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Director, Digital Media Analytics
San Francisco, CA
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Digital Media, Display, Programmatic, Video, Campaign Management, Supervisor, Manager, Leader, Paid Social, Supervisor, Facebook, Twitter, Social Media, Media Analytics, Analysis, Analyst, Portland, Oregon, Facebook Ads Manager, Advertising, Marketing, Agency, Paid Social Media.
US$70000 - US$75000 per year
Los Angeles, California
Fantastic opportunity to take on the analyses for a major automotive client.
US$140000 - US$160000 per year
Los Angeles, California
Opportunity to lead the Paid Search efforts for a community-focused and dynamic ad agency.
US$65000 - US$75000 per year
Los Angeles, California
Looking for experienced paid searchers with an emphasis in analytics!
US$80000 - US$90000 per year
Santa Monica, California
Work with a community-focused and innovative ad agency, leading the digital analytics for one of their biggest clients.
US$110000 - US$120000 per year
Los Angeles, California
A fantastic opportunity to join a fast growth Ecommerce organization as their Manager of Marketing Analytics.
With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
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Ever wondered how your email system knows which emails to show you and which to put in your junk or spam folder? Enter Machine Learning. It learns what you open and read and after a time can differentiate what you ignore, toss, or move to spam. Now imagine that same type of learning in the life sciences. As scientific advances move toward Data and Machine Learning to scale their knowledge, you can imagine the possibilities. After all, as you read this, trends in the life sciences, specifically with an eye toward bioinformatics showcase machine learning such as genome sequencing and the evolutionary of tree structures. Human and Machine Learning with a Common Goal There has been so much data provided over the past few decades, no mere mortal could possibly collect and analyze it all. It is beyond the ability of human researchers to effectively examine and process such massive amounts of information without a computer’s help. So, machines must learn the algorithms and they do so in any number of ways. For the most part, it’s a comparison of what we know, or is already in a databank, with the information we have and don’t yet know. Unrecognized genes are identified by machines taught their function. The Future is Bright Machine Learning is giving other fields within the life sciences both roots and wings. Imagine scientists being able to gain insight and learn from early detection predictions. This type of knowledge is already in play using neuroimaging techniques for CT and MRI capabilities. This is useful on a number of levels, not the least of which is in brain function; think Alzheimer’s Research, for example. The hurdle? It isn’t the availability of such vast amounts of data, but the available computing resources. Add to that, humans will be the ones to check and counter-check validity which can in turn become more time-consuming and labor intensive than the computer’s original analysis. And it’s this hurdle which leads to a caveat emptor, or “buyer beware” of sorts. Caveat Emptor: Continue to Question Your Predictions In other words, how much can you trust the discoveries made using Machine Learning techniques in bioinformatics? The answer? Never assume. Always double check. Verify. But as you do so, know this. Work is already in progress for next-generation systems which can assess their own work. Some discoveries cannot be reproduced. Why? Sometimes it’s more about asking the right question. Currently, a machine might look at two different clusters of data and see that they’re completely different. Rather than state the differences, we’re still working on a system that has the machine asking a different kind of question. You might think of it as a more human question that goes a bit deeper. Imagine a machine that might say something noting the fact that some of the data is grouped together, but if different, it might say while it sees similarities, but am uncertain about these other groups of data. They’re not quite the same, but they’re close. Machine Learning is intended to learn from itself, from its users, and from its predictions. Though a branch of statistics and computer science, it isn’t held to following explicit instructions. Like humans, it learns from data albeit at a much faster rate of speed. And its possibilities are only getting started. Want to see where Bioinformatics can take your career? We may have a role for you. If you’re interested in Big Data and Analytics, take a look at our 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 email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
12. September 2019
