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In the early days of 2020, Johns Hopkins, the CDC, the WHO, and a host of other public organizations banded together in collaboration. They were on a mission to ensure the world had real-time information to a virus that would forever chance the course of this year and the years to come. Which is great for those families with a computer in every home or every person with smartphone access. But what about the rest of the world? How do you ensure those people without access to basic needs lives can be improved? A health non-profit using AI and Machine Learning is aiming to do just this. But the Data is vast and the sheer numbers of people need to be corralled by someone into something the computers can read and make decisions on. Who would have thought Public Research and Data Science would come together in such a manner and in such an important time? Three Benefits of Data Science and Machine Learning in Healthcare According to a seminar given in September 2019, two research scientists explained to the CDC the promises and challenges using Big Data for public health initiatives. After explaining a few definitions and making correlations, the focus was soon on the benefits. The focus of Machine Learning is to learn data patterns.From the initial focus, patterns can then be validated to ensure they make sense.These patterns and validation of patterns can find links between seemingly uncorrelated factors such as the relationship between one’s environment and their genetics. To the scientists working with these scenarios, the decisions seem simple. Yet, when it comes to explaining them to laymen like policymakers, there can be a shift in understanding. This shift can lead to arbitrary and different findings which can affect medical decision making. Why? Could it be using Random Forests in linking the data could be confusing? Data Classification is Not as Cut-and-Dried as a Work Flow or Org Chart If someone shows us a work flow or organizational chart, we understand immediately each task to be done in which order or who reports to whom. But in trying to link uncorrelated bits of information using decision trees, it can seem more like abstract art, more subjective than direct. Yet, it is those correlations which answer the bigger questions brought to bear by Research Scientists, Public Health Researchers, the Data Scientists, and AI working together to see the bigger picture. Decision trees, ultimately, are the great classifier. But there are a few things which need to be in place first. Yet, in the random forest model it’s not just one decision tree, it’s many. This is definitely a case where, if you done right, you will see the forest for the trees and at the same time be able to determine patterns in those trees. A bit counter-intuitive, but this is what stretches our minds to see correlations and patterns we might not see otherwise, don’t you think? So, what do you need to help make predictions? Two Important Needs to Help Make Predictions Predictive power. The features you employ should make some sense. For example, without a basic knowledge of cooking, you can’t just throw random items from your refrigerator into a pot and expect it taste good. Unless of course, you’re making soup and all you have to do is add water.The trees and their predictions should be uncorrelated. If you’ve ever seen M. Night Shymalan’s Lady in the Water, there’s a little boy who can ‘read’ cereal boxes and tell a coherent story. A predictive coherent story. This is the layman’s version of random forests, their predictive nature, and ultimately, the scientists who can ‘read’ and explain the patterns. If you're looking for your first or next role in Big Data, Web Analytics, Marketing & Insight, Life Science Analytics, and more, check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, contact us at (415) 614 - 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to email@example.com.
