manufacturers are saying that cloud-based price optimization and management apps are easier to use and more powerful based on rapid advances in AI and machine learning algorithms than ever before. The combination of easier to use, more powerful apps and the need to better manage and optimize omnichannel pricing is fueling rapid innovation in this area. The following example is from Microsoft Azure’s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.

  1. Capitalizing on insights gained from AI and machine learning, omnichannel leaders are redesigning IT infrastructure and integration so they can scale customer experiences. Succeeding with omnichannel takes an IT infrastructure capable of flexing quickly in response to change in customers’ preferences while providing scale to grow. Every area of a brand, retailer or manufacturer’s supply chain from their supplier onboarding, quality management and strategic sourcing to yard management, dock scheduling, manufacturing, and fulfillment need to be orchestrated around customers. Leaders include C3 Solutions who offers a web-based Yard Management System (YMS) and Dock Scheduling System that can integrate with ERP, Supply Chain Management (SCM), Warehouse Management Systems (WMS) and many others via APIs. The following graphic illustrates how omnichannel leaders orchestrate IT infrastructure to achieve greater growth. Source: Cognizant, The 2020 Customer Experience.

  1. Omnichannel leaders are relying on AI and machine learning to digitize their supply chains, enabling on-time performance, fueling faster revenue growth. For any omnichannel strategy to succeed, supply chains need to be designed to excel at time-to-market and time-to-customer performance at scale. 54% of retailers pursuing omnichannel strategies say that their main goal in digitizing their supply chains was to deliver greater customer experiences. 45% say faster speed to market is their primary goal in digitizing their supply chain by adding in AI and machine learning-driven intelligence. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).

  1. AI and machine learning algorithms are making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.

  1. Combining machine learning-based pattern matching with a product-based recommendation engine is leading to the development of mobile-based apps where shoppers can virtually try on garments they’re interested in buying. Machine learning excels at pattern recognition, and AI is well-suited for creating recommendation engines, which are together leading to a new generation of shopping apps where customers can virtually try on any garment. The app learns what shoppers most prefer and also evaluates image quality in real-time, and then recommends either purchase online or in a store. Source: Capgemini, Building The Retail Superstar: How unleashing AI across functions offers a multi-billion dollar opportunity.

  1. 56% of brands and retailers say that order track-and-traceability strengthened with AI and machine learning is essential to delivering excellent customer experiences. Order tracking across each channel combined with predictions of allocation and out-of-stock conditions using AI and machine learning is reducing operating risks today. AI-driven track-and-trace is invaluable in finding where there are process inefficiencies that slow down time-to-market and time-to-customer. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).
  2. Gartner predicts that by 2025, customer service organizations who embed AI in their customer engagement center platforms will increase operational efficiencies by 25%, revolutionizing customer care in the process. Customer service is often where omnichannel strategies fail due to lack of real-time contextual data and insight. There’s an abundance of use cases in customer service where AI and machine learning can improve overall omnichannel performance. Amazon has taken the lead on using AI and machine learning to decide when a given customer persona needs to speak with a live agent. Comparable strategies can also be created for improving Intelligent Agents, Virtual Personal Assistants, Chatbot and Natural Language (NLP) performance.  There’s also the opportunity to improve knowledge management, content discovery and improve field service routing and support.
  3. AI and machine learning are improving marketing and selling effectiveness by being able to track purchase decisions back to campaigns by channel and understand why specific personas purchased while others didn’t. Marketing is already analytically driven, and with the rapid advances in AI and machine learning, markets will for the first time be able to isolate why and where their omnichannel strategies are succeeding or failing. By using machine learning to qualify the further customer and prospect lists using relevant data from the web, predictive models including machine learning can better predict ideal customer profiles. Each omnichannel sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies.
  4. Predictive content analytics powered by AI and machine learning are improving sales close rates by predicting which content will lead a customer to buy. Analyzing previous prospect and buyer behavior by persona using machine learning provides insights into which content needs to be personalized and presented when to get a sale. Predictive content analytics is proving to be very effective in B2B selling scenarios, and are scaling into consumer products as well

Using Machine Learning To Find Employees Who Can Scale With Your Business

  • Eightfold’s analysis of hiring data has found the half-life of technical, marketable skills is 5 to 7 years, making the ability to unlearn and learn new concepts essential for career survival.
  • Applicant Tracking Systems (ATS) don’t capture applicants’ drive and intensity to unlearn and learn or their innate capabilities for growth.
  • Artificial Intelligence (AI) and machine learning are proving adept at discovering candidates’ innate capabilities to unlearn, learn and reinvent themselves throughout their careers.