Today’s companies know how important it is to add Machine Learning and AI into their business, but without a plan, things can easily go sideways. Hiring Data Scientists for your business involves more than just hoping for the next Facebook or Google success. It’s about moving the needle on your own business. So, how do you do that? Well, first you’ll want to think about what is you want from your business and from there, build a team to help get you there. We know that sometimes these two things pose challenges as well. So, where should you should begin? Ensure you have the right leadership structures in place In their role as Stakeholder, it’s important for top executives to have a clear understanding of where their business is and solid framework for where they want it to go. In other words, it’s important to ask yourself, is your business ready for that sort of growth and transition? Before you say, yes, and start looking for that unicorn employee, there’s a few things you need to know. Data Scientists aren’t magicians. They cannot wave a magic wand and make you ten times profitable overnight. Efficiency will come, but it will take time. In reality, if you don’t have the right Data sets, the right people in place, or the right backing and investment, then it will be a hard road to success and can lead to failed initiatives. So, how do you avoid that and get yourself set back on the right path? Here are a few key steps to consider: Don’t Put the Cart Before the Horse Understand your focus and the why of your business. Align your teams with a clear view of where you are and where you’d like to go in your business. Set clear expectations for yourself and your team.Decide What Your Team Should Look Like Ask yourself, “How much talent do I need?” Well, that depends on how much Data you’ll be working with and where you want those initiatives directed. For example, if you’re building a team just to focus on work recommendation systems, then you’ll need a far smaller team than if you were overhauling an entire platform or product line. Stop Chasing Shiny Objects Be realistic in your expectations as you build your team. So many businesses, when they think of Data Scientist, focus on the word scientist. And their first thought is they’d like to get someone with a PhD from Stanford who’s worked ten years at Google. A couple of things come to mind here, when I hear this and the first is this: When businesses first reach out, they talk to me about how they want someone from Google or Facebook or Netflix, but the reality is 9 times out of 10, you’re not going to be able to access that kind of talent.Be realistic about what sort of talent you can gain access to and ask yourself, if you could get someone from Google, Facebook, or Netflix, why would they leave that job to come work for you? What can you offer that those businesses cannot? It's important to understand the goal here is not to chase the shiny stereotypes, but to have clarity and desire to set up for success those that you do hire. For some businesses, the initial reaction is to just throw Data Scientists at a problem and believe they can fix things or move you forward faster. But like everything you need to put steps in place to get you from where you are now to where you want to be. Learn, Grow, Pivot For most people who don’t come from a Data Scientist background, there are two schools of thought. Data Scientists are bright shiny objects who will fix all of their business problems overnight orA waste of time. To build a successful team, begin by educating those in the dark about what Data professionals are capable of and ensure everyone is aware of what is realistic. It’s important to understand that it may take six months to a year for a business to see any real outcomes. This doesn’t mean things aren’t working. It’s about investing time and not getting itchy, if something doesn’t instantly showcase results. Rethinking Stability The gold watch after 40 years of working for the same company is a thing of the past. Good Data Scientists today, typically stay on for about 20-months, then move on to their next creative endeavor. This can be scary for a world that expects people to stay in positions for four or more years, particularly if you’re not from a tech background. However, the definition of a scientist is someone who researchers, someone who tries to find new ideas and new concepts, so these are people who are naturally inclined towards learning and towards being in new situations and these people get very, very bored very quickly. Understand that if you build your team well, people will move on and drop away. This is a good thing. It means you built a strong team and the ones who have moved on have got you to this place, now you need the next team to help you get to the next level. A constant state of flux is scary, but it can also mean your business is scaling faster, and your team of Data professionals are doing their job to move the needle. Give your team the freedom to learn, give the freedom to work on projects outside of their natural scope to be able to bring value to your business, although even within that year 18-month framework you might not see proof straight away. You need to be ok with the fact that you going to lose people. That typically means you’re doing something good. If you’ve got a Data Scientist who is happy to just stay at your company and work on the same projects for six, seven, eight years, that’s probably a red flag; how will they keep improving? “What Got Us Here, Won’t Get Us There” The needs of your group are going to change and shift and so the people you need are going to change and shift. Again, it’s about being adaptable and being able to ride those waves and change as and when we need to. If you’re looking for more guidance in scaling your business using Data Scientists and building your Data team, Harnham can help. If you’re a candidate interested in Big Data & Analytics, we may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
05. September 2019