01. October 2020
One of the things I like to most is to meet our candidates face-to-face. Because most them are local, it’s simple. We call them in and the traditional interview process begins. But, sometimes, the best person for the job or the clients themselves aren’t local. Enter Zoom or Skype or any such communication method where you can see the person you’re talking to. While it’s a step in the right direction, it’s not the complete step. Why? Because you can’t pick up on those subtle clues you might miss, if the meeting isn’t in person. Going The Extra Mile for the Right Placement One of my colleagues recently shared a story with me. She’d been working with a candidate via Zoom for a placement in another State. Though the candidate and the client were both in the area, she wasn’t. The communication with the prospective candidate felt right, but she just wasn’t sure. To ensure she was making the right placement, she traveled to meet them. She wanted to meet the candidate to get a better understanding of him before she was able to successfully place him. Fortunately, it wasn't on the other side of the country, but, it was definitely something that needed to happen. In today’s hyper-digitized world, it's important to remember that the ability to meet in person is an advantage. From the Client’s Side From an office perspective on the client’s side, an in-person meeting offers further advantages. The client can see how the candidate will interact in the actual environment of their business. A birds’ eye view of how the candidate handles themselves in the cultural atmosphere of the business, if you will. In sharpening their focus, the client can also see how a candidate’s appearance, point of view, and communication side affect their performance at the interview and beyond. We make these snap judgements without realizing it, but they’re important. And you can’t really get a good idea of the person over the phone or via email as it can occasionally be difficult to read a candidate’s intentions. At Harnham, we have tried to spearhead the interaction point of view for our own relationships. One of the most unique aspects of our business our dedication to the people we place and our clients we serve. So, navigating data-driven trends with our face-to-face culture finds a distinctive focus as we enter the Age of Data 2.0. A Shift Toward Pipeline Experience With U.S. office locations in both New York and San Francisco, we have a variety of clients from startups to Fortune 500. So, to say one thing is definite in one place or another is a stretch. But, there is a trend, here in New York for professionals with pipeline experience or Machine Learning model development. On the flip side, a growing trend in the San Francisco market has most of their clientele looking for a Machine Learning Engineer profile within the pipeline development lifecycle. So, while we’re (New York) a little bit behind, it’s a trend I’m seeing on both sides of the spectrum within the last six months or so. Though it’s not exactly the unicorn employee, clients seek, there is shift toward higher level oversight. Someone who will be responsible for the entire pipeline. Demand remains high for a field still facing a data shortage. Though the U.S. still lags behind the U.K. and Europe, it’s catching up. As businesses focus on their data strategies in the new year, below are a few things to consider before you hire or accept. Top 3 Questions to Ask Before You Hire From the client’s side determine and the role you want to fill. Ask yourselves the following: What’s the objective of the role you’d like to fill? What is the goal?What contribution do you want from the person in that role?What is your timeline to have that person on board? What happens if you can’t fill the role within your timeline? Top 3 Questions for Mid-to-Senior Level Candidates Did you list the business impact of your list of accomplishments? Can you communicate as easily with your Data team as you do with the Executives? Clients are looking for a mix of technical understanding and the ability to communicate to technical and non-technical audiences. Are your projects keeping you engaged creatively? When was the last time you were given a new initiative, new project, or new client to partner with? If not, then it may be time to search or perhaps consider a contractor role for a fresh perspective. If you’re interested in AI, Big Data or Digital and Web Analytics, we may have a role for you. Check out our current opportunities or contact one of our expert consultants to learn more. For our Mid-West and East Coast Team, call (212) 796 - 6070 or send an email to firstname.lastname@example.org. For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com.
13. February 2020
High speed trains in Florida. Driverless cars in Arizona. National grid union agreements. All these and more are working to create a more smoothly operating system of infrastructure. While privacy laws and transparency vie for attention at every level of government in the US, cities have taken the onus of using data to make decisions. The functionality a critical infrastructure society is built on – railroad tracks, flare stacks, power lines – has been brought together by robotics and AI. The decentralization of intelligence, cloud systems which remotely control Industrial IoT, and AI are just a few of the ways in which 2019 will be a breakout year for Distributed AI. New York City Uses Data to Alleviate Damage Risk to Buildings In their race to stay ahead of Big Data, they may also find ways to improve they might never have discovered without it. New York City has limited staff who can analyze its million properties and incorporate analytics to discern fire risk considering past risk and building traits. City coding has therefore become more important than ever to alleviate potential risk. Philadelphia Focuses on City Interaction with its Residents Evidence-based decision making has debuted in Philadelphia’s GovLabPHL, a multi-agency