Senior Deep Learning Research Scientist
City of London, London / £50000 - £60000
£50000 - £60000
City of London, London
SENIOR DEEP LEARNING RESEARCH SCIENTIST
£60,000 - £75,000
We have been working with this DeepTech client for over a year, helping them to build out their Deep Learning and Research capabilities. They are on the lookout for a new Deep Learning expert to drive cutting-edge research in real-world scenarios.
Project areas so far have included: Renewable energy, Microchips, Healthcare, Aerospace etc.
The ideal candidate for this role will have experience with Deep Learning, Simulations, 3D Data and fluid dynamics.
In the team, you will have the following responsibilities:
- Building models for deep learning techniques
- Working closely with Data Scientists, MLEs and customers to drive innovation
- Read and assess new scientific papers and models
- Making models more scalable and efficient
- An opportunity to mentor juniors
The successful applicant will have the following skills and experience:
- 3+ years of deep learning experience
- Strong skills in Python, PyTorch, JAX, TensorFlow (or other flow tools)
- Experience with simulations and 3D /2D modelling
- Knowledge of fluid dynamics
- Fundamental knowledge of neural networks
- Worked with Software engineering concepts
- Cloud knowledge (AWS, GCP or Azure)
- MSc or PhD from a strong university in a relevant field (Data Science, Mathematics, etc.)
Get in touch! Register your interest by sending your CV to Joseph Gregory via the Apply link on this page.
Deep Learning, Machine Learning, Data Science, Python, Neural Networks, Cloud, Graph, Meshes, CAD, CFD, Fluid, Turbulence, 3D, 2D, Simulation
Battle Royale: Computational Biologists vs Machine Learning Engineers | Harnham US Recruitment post
From the first genome sequencing in the second revolution to Life Science Analytics as a growing field in the fourth industrial revolution, change has been both welcomed and fraught with fear. Everyone worries about robots, Artificial Intelligence, and in some cases even professionals who have stayed current by keeping up-to-date with trends. And it’s beginning to affect not only “office politics” within the tech space, but even interviewer and interviewee relationships.We’ve seen a growing trend of apprehension between Computational Biologists and Machine Learning Engineers. What could be the cause? Aren’t they each working toward a common goal? It seems the answer isn’t quite so cut and dry as we’d like it to be. Here are some thoughts on what could be driving this animosity. But first, a bit of background.So, What’s the Difference?Computational Biology and Machine Learning are two sides of the same coin; one sets the framework and the other applies what’s been learned. Both use statistical and computational methods to construct models from existing databases to create new Data.However, it is within the framework of biomedical problems as computational problems, that there seems to be a bit of a breakdown. It’s one thing to have all the information and all the Data, but its quite another to know how the Data might interact or affect the health and medications of people seeking help. This is the job of those in Life Science Analytics. Determine through Data what needs to be done, quickly, and efficiently, but at the same time, ensure the human element is still active. A few examples of Computational Biology include concentrations, sequences, images and are used in such areas as Algorithmics, Robotics, and Machine Learning. The job of Machine Learning can help to classify spam emails, recognize human speech, and more. Here’s a good place to start if you’d like to take a deeper dive into the differences between the two or read this article about mindsets and misconceptions.Office Politics in the Tech SpaceCircling back to the concern between Computational Biologists and Data Scientists with a focus on Machine Learning. The latest around the water cooler within the tech space is that those with a biological background who understand Machine Learning are looked upon as dangerous to the status quo. But, as many of our candidates know, it’s important to stay on the cutting edge and if that means, upskilling in Machine Learning so you have both the human element as well as the mathematical, robotic components, then that is more marketable than just having one or the other.The learning curve in biology training within the Life Sciences Analytics space means Computational Biologist with a Machine Learning skillset is best able to apply Data Science and computer science tools to more organic and biological datasets. Someone with just a computer science background may not have the depth of knowledge to understand how these models, systems, and data affect and impact medicine.Computational Biologists who are trained simultaneously in computer science