Hiring managers in search of qualified job candidates who can scale with and contribute to their growing businesses are facing a crisis today. They’re not finding the right or in many cases, any candidates at all using resumes alone, Applicant Tracking Systems (ATS) or online job recruitment sites designed for employers’ convenience first and candidates last. These outmoded approaches to recruiting aren’t designed to find those candidates with the strongest capabilities. Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed and its unbiased data-driven approach selecting candidates works. Resumes, job recruitment sites and ATS platforms force hiring managers to bet on the probability they make a great hire instead of being completely certain they are by basing their decisions on solid data.

Playing The Probability Hiring Game Versus Making Data-Driven Decisions

Many hiring managers and HR recruiters are playing the probability hiring game. It’s betting that the new hire chosen using imprecise methods will work out. And like any bet, it gets expensive quickly when a wrong choice is made. There’s a 30% chance the new hire will make it through one year, and if they don’t, it will cost at least 1.5 times their salary to replace them. When the median salary for a cloud computing professional is $146,350, and it takes the best case 46 days to find them, the cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. The average size of an engineering team is ten people so only three will remain in 12 months. These are the high costs of playing the probability hiring game, fueled by unconscious and conscious biases and systems that game recruiters into believing they are making progress when they’re automating mediocre or worse decisions. Hiring managers will have better luck betting in Las Vegas or playing Powerball than hiring the best possible candidate if they rely on systems that only deliver a marginal probability of success at best.

Betting on solid data and personalization at scale, on the other hand, delivers real results. Real data slices through the probabilities and is the best equalizer there is at eradicating conscious and unconscious biases from hiring decisions. Hiring managers, HR recruiters, directors and Chief Human Resource Officers (CHROs) vow they are strong believers in diversity. Many are abandoning the probability hiring game for AI- and machine learning-based approaches to talent management that strip away any extraneous data that could lead to bias-driven hiring decisions. Now candidates get evaluated on their capabilities and innate strengths and how strong a match they are to ideal candidates for specific roles.

A Data-Driven Approach To Finding Employees Who Can Scale

Personalization at scale is more than just a recruiting strategy; it’s a talent management strategy intended to flex across the longevity of every employees’ tenure. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring and growing talent that can support their growth goals and strategies. Eightfold’s approach makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. Personalization at scale has succeeded in helping companies find the right person to the right role at the right time and, for the first time, personalize every phase of recruitment, retention and talent management at scale.

Eightfold is pioneering the use of a self-updating corporate candidate database. Profiles in the system are now continually updated using external data gathering, without applicants reapplying or submitting updated profiles. The taxonomies supported in the corporate candidate database make it possible for hiring managers to define the optimal set of capabilities, innate skills, and strengths they need to fill open positions.

Lessons Learned at PARC
Russell Williams, former Vice President of Human Resources at PARC, says the best strategy he has found is to define the ideal attributes of high performers and look to match those profiles with potential candidates. “We’re finding that there are many more attributes that define a successful employee in our most in-demand positions including data scientist that are evident from just reviewing a resume and with AI, I want to do it at scale,” Russell said. Ashutosh Garg, Eightfold founder, added: “that’s one of the greatest paradoxes that HR departments face, which is the need to know the contextual intelligence of a given candidate far beyond what a resume and existing recruiting systems can provide.”  One of the most valuable lessons learned from PARC is that it’s possible to find the find candidates who excel at unlearning, learning, defining and diligently pursuing their learning roadmaps that lead to reinventing their skills, strengths, and marketability.

Conclusion

Machine learning algorithms capable of completing millions of pattern matching comparisons per second provides valuable new insights, enabling companies to find those who excel at reinventing themselves. The most valuable employees who can scale any business see themselves as learning entrepreneurs and have an inner drive to master new knowledge and skills. And that select group of candidates is the catalyst most often responsible for making the greatest contributions to a company’s growth.

Zero Trust Security Update From The SecurIT Zero Trust Summit

  • Identities, not systems, are the new security perimeter for any digital business, with 81% of breaches involving weak, default or stolen passwords.
  • 53% of enterprises feel they are more susceptible to threats since 2015.
  • 51% of enterprises suffered at least one breach in the past 12 months and malicious insider incidents increased 11% year-over-year.

