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US$170000 - US$190000 per annum

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Philadelphia, Pennsylvania

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This start-up in Philly is growing fast - be a part of building out the team!

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102061/AN0118

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Philadelphia, Pennsylvania

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A well-funded start up is looking to add a Lead Artificial Intelligence Engineer to help improve the medical industry.

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21425-MR9

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Philadelphia, Pennsylvania

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A fast-growing medical imaging start-up

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102061/AN0117

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Boston, Massachusetts

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I am looking for a Lead Software Engineer, this person will be the centerpiece for the growing team.

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103854/jr #JAR1

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US$120000 - US$150000 per annum

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Boston, Massachusetts

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This company is revolutionizing the autonomous vehicle industry by creating a new way to map the surroundings of autonomous vehicles

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32766-mr9

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US$135000 - US$155000 per annum

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Boston, Massachusetts

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Join a growing start-up in Boston!

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82112/ASN0117

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Boston, Massachusetts

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Have the chance to work on software for advanced autonomous systems!

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081120/AN0116!!

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Philadelphia, Pennsylvania

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This start-up is focused on saving lives around the world

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102061/AN0116

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New York

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Do you have strong Statistical Modeling skills in Python and have progressive experience in paid marketing or paid search?

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00005/GL

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US$140000 - US$160000 per annum

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Pittsburgh, Pennsylvania

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A well-funded robotics team is looking to add a Lead SLAM Engineer to their fast growing team

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20644-mr5

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Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out our recent posts below.

Back To Basics In The Business Of Data Science

Though COVID-19 and the US Election still dominates the news, there’s a lot going in the world of Data Science, too. 2020 ramped up efforts in the healthcare industry to combat the pandemic. Cybersecurity is entering a renaissance of sorts as we tackle the misinformation age, but there’s some fun stuff, too. Here a few Data Science trends finding foundation and leverage in 2021. Businesses Kick Data Science into High Gear To stay viable, businesses are kicking their Data Strategy into high gear. From the top down, there will be an estimated increase of Chief Data Scientists to help businesses make critical business decisions from e-commerce to SaaS. Trajectory of Data Analysts Upskilling to Data Scientist  Once relegated to sampling bits of data and leaving others to break it down into workflow, Data Scientists could see a boost of responsibility. The demand for soft skills, upskilling, and cross-training could reduce the need to have Machine Learning and Data Engineers process empowering the Data Scientists to do more. Breaking the Mold With businesses and education moved online, businesses will be challenged to keep employees engaged. Training and education are now available to employees and would-be Data Scientists at home for on-the-job training as they face new technologies being developed, use new tools, and lessened demand on the college degree, but the experience in applying what’s been learned. Machine Learning Gets Smarter AI and Machine Leaning applications will focus on charting algorithms to understand cause-and-effect. But it won’t happen overnight. Teaching and testing machines is intense and time consuming. These technologies might present probability, but can’t determine definites. Yet.  Applying Machine Learning strategies to business problems through systems will focus businesses on finding solutions rather than focusing on building products that aren’t in their wheelhouse. Adding neuroscience and computational neuroscience into the mix for Machine Learning will see these fields grow. Ultimately, Machine Learning and AI are estimated to be the final piece in the puzzle when it comes to Data Science strategies for a variety of industries.  Back to Basics As everyone gets organized in their new ways of doing business, Data Scientists are getting back to basics. Their solving big problems with better tools, technologies, and open-source information now available. The push for open access scientific and medical journals along with the global team environment offers a variety of ways in which Data Scientists can come together to focus on problems more efficiently than anyone else. In other news, projects such as the new James Webb Telescope, the open access drive for scientific and medical journals, and the latest space race information, Data Scientists have been busy getting these projects off the ground as well. Though 2020 took us by surprise in so many ways, we took what we had and ran with it. So, as we enter 2021, we’re on a unique footing from Machine Learning and AI to Data Science with the added boost of nuero-and computational science to employ every tool at our disposal. Businesses have ramped up their efforts and are empowering the professionals in the Data Strategy teams to help them make critical business decisions with an eye toward the future. Data Scientists are getting back to basics while leveraging their skillsets from open access, online education sources, and on-the-job training to solve the big problems we face. And of course, while we have our eyes on the sky when it comes to space exploration this year and our feet on the ground as we work to vaccinate populations against COVID-19, and Chief Data Scientists split their focus to improve business bottom lines, we know demand will remain high for those in the Data industry. If you’re interested in Data & Analytics, Harnham 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, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.

Making It As A Woman In Data Science: An Interview with Ashley Holmes

Meet Ashley Holmes. Senior Data Scientist for a firm working to improve healthcare. Or rather, the healthcare system.                   It’s been an unusual year by all accounts. Most jobs have moved online for the foreseeable future, yet jobless rates climb. Everyone is learning to pivot and accelerating their focus and skillsets. It’s also a time to evaluate where you are in your career and where you want to go. So, from time to time, we find it’s best to hear some stories directly from those in the field.  Ashley's story begins with a desire to become a math teacher which in later years included Computer Science classes. A girl with a talent for math taking computer classes? This is her story: What drew you to Data Science from your original education focus? I’d wanted to be a middle or high school math teacher since I was 12 years old. In college, I discovered part of the math major required students to take one computer science course. I took the computer course my first semester of college, and really liked it. Programming was fun! So, to my Math major, I added a Computer Science minor in which I was the only woman. I recall a course in Operations Research in which we’d used mathematics to answer problems in healthcare by using linear algebra to optimize a design for a staffing schedule. This staffing schedule would be used by surgeons for operating rooms. Who knew there was a field where you could solve healthcare problems with math and Data? Once I knew, I dug in. Enter Binghamton University’s Systems Science and Industrial Engineering Department. Though at the time, Master’s Degrees in Data Science didn’t exist yet. But this program at Binghamton had a concentration for healthcare systems. This concentration had it all – courses for Data Science skills like Statistics, Machine Learning, and Artificial Intelligence.  After some of my own horrifying interactions with the healthcare system in the US, and realizing I could use my skills in Math and Computer Science to improve it, then that’s what I wanted to do.  With a graduate research assistantship from The Watson Institute for Systems Excellence (WISE) at Binghamton University, I found myself in the process engineering department at a large care management organization in New York City. It was there I got some real-world experience using clinical Data collected by the hospital to improve processes and solve problems the company had been facing. I was hooked and so I pivoted from Math Teacher to Data Scientist.  It's been 10 years since you started on this path, it seems, what changes have you seen in women in the field and/or STEM focus of young women still in school?  While R and Python are taught a lot more in required courses, there was no such thing as a Data Science Masters Degree when I was in school. Most of the Data Scientists I know have Mathematics, Computer Science, or Engineering degrees. Though we did some light coding in my grad school courses, most of my real programming skills have come from my graduate research assistantship and various jobs I’ve had. Talk about on the job training! When it comes to women in the field, that has grown significantly thanks to hackathons, events, and groups tailored to encourage women to enter the field.  What Do You Think Now?  In 2018, I heard about a non-profit hackathon in Boston called TechTogether whose mission was to end the gender gap in technology, which I thought was amazing. I’m also now part of a few professional groups for women in STEM that meetup in person and have conferences (pre-COVID) or at least have Slack channels.  These advances for women in technology have been great, but there is still a lot of work to be done. I actually attended a talk yesterday by Melinda Gates (who was herself a computer science major) about how the pandemic is affecting women and girls, who mentioned that in the late 80’s when she was in school, women made up about 35% of computer science majors, whereas now in 2020 it’s down to 20%.  Wait, it's Declined? Why is it Do You Think? I was curious about this too. So, I did some digging to try and find data on this, and came across this NPR article which suggests that the share of women in computer science started falling at roughly the same moment when personal computers started showing up in US homes in significant numbers. It was at this time, computers in homes were mostly for gaming, and "computers are for boys" became a popular narrative. A 1990 study shows that families became more likely to buy computers for boys than for girls, even when their girls were really interested in computers. As those kids got to college, computer science professors were increasingly men, and increasingly assumed that their students had grown up playing with computers at home. Surprisingly, this extended even to the 2010s, because I only had one female professor in my computer science department; the rest were male. Not that they were bad professors by any means, but it seemed to me even then that it was much more difficult for women to break into the profession and actually succeed. Needless to say, I was shocked (and thrilled!) when I first read the book Hidden Figures, and found out about NASA's women computers who were essential to putting human beings on the moon.  I think more stories like this have come out since I was in school...I also remember hearing that Edie Windsor, who was already a hero of mine for her LGBTQ rights activism, was a technology manager at IBM. As these stories have continued to come out, I think more women have been able to see themselves as able to do these kinds of jobs, and that is part of the reason we are on the rebound. Though 2020 has been an unusual year by all accounts, it is also the beginning of a decade. What do you see for the future of women in data science and what has your experience been? With the prominence of social media now, I think it’s becoming much easier to find women in your field to connect with and ask for advice and support, and I think this is true for both young girls potentially interested in data career paths and professionals already in the industry.   What steps would you recommend to young professionals entering the data professional path or those looking to change careers? Any job or networking trade secrets you wish you'd known before finding your current position?  Being part of a community and making connections with other women in the field has been very helpful both personally and professionally. Join a club: Girls Who CodeGirlstartSociety of Women EngineersCheck out conferences like Grace Hopper and Women Impact Tech. Just knowing that there are women out there with jobs that you’ve never heard of can be really beneficial to believing that you can do it yourself. Look at people with the job titles you’re interested in, and see what they’ve done in the past as far as jobs, education, etc. Network and establish relationships with other women in your field. This is a very valuable tool both for getting a job and for general professional support. Take every opportunity to network that you can; I’ve gotten most of my jobs through networking and knowing people.  As a Senior Data Scientist and a woman what challenges do women still face in the industry and what's something surprising you've encountered that helped you grow either personally or professionally? I think women still face a lot of challenges in the industry. Firstly, there are just so few of us. In most of my jobs (except for my current one), Data Science teams are largely made up of men.  Document your accomplishments throughout your job and bring it with you when it’s time to talk promotions and raises. It is absolutely crucial to be able to speak up for yourself and be your own biggest cheerleader. I used to think that the way to advance through a career was just doing excellent work and waiting for someone to notice you and give you a raise or a promotion. I’ve found that isn’t true at all, and if you aren’t talking about your own accomplishments, who else is going to? In that same vein, finding mentors, coaches, and sponsors is critical. Finding someone who has seen your work and can speak about it and you to other people is incredibly important.  Your Best Advice? My best advice is to apply for the job, even if you don’t think you’re 100% qualified. If you’re looking for a role in Data Science, Harnham may have a job for you. Check out our current opportunities or get in touch with one of our expert consultants to learn more.   For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.