collaboration. Together, they are centralizing and digitizing records making information easier to share among agencies that historically kept information to themselves. With everything in one place, they can provide city services to their residents much more effectively and efficiently. Florida’s First High Speed TGV Train Rolled out late last year, this high-speed train travels from Miami to West Palm Beach with plans to branch into Orlando and Tampa soon. America’s first high speed passenger train in years will help alleviate road traffic, noise pollution, and more. Data collected may include best safety measures, business practices, and economic value to the city and its residents as money shifts from car buying to rail ticket purchases. The Ethics of Data and Potential Risk of Bias Gaining insights into human behaviors, ease of transportation, and predictive information to curb damage to buildings and other city properties are all important to a smart city’s infrastructure. But, data is, after all, input by humans and isn’t infallible; falling prey to natural biases. Researches and analysts caution decision-making from computer-based algorithms isn’t perfect and should be considered with discretion. For example, the rise in AI, face recognition software, traffic cams, and statistics currently on file may hold a prejudice against certain ethnicities based upon their developer’s biases. This is especially glaring in criminal behavior predictions and as such, policymakers need to think critically and to not take technology at face value. After all, those inputting the data are human, and our biases have a way of seeping into our information. In 2019, AI systems are no longer the robotic machines once shown in movies as something to fear. Today, vendors who build these systems must not only focus on the value provided, but also consider moral foundation of their service. It’s important to understand exactly why and how data will be collected and with whom it will be shared. As cities and businesses continue to catch up, this knowledge is necessary for long-term viability, credibility, and transparency. Trust is a crucial element of data strategy. See Through Cities – Transparency is Key City governments and researchers are working to lessen discriminatory outcomes by turning to transparency. Major cities such as Philadelphia and New York have opened up their websites and invited the public to examine information and their methods of interpreting the data. New York implemented a task force to study how the city uses data and its goal is to present in December of this year ways the city should assess its automated decision-making for transparency, equity, and opportunity. This is a pivotal year for cities to understand their urban ecosystems. Understanding challenges such as traffic, pollution, parking and inefficiency of movement in urban areas may help realize how, when, and where people are moving. With core infrastructures in place, movement may be reduced. In addition, mobility will become efficient and lessen people’s need to move around for better jobs and/or housing. AI is the tool to help cities gain visibility into this type of data. It will enable not only visibility, but also foster prediction capabilities, and provide actionable insights to improve our understanding of why, how, and the way we move. If you’re interested in Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 4999 or send an email to firstname.lastname@example.org. For our Mid-West and East Coast Teams, call (212) 796 6070 or send an email to email@example.com.
09. January 2019
This Monday was Labor Day; a tribute to the American worker. Established over a century ago as the Industrial Age got under way, the holiday was meant to showcase their strength, freedom, and leadership. Now, as the Fourth Industrial Revolution ushers in a new age of robotics, automation, and digitization many workers may wonder how they’ll fit into the new workplace. These concerns are being addressed in a land known for its startup and innovation culture. San Francisco. For 3 days, the city will host an Artificial Intelligence Conference focused on how to use emerging AI techniques in real-world implementations. Will Robots Take My Job? Movies and books tend to offer cautionary tales of problems which might occur if rapid changes go unchecked. It’s unlikely, however, that you’ll find yourself fending off a group of Androids or Terminators any time soon. AI, like all forms of progress, is intended to help improve the quality of human life. Imagine, for a moment, life before washing machines, dishwashers, microwaves, and mobile phones. Not only were these items intended to improve lives, but to give people more time for other things like creativity and cognitive thinking. At the AI Conference in San Francisco, Adam Cutler of IBM Design is taking a deeper dive into how human and machine can form meaningful relationships. It is one thing to respond to machines like Alexa or Siri, who are boxes with voices, but what about when there are more Sophias in the world? Sophia is the first ‘robot citizen’ created by Hanson Robotics. She looks, talks, and reasons much like we do. Though repetitive jobs such as manufacturing may be more affected as robots will be able to make things for longer, at a faster pace, there will still need to be oversight. Even here, there will still be a need for humans to make higher level decisions. Humans are able to gauge reactions whereas robots will look only at the data. How Businesses Can Integrate AI into their Enterprise In the world of startup cultures, AI may be seamlessly integrated and built into the culture or around it, but what about legacy companies? Those who have been following the same patterns for 25, 50, or 100 years? In Taming dragons: a breakthrough approach to AI for business leaders, the discussion focuses on how to implement enterprise AI into businesses. Every new technique we introduce is nuanced and massaged to make the pill of change easier to