Life Science Analytics Lead the Way to a New Normal

Judith Kniepeiss our consultant managing the role
Posting date: 7/30/2020 9:19 AM
The Life Science Analytics industry has always beaten to its own drum. But in the days of Covid-19, there’s a different feel and it’s one in which teams are coming together and candidates are staying longer in jobs where they feel connected and impactful. 

As the drive for a vaccine and the virtually overnight demand for telemedicine and contactless care come to bear, this industry which once seemingly fell behind that of retail and banking has caught up. So, what can businesses like biotech, pharmaceutical, and other healthcare providers do to retain and keep top candidates?

EXPAND AND GROW YOUR TEAM LOCALLY AND GLOBALLY

  • Reskill and Upskill for Career Advancement - If you’re lucky enough to have retained top talent, re-consider tenure-based positions. Advance your great candidates based on performance, need, upskilling, or reskilling. You may already have someone on staff who can do the job you need done or have the potential. Let them. The world has been moving faster than it ever has in this year alone its jumped into warp speed.
  • Consider Global Collaboration – While many professionals, in every industry are working from home these days, some simply can’t due to the nature of their business. In this case, the need to be in the lab. However, as the Life Sciences & Analytics industry leads the way in their approach to flexible hours and the available Data on COVID-19, for example, global collaborations allow teams to do their work without the need for lab access.

  • In demand technical skills - Candidates skilled in Data gathering, algorithm development, and predictive modeling are in high demand as well as AutoML, NLP, and other Machine Learning solutions.

  • In demand soft skills – As the impact of the above technical skills increase and offer proven solutions, it will be important to have Data professionals who cannot only manage the technical side of things, but who can also explain solutions to the nontechnical and business executives in plain language.

Since the start of the year, we’ve seen a massive shift in the way we do business. While for some businesses, it was business as usual for the most part. For others, it completely reinvented others. Healthcare and Life Sciences are no exception. And in the healthcare industry, they’ve been stretched in ways unimaginable just last year. And have learned a new respect for numbers and accurate Data. Two things vital to moving forward.

A NEW RESPECT FOR NUMBERS AND ACCURATE DATA


This new respect for accurate numbers and Data will help teams align to predict new threats while tracking current ones. In other words, no one will be caught off guard next time as the Life Sciences and Healthcare industry prepare for a post pandemic transformation. And how will it impact the industry moving forward?

Work from home policies, global teams, telemedicine, the demand for PPE and ventilators, even the demands of the financial side of healthcare have shifted. But with the right data, innovation, and improved efficiency, it’s a sure bet the industry won’t be caught unawares again.

WELCOME TO THE NEW NORMAL


Though every profession has been hard hit during the pandemic, it’s the healthcare industry which has seen an even greater shift in the need demands to be met, shifting priorities, and patient care delivery has gone online. By moving forward with telemedicine and other automated services, the revenue cycle of the industry, too, has seen a shift. Yet to maintain business continuity, they must close the revenue gap.

And here’s where Life Science Analytics meets FinTech and InsurTech. All of these industries will need Data professionals who can speak code and translate it to the nontechnical. All will need professionals with skillsets in predictive modelling, automation, Machine Learning, AI, and more. Is it you they’re looking for? 

If you’re interested in Data & Technology, Risk or Digital Analytics, Life Science Analytics, Marketing & Insight, or Data Science jobs we invite you to check out our current vacancies or get in touch with one of our expert consultants to find out more. 

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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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Seven Ways To Minimise Unconscious Bias In Your Recruitment Process

When it comes to the recruitment process, organisations will often take different approaches to securing their next hire. Yet, one challenge that remains the same across the board is the ubiquitous nature of unconscious bias. This typically means that individuals will favour those that look or think similarly to themselves. Not only is the potential for prejudice to arise an alarming issue here, but the impacts of unconscious bias can also have a detrimental effect on the hiring process, in both the short and long term. You could face missing out on a highly skilled and qualified candidate, as well as damaging opportunities for improving the diversity of the business. In order to address unconscious bias, organisations really need to take a moment to reflect and challenge their perceptions on the positive and the negative implications. Our own research demonstrates the opportunities of bolstering not only a diverse team in Data and Analytics, but an inclusive one too. Here are some core ways in which organisations can challenge and adapt their processes: Check your job descriptions It’s one of the simplest changes to make, but far too often overlooked. Many of us will use gender coded language without even realising it. It is therefore critical that all job descriptions are neutral, and that descriptive language is removed. Masculine-coded words such as ‘confident’ and ‘guru’ and feminine-coded words such as ‘understanding’ and ‘modest’ can really discourage individuals from applying for positions. Make use of panel-based interviews Over the past year, we’ve all become accustomed to a much more virtual way of working, which includes the recruitment process.  Our reliance on technology now plays an integral role in how we interview, test and hire candidates. When interviewing candidates, organisations should involve a range of different people (even if this is just in an observational role), as they may challenge your preconceptions and provide an alternative viewpoint. Instead of only involving the CEO and Managing Director, for example, make sure you have individuals from other departments and areas within the team sitting in too. Interviews should instead focus on skills-based tasks  In order to minimise the unconscious bias that permeates the recruitment process across industries, interviews need to focus on skills-based tasks. Importantly, hiring managers should be assessing the suitability for a role, so practical, skills-focused tasks are important in establishing this. Appoint an external inclusion agency If you’re stuck for where to start when it comes to improving the ways in which you plan and execute your hiring strategies, it could be worthwhile to seek support from an external agency or individual that specialises in inclusion. Their insights, experiences and knowledge will be able to support an organisation to ensure that their hiring process minimises the impacts of unconscious bias. Facilitating discussions and training In the same way that liaising with external experts can support an organisation, so to can introducing training sessions. Stamping out unconscious bias requires us all to challenge our ways of thinking to create an inclusive culture for all. Regardless of whether this is during the recruitment process, through onboarding or once an individual is working within the business, facilitating discussions and training can help. It should be noted though, that generalised training to minimise unconscious bias training isn’t always effective, so this should be assessed and planned according to relevant objectives and goals. