Lead Data Scientist - Deep Learning

San Francisco, California
US$165000 - US$185000 per year + Competitive Benefits

Lead Data Scientist - Deep Learning
San Francisco Bay Area
$165,000 - $185,000 + Competitive Benefits

The Company

Harnham is working with an e-commerce marketplace that is revolutionizing how high-end goods are delivered to consumers. This company has a remarkable global presence and continues to expand its establishment to new places, with the most-coveted consume goods - It's your chance to work on automating their marketplace!

You will be working with a diverse team of specialists - from fellow data scientists and engineers to consumer experts in the e-commerce market - to optimize and streamline their logistic process. The role you will face involves a profound engagement with deep learning to develop their automated approach to getting the right product to their customer base. The company we are working with is excited to bring on people from diverse backgrounds and are passionate to providing the best consumer experience.

The people you will work for care about what you have to say - they foster an environment of ownership and support to grow within the organization. This organization is making the luxury economy more accessible and sustainable - and this is your chance to join a team that spares no expense on good talent!

The Role

You will:

  • Lead a team of diverse individuals to take on projects designed to improve on a personalized user experience
  • Develop high-end image analytics in a machine learning framework
  • Collaborate with inquisitive and bright individuals like yourself to manage and streamline production
  • Perform high-level analysis of social media data, using innovative machine learning and NLP techniques
  • Communicate with organizational leaders across different verticals to deliver on data-driven goals

Skills and Expertise

You have:

  • Dedicated knowledge in deep learning for image processing and the ability to produce object-oriented solutions to logistical strategy
  • Expertise in python, implementing machine learning techniques using python libraries, like TensorFlow
  • Huge plus! - Key point detection with deep learning experience (KCNN), at a production level
  • Exceptional communication skills - you can effortlessly relate insight to a team of engineers, business operations personnel, and an overseas data lake team!

Benefits

$165,000 - $185,000 + Competitive Benefits

*My client can sponsor employment authorization/visa! *

How to Apply

Please register your interest by sending your CV to Karla Guerra at Harnham via the Apply link on this page.

For more information this role or other Data Science opportunities, please contact Karla Guerra at Harnham.

KEYWORDS
Python, R, Machine Learning, Deep Learning, Image Analytics, Computer Graphics, Big Data, AI, Artificial Intelligence, KCNN, Java, Image Processing, Hadoop, Spark, AWS, SQL, Modeling, Algorithm, Social Media, Social Analytics, Content Analysis, Sentiment Analysis, Data Scientist, Data Science, Scikit-learn, TensorFlow, PyTorch, Keras, Classification, Clustering, Extraction

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52338
San Francisco, California
US$165000 - US$185000 per year + Competitive Benefits
  1. Permanent
  2. Deep Learning and AI

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Tips for your Data & Analytics Resume

Tips for your Data & Analytics Resume

So, you’re pursuing a career in Data & Analytics. The brilliant thing about this is you’re entering a fast-growing industry with the potential for a great salary. But, unfortunately, this also means you’re probably entering into one of the most competitive fields out there right now.  The question is, how can you ensure your resume stands out from the crowd and impresses any potential employer?  Here are some top tips to help boost your Data & Analytics resume. Formatting is important It may seem obvious, but handing over a messy resume with no headings and massive blocks of text is no way to make a good first impression. Research suggests your resume is only looked at for a total of six seconds, so it’s important to make an impact on first glance.  Not only does this entail creating a well-presented document overall, but it also means paying attention to the small details such as structuring your resume to best emphasise the qualities and experience you think speak most highly of your ability to do the job well. This is why utilising a reverse chronological format is sometimes a worthwhile idea. For a highly competitive job in a Data & Analytics related field, where past experience is an important factor, beginning a resume with your most recent experience nearest the top will draw the eye and attention of the hiring manager reading it. Additionally, make sure your skills, qualifications, extra courses and impressive achievements are highlighted and clearly stated within the main body. As such, it’s better to use bullet points wherever possible instead of paragraphs and, consequently, you’ll find your resume a lot more compact and legible; in other words, much more likely to be read and remembered.  Quality over quantity  Having the most aesthetically pleasing resume in the world will mean nothing if the content doesn’t relate to the job you’re applying for. Again, this may sound obvious but it’s always worth combing through your resume to eliminate any irrelevant features and leave more space to talk about the things that matter.  Having a single page summarizing the most impressive contributions in your last role, or the most valuable insights gathered from a particular project you were involved with, is much more valuable than a multi-page essay about your volunteering with a local soccer club five years ago (unless, of course, your role heavily related to Data & Analytics). When introducing yourself, avoid long sentences and pronouns, and use impactful verbs when describing your achievements: for instance, try “instigated” instead of “started” and “spearheaded” instead of “led”. Also be sure to highlight and, where possible, quantify how your previous work in data/analytics benefitted your old company.  Know the value of your skillset It’s worth dedicating a section of your resume just to listing your most valuable skills as they relate to the job you want. However, make sure to be specific when describing your technical skills and experience with whichever tool you’re talking about. State your level of expertise and how you utilized said software to make your knowledge clear to whoever’s reading.  If you’re applying for an entry level position, however, and don’t have much experience or technical skills yet, it’s important to show off whichever skills you already have and how they  will make you a great addition. It’s worth researching which of your more general skills are the most sought after by employers, and then gaining an understanding of which ones best relate to the job you’re trying to get. For jobs working in Data Science, for instance, maths skills, analytical skills and problem solving are well worth mentioning. Ultimately, you want this section to contain a comprehensive, impressive sounding, and accurate, list of your most relevant skills.   If you’re interested in Big Data & Analytics, we may have a role for you. Take a look at our latest opportunities or contact one of our expert consultants to find out more:  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com. This guest blog was provided by check-a-salary. 

