Machine Learning Engineer (Contract)
London / £600 - £700
£600 - £700
Harnham's Data Engineering contract team is currently working with an exciting consultancy, helping them to support their Aerospace Engineering projects.
SKILLS AND EXPERTISE
As a Machine Learning Engineer, you must have:
- A proven experience in programming with Python
- 3 years minimum working as a Machine Learning Engineer
- Experience with time-series modeling (classical/machine learning/deep learning)
- Experience in deploying and productionising Machine Learning models
- Experience in the end-to-end life cycle of Machine Learning Models
- Experience working in a start-up environment
- A degree in a quantitative or engineering discipline (e.g. Maths, Computer Science)
NICE TO HAVE EXPERIENCE
- Experience in the Aerospace Engineering sector
- Experience in algorithmic trading
- MLops experience
- Exposure to PySpark
In this role, you could earn up to £700 per day outside IR35.
HOW TO APPLY
To be considered for this exciting opportunity, please submit your details using the Apply button on this page. Or for more information regarding other roles please contact Olivia Kavaya-Savage at Harnham.
Python, Machine learning, Software Engineering, CI/CD, Unit Testing, Deploy, Production, Integration.
Data Engineer Or Software Engineer: What Does Your Business Need? | Harnham US Recruitment post
We are in a time in which what we do with Data matters. Over the last few years, we have seen a rapid rise in the number of Data Scientists and Machine Learning Engineers as businesses look to find deeper insights and improve their strategies. But, without proper access to the right Data that has been processed and massaged, Data Scientists and Machine Learning Engineers would be unable to do their job properly. So who are the people who work in the background and are responsible to make sure all of this works? The quick answer is Data Engineers!… or is it? In reality, there are two similar, yet different profiles who can help help a company achieve their Data-driven goals. Data Engineers When people think of Data Engineers, they think of people who make Data more accessible to others within an organization. Their responsibility is to make sure the end user of the Data, whether it be an Analyst, Data Scientist, or an executive, can get accurate Data from which the business can make insightful decisions. They are experts when it comes to data modeling, often working with SQL. Frequently, “modern” Data Engineers work with a number of tools including Spark, Kafka, and AWS (or any cloud provider), whilst some newer Databases/Data Warehouses include Mongo DB and Snowflake. Companies are choosing to leverage these technologies and update their stack because it allows Data teams to move at a much faster pace and be able to deliver results to their stakeholders. An enterprise looking for a Data Engineer will need someone to focus more on their Data Warehouse and utilize their strong knowledge of querying information, whilst constantly working to ingest/process Data. Data Engineers also focus more on Data Flow and knowing how each Data sets works in collaboration with one another. Software Engineers – DataSimilar to a Data Engineers, Software Engineers – Data ( who I will refer to as Software Data Engineers in this article) also build out Data Pipelines. These individuals might go by different names like Platform or Infrastructure Engineer. They have to be good with SQL and Data Modeling, working with similar technologies such as Spark, AWS, and Hadoop. What separates Software Data Engineers from Data Engineers is the necessity to look at things from a macro-level. They are responsible for building out the cluster manager and scheduler, the distributed cluster system, and implementing code to make things function faster and more efficiently. Software Data Engineers are also better programers. Frequently, they will work in Python, Java, Scala, and more recently, Golang. They also work with DevOps tools such as Docker, Kubernetes, or some sort of CI/CD tool like Jenkins. These skills are critical as Software Data Engineers are constantly testing and deploying new services to make systems more efficient. This is important to understand, especially when incorporating Data Science and Machine Learning teams. If Data Scientists or Machine Learning Engineers do not have a strong Software Engineers in place to build their platforms, the models they build won’t be fully maximized. They also have to be able to scale out systems as their platform grows in order to handle more Data, while finding ways to make improvements. Software Data Engineers will also be looking to work with Data Scientists and Machine Learning Engineers in order to understand the prerequisites of what is needed to support a Machine Learning model. Which is right for your business? If you are looking for someone who can focus extensively on pulling Data from a Data source or API, before transforming or “massaging” the Data, and then moving it elsewhere, then you are looking for a Data Engineer. Quality Data Engineers will be really good at querying Data and Data Modeling and will also be good at working with Data Warehouses and using visualization tools like Tableau or Looker. If you need someone who can wear multiple hats and build highly scalable and distributed systems, you are looking for a Software Data Engineer. It’s more common to see this role in smaller companies and teams, since Hiring Managers often need someone who can do multiple tasks due to budget constraints and the need for a leaner team. They will also be better coders and have some experience working with DevOps tools. Although they might be able to do more than a Data Engineer, Software Data Engineers may not be as strong when it comes to the nitty gritty parts of Data Engineering, in particular querying Data and working within a Data Warehouse. It is always a challenge knowing which type of job to recruit for. It is not uncommon to see job posts where companies advertise that they are looking for a Data Engineer, but in reality are looking for a Software Data Engineer or Machine Learning Platform Engineer. In order to bring the right candidates to your door, it is crucial to have an understanding of what responsibilities you are looking to be fulfilled.That’s not to say a Data Engineer can’t work with Docker or Kubernetes. Engineers are working in a time where they need to become proficient with multiple tools and be constantly honing their skills to keep up with the competition. However, it is this demand to keep up with the latest tech trends and choices that makes finding the right candidate difficult. Hiring Managers need to identify which skills are essential for the role from the start, and which can be easily picked up on the job. Hiring teams should focus on an individual’s past experience and the projects they have worked on, rather than looking at their previous job titles. If you’re looking to hire a Data Engineer or a Software Data Engineer, or to find a new role in this area, we may be able to help. Take a look at our latest opportunities or get in touch if you have any questions.
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.
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