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Deep Learning Data Scientist
Harnham are currently partnering with a market leading business, disrupting their industry and generating substantial impact through the implementation of Deep Learning solutions in their continued growth of a best-in-class Data Science team.
Based out of the East Bay, with panoramic views overlooking the Bay, you'll be working with an innovative, AI first, Data Science team backed by one of the largest PE firms in the world.
If you're someone who wants to work in a collaborative environment, where you'll see your work go into production and make substantial changes, while directly benefiting from the value that you generate. This is the place for you.
YOUR ROLE AS DEEP LEARNING DATA SCIENTIST:
SKILLS AND EXPERIENCE:
A base salary of between $175,000 - $200,000 as well as a performance related annual bonus and a first of it's kind incentive program with potentially limitless upside.
HOW TO APPLY:
Please register your interest in this Senior Data Scientist role by sending your résumé via the' Apply' link on this page.
Data Science, Data Scientist, Deep Learning, Tensorflow, Neural Networks, Big Data, R, SQL, Python, Insight, Analytics, Data, Statistics, Modeling, Machine Learning, Algorithms, Bayesian
US$120000 - US$150000 per year + Equity & Benefits
Harnham are working exclusively with one of New Yorks most talked about Computer Vision start-ups.
US$120000 - US$160000 per year + Bonus & Benefits
Harnham are exclusively partnered with a very exciting Computer Vision start-up in Pittsburgh who are revolutionizing medical imaging.
US$110000 - US$160000 per year + Equity, Bonus, Benefits
An exciting Robotics start-up in Philly are looking to grow their engineering team
US$130000 - US$140000 per year + Remote
Scala Engineer | Remote Opportunity
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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Boston, Massachusetts is once again on the cutting edge of medical research and technology. From Electronic Health Records (EHR) to Machine Learning and predictive modeling of healthcare best practices to Computational Biology; the final frontier of genetic editing. We have come a long way in our quest to understand and improve our quality of life. In the face of cancer research, diabetes, and liver or heart failure, the world of Computational Biology opens the scientific doors to discovery and solution. This is a place for scientists to not only get to the heart of the matter, but to the core of the problem at the cellular level. There is an old adage which states, “when pigs fly”, usually meaning some thing will never happen or is impossible. But what happens when the impossible becomes possible? The jury’s still out, but researchers are making great inroads in developing ways to save human lives using animal organs. Could Animal Organs Help Solve Donor Deficiency? There are over 100,000 patients in the U.S. waiting for a transplant operation and, for many, a this may be their only cure. Yet, our growing population and the sheer number of those waiting has created a donor deficiency of epic proportions. Researchers have been working toward successfully transplanting organs from animals into humans. Not only has their study of stem cell technology grown over the years, but with the advent of bioinformatics, statistics, and Computational Biology, a new possibility has arisen. The chance to not only transplant organs from one species to another, but using another species to host the growing of transplantable human tissue. Getting the Framework Right Computational Biology is a broad discipline honed to a fine point. Using statistical modelling, it builds a wide variety of experimental Data and biological systems to understand algorithmics, Machine Learning, automation, and robotics. Its job is to ask and answer the question of how to efficiently gather, collate, annotate, search for information. But how can it do all this to determine appropriate biological measurements and observations? At the tipping point is the notion that to truly get a good picture of the problem, the frame must be in focus. And it is this, which is the most important task for Computational Biologists to solve before continuing their research. It’s a reminder to step back and look at the problem from another angle and to challenge assumptions turning “what if” on its head. Stretching, bending, and twisting toward a solution that might not otherwise have been thought without a framework in place in order to begin modelling the system. It is in this constant learning phase, Machine Learning applications with parameters set by the biologists, in which new information is processed, analyzed, and understood. This active learning model offers opportunities for applications to learn how to learn and will play a critical role in biomedical research now and in the future. And from this place, the second biggest problem to be solved enters the equation. Now, it’s time to refine the methods of how to solve the problem. Next Steps As exciting as the possibilities are, like all things new, there are challenges. For example, not all animals will fit the bill for transplantation. The idea is to mimic as closely as possible the size and evolution of humans such as pig, sheep, or non-human primates. But, at an even finer point of challenge are our own cell’s reactions and expressions and understanding why they act the way they do. Ultimately, it’s important to be sure information at the individual cell level is inferred with statistical references to verify findings. At the pixel level, not using a fine-tooth comb could mean your conclusions are wrong. If you’re interested in Biostatistics, Bioinformatics, Computational Biology, Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our latest Computational Biology opportunities in our new Life Science Analytics specialism or our current vacancies for additional opportunities. 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.