swallow. But, if we grab for every shiny new toy without fully understanding why we need it and how it can help us improve our bottom line, we risk either misusing it or tossing it aside because it didn’t deliver as promised. What Can I do to Futureproof My Job and Myself? Ultimately, the key is to always be learning. Understand the deeper changes and effects of AI in the marketplace and figure out where else you might be able to use your skills. Diversify. Get creative. Determine your strengths, weaknesses, opportunities, and threats. See if you can’t spin those threats into opportunities. Technical skills are high priority, of course; computer science, Data Science, and Natural Language Processing are all woven into the tapestry of today’s job market. But, as AI capabilities continue to grow, it’s skills such as communication, creativity, empathy, and leadership where humans will continue to have the edge. Want to learn more and see if we have a role for you? Check out our current vacancies. See where Harnham can take you. For our West Coast team, call (415) 614 4999 or email us at firstname.lastname@example.org. For our Mid-West and East Coast teams, call (212) 796 6070 or email us at email@example.com.
04. September 2018
Data drives business and Data Scientists are in high demand. Employers know hiring the right talent is the key to a healthy bottom line now and in the future. As the year comes to a close, many are hurriedly finalizing their fourth quarter numbers, while others think ahead to January’s New Year resolutions. In the first of our 2-part series, we’ll focus first on how and where employers find and retain talent. Next, we’ll focus on how employees can improve their chances to land one of the hottest jobs for 2018.In our 2017 salary guide, we can help you find the best talent at the right price. According to our latest big data conference 76 percent of employers are seeking to grow their analytics team; learn how to find and keep top level data and analytics employees for your business.Where the Data Scientists areWhile every state has some concentration of data science professionals, the hotspots for deep analytic talent are found in large metro areas such as New York, New Jersey, Boston, Washington D.C., Chicago, and San Francisco. Of the five largest metropolitan areas, San Francisco tops the list for deep analytic talent. As tech companies move their Silicon Valley startups from San Jose to San Francisco, employers are upping their game to find and retain big data professionals. An estimated 65 percent of San Francisco startups are less than eight years old offering the perfect opportunity to grow a data science team from the ground up. Beyond the unicorn - What employers look for in a data scientistCEOs are looking for problem-solvers in technology-rich environments. Problems are like people, everyone is different and with no unifying common language to identify competencies in job postings, attracting candidates needs to be approached differently.Rather than look for unicorns, that one mythical creature which encompasses it all, employers are building data science teams. Data science is more than simply looking at numbers and determining algorithms, it requires creativity and non-linear thinking; it’s looking at numbers and applying it to people. The more diverse skills and backgrounds, the greater insight can be brought to analytics. One key skillset is for data professionals to be able to tell a story with data.Competencies needed for data analytics roles often include strong credentials in both education and experience. The nature of data science requires a high bar of educational experience with employers typically interested in a college degree and three to five years’ experience with about a third of job postings requiring an M.A. or higher. But, just as data scientists may be tasked to think outside their comfort zone, so too must employers. Demand is often much higher than supply, so employers must think more creatively and strategically about finding talent. Many businesses often look inward to identify staff with potential to learn new skills. Best Practices for Retaining TalentWhether employers begin in-house or seek outside candidates the goal is to ultimately keep such talent. Integrating data science across the company and keeping data scientists engaged in meaningful work are a few ways to retain top talent. Good data scientists are in high demand and competition is fierce. To keep them motivated and excited to stay, best practices include offering support, a sense of ownership, and a strong purpose. Support can be as simple as making sure your data science team has the tools they need to succeed such as the correct software to an investment in education. Some companies may even offer opportunities to meet with other academics and data scientists from other companies to build a team’s skillset with new ways of doing things and helping them to keep with the trends in the sector. Data scientists don’t work in a vacuum. Recognition and a sense of ownership work hand-in-hand to ensure a deeper understanding of the problem and opportunity to present their analyses to the decision makers. Ultimately, motivation lies in the opportunity to solve challenging problems and by engaging their minds with interesting data sets and questions to solve keeps them mentally sharp and more engaged with the company.Companies everywhere need data talent to help them grow and improve their bottom line, but companies with more complex problems and continual data flow have the best chance of attracting deep analytic talent.If you’re interested in building your data and analytics team, we may have the data professional for you. We specialize in Data and Analytics recruitment and always have a wide range of Data Science vacancies at both junior and senior levels.For the East Coast and Mid-West teams please call 212-796-6070, or email firstname.lastname@example.org.For the West Coast team call 415-614-4999 or email email@example.com.