and biology, and are a little heavier on the biology side, see Machine Learning Engineers as a threat because utilizing Machine Learning and other cutting-edge tools could mean their job is on the line.They worry their job will fall by the wayside. That when somebody proves Machine Learning is faster and more efficient the impetus might be why hire a Computational Biologist when a Machine Learning engineer will do?It’s like when a lot of people joke about how robots are going to take over the world and everybody will be out of a job. I think the worry with some folks on the Computational Biology side is that maybe they just aren’t up to date with their training or haven’t kept up with cutting edge of technology.With a Recruiter’s EyeWhile what I’ve seen agrees that, yes, Machine Learning is incredibly helpful and fast and you can get through so much more data. But its still that understanding of biology and chemistry that you will need because you need to be able to understand, for example, how these proteins are going to be reacting with one another or you need to understand how DNA and RNA work, how best to analyze, and what analyzing those things means.On the other hand, just because you know, “oh, this reaction comes out of it”, if you don’t know why that is or how that could impact a drug or a person, then you don’t really have anything to go on. There’s a caveat there.Though there may be concerns among Computational Biologists and Machine Learning Engineers, at both the upper and entry levels, it’s still the technical lead who will say, “we really do need somebody with a biological background because if we get all this Data and don’t really know what to do with it, then we’ll need to hire a Project Manager to converse between the two and that’s an inefficient use of time and resources”. What I hear most often is a company wants a Computational Biologist but they also want someone who knows Machine Learning. But they don’t want to compromise on either because they don’t understand there are limitations to things. We all want the unicorn employee, but we can’t make them fit into a box with too specific parameters.It’s a Fact of LifeAny job, whether it’s in the tech industry, the food industry, Ad Optimization, or even recruitment, uses Machine Learning in one way or another. Yet compared to spaces which work on sequencing the human genome, it’s amazing to see how far things have come. It used to take days to process DNA. Now you can spit in a tube and send it off to 23andMe to learn a little about your health. That’s what Machine Learning enables people to do.But it doesn’t mean Computational Biologists are going to fall by the wayside. It means there will be times you’ll have to liaise more between the two groups. It means you’ll be more marketable by adding Machine Learning to the work you’re already doing or taking some classes in Computational Science, for example, to keep your skills up to date.It’s a Transparency IssueUltimately, it seems the heart of this apprehension comes down to a transparency issue. For example, let’s say companies begin to bring in AI people and suddenly the staff already in place begins to get worried about the security of their jobs. Even in an industry tense with skills gaps, the fear still abounds.In coming back to speak with the Hiring Manager, it became clear the animosity is even more prevalent than first imagined. So, it’s important to get input from within the company and develop a unified story, a unified message across departments, and especially within the Life Science Analytics and Data Science teams as well. In other words, “keep people in the loop.”If it’s happening to this company, it seems other companies may be facing this same issue. However, it’s not going away and is creating a kind of competition between the old guard and the up-and-coming startups. For example, any new company is going to want to integrate AI and will be asking the question how best to integrate it into their structure. They might also ask how best to optimize the ads coming through AI. This is just another way of how companies are catching up, but also how people are catching up to the companies. Technology is coming whether you like it or not. So, if you want to stay marketable and work on really interesting projects, there’s always going to be the challenge of staying up-to-date and different companies attack this in different ways. Stay open minded, keep an eye and an ear out for ways to stay on top of your game. Even just taking a few minutes to watch a YouTube video, listen to a TedTalk or a podcast, so you can talk about it and be informed. These are some really simple ways to stay on the cutting edge and help you figure out where you can grow and improve for better opportunities.Ready for the next step? 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.