13. February 2019
Just because pricing deals with numbers, it doesn’t mean it’s exclusive to the financial sector. In our last few posts, we focused heavily on the role of Pricing Analyst, what it is and how to get there. This type of analyst role is more often found in the marketing arm of many companies and might also be known as Behavior Analyst, Customer Analyst, or something similar. However, there is another type of analyst sometimes confused with Pricing Analyst which falls squarely within the Finance sector. These roles might boast titles such as Risk Analyst, Financial Analyst, or Actuary. Often, it isn’t the title that speaks to the particular strengths of one type of role over another; it is the responsibilities and skill sets documented within the job description. Like Pricing Analysts, these professionals deal with numbers and pricing. However, their focus is on models, such as those required for mergers and acquisitions or how to set health insurance premiums looking at risk. Looking for a Low to No-Risk Gig? Actuaries are in high demand. As a profession, it is one of the most diverse and tends to be more open to women and under-represented minorities. Though the focus is often on insurance and pension programs, Actuaries can find work in a number of industries including consulting firms, hospitals, banks, investment firms, and government. As advisors who manage risk portfolios while analyzing historic and current data, these professionals are business-minded people with a mathematical basis. Using mathematics, statistics, and financial theory, they analyze the financial consequences of risk. The Masonic-esque Levels of Becoming an Actuary For individuals who are numbers focused and are interested in using their data, technical, and mathematical skills coupled with business acumen; the role of Actuary might be the perfect fit. However, there are steps or levels which need to follow to enter the profession. These are exam-based and work-experience levels and your salary increase incrementally with each step. To begin, a graduate with a high GPA and one exam under their belt may find the role quite lucrative. Each exam leads to the next level and enters you into an Actuarial Society. Depending on where and what you want to practice will determine which society you’ll sit the exam: Society of Actuaries (SOA) – focus is life and health insurance, pensions, and employee benefits. Casualty Actuarial Society (CAS) – focus is automobile, fire, and liability insurance as well as worker’s compensation. American Society of Pension Actuaries (ASPA) – focus is those in the pension field, particularly in relation to federal and state governments. Each organization has its own exams and competition is fierce. Qualities sought beyond a high GPA and actuarial exam include: Good communication skills High technical ability A wide background from mathematics and statistics to the liberal arts Actuaries and analysts with an eye toward the financial and insurance sectors use their statistical skills to research, network, and connect the dots between discerned variables. The research begins with statistical modeling. Connect the Dots with Statistical Modeling In statistical forecasting models, the information gathered helps analysts make statements about real outcomes which haven’t yet come to pass. The model can then help identify what might influence these variables. An Actuary, Financial Analyst, or Risk Analyst may use a: Merger Model (M&A) – This model is most often used in investment banking and corporate development. Think mergers and acquisitions. After all, someone has to decide the value of each company, then the basis of that value once they’re merged. Complexity varies widely in this model. Budget Model – This model is used in financial planning and analysis and helps set the budget for the coming year and the years to come. Focused heavily on a company’s income, these budgets are designed on a monthly or quarterly basis. Forecasting Model – This model is used to build a forecast of the budget model. Think of it as a building block as companies structure their budget and strategies using one or a combination of these models listed. Sometimes, the forecasting and budget model are combined. Sometimes they’re kept separate. These are only three of the ten types of models used in financial planning and analysis for any number of firms and industries. But, it’s the people behind the numbers who help businesses navigate what is best for their client, customer, and bottom line. An Actuary is just one title those interested in the mathematical and statistical applications for business might find interesting. And like many of those in the Data Science field and higher tech applications, this role is in high demand. Are you the one companies are looking for? If you’re interested in finance, modeling, statistics, 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 email@example.com. For our Mid-West and East Coast Teams, call (212) 796-6070 or send an email to firstname.lastname@example.org.
07. February 2019