Big Data In Politics – Win, Lose, Or Draw

Big Data In Politics – Win, Lose, Or Draw

In the movie Definitely, Maybe starring Ryan Reynolds, there’s a scene in which he must sell tables for a political campaign dinner fundraiser. He makes call after call with no luck. Finally, in frustration, he speaks plainly and finds a connection between the politician and the prospective donor. In an instant, he understands. Make the connection and you can’t go wrong. This is the 90’s version of micro-targeting. Online advertising today has honed targeted Marketing to an art form and it’s infused every industry from Fisherman’s Wharf to Wall Street to Washington. Messages are crafted on detailed profiles of what makes us unique such as hopes, fears, dreams, emotional triggers, and more which is then taken out of the hands of humans. Enter such deep, personal details into automated technologies and you’ll get automated reactions. How did we get here? Ever since Cicero’s brother, Quintus, who approached politics with a do anything to win mindset, we’ve been working toward this point. But, when it comes to technological advances within politics, George Simmel put it best when he wrote around 1915, “the vast intensive and extensive growth of our technology…entangles us in a web of means, and means toward means, more and more intermediate stages, causing us to lose sight of our real ultimate ends.”  What does this mean? It means we have moved so quickly and with such intensity as we push inwards while reaching outward, we get tangled up in our own systems. Before we know it, it’s difficult to separate the means from their ends, and we lose sight of our purpose. In other words, it can be hard to keep our sense of direction with our constant distraction of tasks, systems, and processes. According to Simmel, this would soon morph into what he called a ‘fragmentary character.’ Like a mosaic, we put the pieces back together and assemble the bits to fit our concept of the world.   The Digitizing of Campaigns Traditional campaigning has traditionally looked much like the movie scene mentioned above with phone banks, whiteboards, and handmade signs. But, today, things are changing. Everyone has at least one smart device which can sync information in real time to a range of devices. Algorithms and predictive modeling help reduce the guesswork, though gut feeling and instinct still prevail. At least, for now. Our machines are learning how to learn about us and define what we believe and wish to see by historical Data, or rather our past behaviors. Where psychographic profiling meets micro-targeting. What was once only seen in the Marketing world has now entered politics. Just like marketers want to know what people are interested in, so to do politicians wish to know what voters think. To do this, both industries will study behavioral and attitudinal profiles to help understand a demographic better or discern a gap in the marketplace. In consumer research, companies rely on psychographic micro-targeting to reach smaller groups and individuals. The key question here is to ask is to what extent are politicians prepared to pass laws that restrict their own opportunities to know more about voters. Just as the next generation of voters are coming, so too are the next generation of tools being developed.  One Final Thought… Over the last 20 years or so, we have built an immense Data structure from mobile devices to social media to modelling processes and more. With this kind of connectivity combined with fragmentary media, the use of Data Analysis has a big role to play going forward. If we seek change in our political and social infrastructures, we will have to reimagine the structures currently in place. From algorithmic modelling to AI and Machine Learning, the possibilities for new ideologies has emerged blurring the lines between context and production in which Data underpins capitalism. As those in Data Analytics continue to pursue an uninterrupted (read: non-fragmentary) vision of the world, we find ourselves at a new stage in history of where both looking back and looking forward at the same time informs our future.   Where would you like to go? If you’re interested in Big Data & Analytics, we may have a role for you. Take a look at our latest opportunities or contact one of our expert consultants to find out more:  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

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