These and many other fascinating insights are from SecurIT: the Zero Trust Summit for CIOs and CISOs held last month in San Francisco, CA. CIO and CSO produced the event that included informative discussions and panels on how enterprises are adopting Next-Gen Access (NGA) and enabling Zero Trust Security (ZTS). What made the event noteworthy were the insights gained from presentations and panels where senior IT executives from Akamai, Centrify, Cisco, Cylance, EdgeWise, Fortinet, Intel, Live Nation Entertainment and YapStone shared their key insights and lessons learned from implementing Zero Trust Security.

Zero Trust’s creator is John Kindervag, a former Forrester Analyst, and Field CTO at Palo Alto Networks.  Zero Trust Security is predicated on the concept that an organization doesn’t trust anything inside or outside its boundaries and instead verifies anything and everything before granting access. Please see Dr. Chase Cunningham’s excellent recent blog post, What ZTX means for vendors and users, for an overview of the current state of ZTS. Dr. Chase Cunningham is a Principal Analyst at Forrester.

Key takeaways from the Zero Trust Summit include the following:

  • Identities, not systems, are the new security perimeter for any digital business, with 81% of breaches involving weak, default or stolen passwords. Tom Kemp, Co-Founder, and CEO, Centrify, provided key insights into the current state of enterprise IT security and how existing methods aren’t scaling completely enough to protect every application, endpoint, and infrastructure of any digital business. He illustrated how $86B was spent on cybersecurity, yet a stunning 66% of companies were still breached. Companies targeted for breaches averaged five or more separate breaches already. The following graphic underscores how identities are the new enterprise perimeter, making NGA and ZTS a must-have for any digital business.

  • 53% of enterprises feel they are more susceptible to threats since 2015. Chase Cunningham’s presentation, Zero Trust and Why Does It Matter, provided insights into the threat landscape and a thorough definition of ZTX, which is the application of a Zero Trust framework to an enterprise. Dr. Cunningham is a Principal Analyst at Forrester Research serving security and risk professionals. Forrester found the percentage of enterprises who feel they are more susceptible to threats nearly doubled in two years, jumping from 28% in 2015 to 53% in 2017. Dr. Cunningham provided examples of how breaches have immediate financial implications on the market value of any business with specific focus on the Equifax breach.

Presented by Dr. Cunningham during SecurIT: the Zero Trust Summit for CIOs and CISOs

  • 51% of enterprises suffered at least one breach in the past 12 months and malicious insider incidents increased 11% year-over-year. 43% of confirmed breaches in the last 12 months are from an external attack, 24% from internal attacks, 17% are from third-party incidents and 16% from lost or stolen assets. Consistent with Verizon’s 2018 Data Breach Investigations Report use of privileged credential access is a leading cause of breaches today.

Presented by Dr. Cunningham during SecurIT: the Zero Trust Summit for CIOs and CISOs

                       

  • One of Zero Trust Security’s innate strengths is the ability to flex and protect the perimeter of any growing digital business at the individual level, encompassing workforce, customers, distributors, and Akamai, Cisco, EdgeWise, Fortinet, Intel, Live Nation Entertainment and YapStone each provided examples of how their organizations are relying on NGA to enable ZTS enterprise-wide. Every speaker provided examples of how ZTS delivers several key benefits including the following: First, ZTS reduces the time to breach detection and improves visibility throughout a network. Second, organizations provided examples of how ZTS is reducing capital and operational expenses for security, in addition to reducing the scope and cost of compliance initiatives. All companies presenting at the conference provided examples of how ZTS is enabling greater data awareness and insight, eliminating inter-silo finger-pointing over security responsibilities and for several, enabling digital business transformation. Every organization is also seeing ZTS thwart the exfiltration and destruction of their data.

Conclusion

The SecurIT: the Zero Trust Summit for CIOs and CISOs event encapsulated the latest advances in how NGA is enabling ZTS by having enterprises who are adopting the framework share their insights and lessons learned. It’s fascinating to see how Akamai, Cisco, Intel, Live Nation Entertainment, YapStone, and others are tailoring ZTS to their specific customer-driven goals. Each also shared their plans for growth and how security in general and NGA and ZTS specifically are protecting customer and company data to ensure growth continues, uninterrupted.

 

 

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