A Deep Dive Into The UK’s Most In-Demand Jobs: Machine Learning Engineers | Harnham Recruitment post
LinkedIn have released the 2022 LinkedIn Jobs on the Rise list which combines the 25 fastest-growing job titles over the past five years. To create the rankings the social media platform identified the job titles experiencing the highest growth rates from January 2017 through to July 2021.The list hopes to provide insights into the direction that the workforce is heading and where long-term career opportunities lie. Ranked third are Machine Learning Engineers, so we’ve decided to take a deep dive into the role, unpicking what it entails and how to apply – it could be your next career opportunity. What does a Machine Learning Engineer do exactly? Machine Learning (ML) is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn and make decisions. It enables systems to learn and improve from experience, rather than being explicitly programmed. The primary role of a Machine Learning engineer is to design high-performing ML models and re-train systems as needed. ML engineers apply software engineering and data science methods to turn ML models into usable functions for products and consumers. This involves seeking out and picking suitable data sets, evaluating and organising data and using statistical analysis to constantly improve models and visualise data to gain deeper insights. As part of the process, regular testing and experiments are carried out and changes made off the back of the results.ML engineers tend to function in a very collaborative way and often work as part of a large data science team, communicating with data scientists, administrators, analysts, engineers and architects along with external players such as IT teams and business leaders.What skills or qualifications will I need?Most AI projects fail because organisations lack the technical knowledge in how to deploy and utilise ML models – a factor behind the explosive surge in demand for expertise in this field. The majority of Machine Learning engineering jobs require more than an undergraduate degree and may require a Masters or PhD in computer science, maths, statistics, neural networks, deep learning or related fields. Outside of qualifications, the role itself will rely heavily on strong analytical and problem-solving abilities as well as the confidence to make decisions based on your analysis. In terms of experience, according to LinkedIn, the median number of years of prior experience is four. Having an understanding of certain processes, such as data structures, modelling, and software architecture is a must for this job. Along with more specific skill sets such as coding and programming languages, including Python, Java, C++, C, R, TensorFlow and Natural Language Processing (NLP).And, of course, any knowledge or previous experience that you have with ML frameworks, libraries and packages and deep learning processes, will help to make you a more attractive candidate.As touched on previously, although much of the work will revolve around navigating systems, there is also a strong human element of collaboration, and liaising between people and teams, which will require teamwork and communication skills. The value of these ‘soft skills’ that go beyond technical expertise is being increasingly recognised as conducive to the success of candidates in these roles. Being able to interpret and decipher meaning from data is one thing but being able to communicate this to team members who may have limited, or no knowledge of the field is quite another. This is often referred to as ‘data storytelling’ where you should be able to present your data in a storytelling format with a beginning, middle and ending at concrete results that you have obtained from the data.Translating technical jargon in a way that can not only be understood but also converted into real action could make up a large amount of the job role, so being able to demonstrate your ability in this on your application could really tip the scales in your favour. What industries might I work in and where?ML is advancing across multiple sectors as more organisations are realising the business value it can bring and exploring its capabilities, whether it be predicting customer behaviour or influencing medical outcomes. A study by Indeed found that the demand for skills in AI and ML on its site has almost tripled in three years and the global Machine Learning market size is expected to hit US$39,986.70 million in 2025. This means that you will have an abundance of options at your fingertips as there are countless industries in need of your skillsets, including information technology and services, cybersecurity and financial firms.Many of the skills listed above will also be relevant to other data and analytics roles and will be easily transferrable to an ML Engineering job. The LinkedIn listing found that the most common roles transitioned from were Software Engineer, Data Scientist and Artificial Intelligence Specialist, so you may find a route in by transferring from a similar position.It’s worth noting that there are also plenty of courses available at a click of a button to supplement your qualifications, and resources where you can practise various parts of the ML engineer skillset, helping to demonstrate your interest in the role and enhance your application.Building a career as a Machine Learning Engineer entails never-ending education. As technology advances, staying up to date with AI and cutting-edge technologies and trends will become more crucial, so if you are hungry to learn, this could be the role for you.Looking for your next big role in Data and Analytics or need to source exceptional talent? Take a look at our latest Machine Learning Engineer jobs or get in touch with one of our expert consultants to find out more.