21. November 2017
Looking to ramp up your career prospects in 2018? Then consider data science. As a Data Scientist, you’ll be filling a much needed role for businesses industry-wide and will be in what a recent article called “this century’s hottest career”. Last week, in the first of our 2-part series, we talked about how employers find and retain top data science talent. In this article, we’ll focus on how prospective employees can improve their chances in the market.As the year comes to an end, revenue growth is expected to double for a third of Fortune 500 companies. But, it’s not just the big boys who’ll need Data Scientists and be growing data science teams. More often than not, you’ll be more likely to get your foot in the door through one of the millions of startups and small to medium businesses. In San Francisco, one of the hiring hotspots for data professionals, 65% of businesses are startups less than eight years old. Basic Skills and ToolsWhile degrees both undergrad and higher education abound, close inspection of job descriptions requires at the least a bachelor’s degree for aspiring and junior level professionals. Advanced positions may require advanced degrees, though the basics and experience count a bit higher. Application of theory is key, so knowing the basic skills help to set up a solid foundation on which to build a career.Python, R, and SQL; nope, it’s not an eye chart. These are a few of the basic statistical programming language you’ll need to know. In addition to these languages, a knowledge and understanding of basic statistics; while useful in machine learning applications, it can also help define, design, and evaluate experiments for decision makers. And last, but not least, a solid grasp of linear algebra and multivariable calculus, round out the basics of a data science skills toolbox. But, what to do with all that data; how do you translate it to explain customer behaviors and how the customer experience can be improved upon? A data scientist who can crunch the numbers, then tell a story is the penultimate employee for any business. It’s this disparate combination which has led to the creation of data science teams. Tell Me a StoryKnowing complex math, statistics, algorithms, and computer languages are basic components of data science, but how do you explain your findings to the decision makers. Companies need data-driven problem solvers, but translating analysis requires a different type of thinking. Think beyond the numbers and consider what is important to the company to help them test an idea or develop a new product. What isn’t important? How should you interact with your team? What about those outside your team such as engineers or product managers? Knowing the kinds of questions to ask and understanding the business can help guide how information is communicated. The ability to communicate for both technical and non-technical audiences is the next step in the process to improve your chances in the market. Data Scientists who can describe their findings and how various techniques work; help decision makers visualize the data as well as the principles behind the information. Looking AheadIn 2011, the McKinsey Global Institute (MGI) report estimated a shortfall of data scientists in the U.S. of between 140,000 and 190,000 by 2018. As we approach 2018, it is now estimated millions of people will be needed to translate data scientist’s work to the organization. Though it’s been suggested that domain experts obtain data analysis knowledge to act as translators, a data scientist with a deep knowledge of their company and its business, who can translate their findings, will be a boon to their organization.If you’ve set a new year’s resolution goal to raise the bar on your career and are interested in big data, check out our current vacancies here. Harnham specialize in Data and Analytics recruitment and have opportunities at both junior and senior levels. For our West Coast team call 415-614-4999 or email firstname.lastname@example.orgFor the East Coast and Mid-West teams please call 212-796-6070, or email email@example.com.
11. November 2017