How To Attract Data Scientists To Your Business (And How Not To) | Harnham Recruitment post
Whilst the role of Data Scientist is still considered one of the most desirable around, many businesses are finding that a shortage of strong, experienced talent is preventing them from growing their teams sufficiently. With a huge demand for such a small talent base, enterprises have begun to ask what they can do to ensure that they can secure the skillsets they need. If you’re looking at hiring a Data Scientist, there are a few key Do’s and Don’ts that you need to bear in mind:THE DO’SCreate A Clear Career PathIn most companies, a career path is defined. Usually you grow from junior to senior to manager etc. However, Data Scientists often like to become experts rather than moving up the traditional career ladder into people management roles. And, once a Data Scientists becomes an expert, they want to remain an expert. To do this, they need to keep up with the latest tools and data systems and continually improve. That’s why it’s important that you put in place a clear career path that suits the Data Scientists. In addition to the possibility of leading teams on projects, businesses should provide opportunities for financial progression that reflect growing skillsets in addition to increased responsibilities.
Let Them Be Inventive One of the biggest turn-offs for Data Scientists is lack of opportunities to try new techniques and technologies. Data Scientists can get bored easily if their tasks are not challenging enough. They want to work on a company’s most important and challenging functions and feel as though they are making an impact. If they are asked to spend their time on performing the same tasks all the time, they often feel under-utilised. Providing forward-looking projects, with innovative technologies, gives Data Scientists the opportunity to reinvent the way the company benefits from their Data.Provide Opportunities To Discover As part of their attitude of constant improvement, Data Scientists often feel that attending conferences or meet-ups helps them become better at their role. Not only are these a chance for them to meet with their peers and exchange their Data Science knowledge, they can also discover new algorithms and methodologies that could be of benefit to your business. Businesses that allow the time and budget for their team to attend these are seen as much more attractive prospects for potential employees in a competitive market.
Give them the freedom they needData Scientists are efficient workers who can both collaborate and work independently. Because of this, they expect their employers to trust that they will get the job done without feeling micro-managed. By offering flexible working, be it flexi-hours or working from home options, enterprises can make themselves a much more appealing place to work. THE DON’TSHire The Wrong SkillsetAs many companies begin to introduce Data teams into their business, they can often attempt to hire for the wrong job. Generally, this will be because they automatically jump to wanting to hire a Data Scientist, but actually need a different role placed first. For example; a company may be looking to hire a Machine Learning specialist, but their data pipeline hasn’t even been built yet. There are many talented candidates out there who want to work with the latest technology and solve problems in complex ways. But the reality is that a lot of businesses aren’t ready for their capabilities yet. Before hiring, asses what skillsets you really need and be specific in your search.
Undervalue Their Capabilities There are still a large number of organisations that do not value Data within their culture and Data professionals pick up on this incredibly quickly. If they feel that their work is under appreciated, and they know that there is high demand for what they do, they will not waste their time sticking around. Ask yourself how you see your Data team contributing to the company as a whole and make this clear within your organisation. Advanced Data Scientists don’t want to work on dashboarding so make sure that their work will have an impact and explain how you see this happening during the interview process. Additionally, be aware of other financial implications that their hire may have. It’s likely that they’ll need a supporting Data Engineer to work with and, if they don’t have access to one, they have another reason to look elsewhere. The Data Scientist market is a candidate-driven one and, as a result of this, businesses need to go the extra mile to ensure they get the best talent around. By offering a strong set of benefits, the opportunity to grow and progress, and an environment that values Data, enterprises can stand out amongst the crowd and attract the best Data Scientists on the market. If you’re looking for support with your Data Science hiring process, get in touch with one of our expert consultants who will be able to advise you on the best way forward.
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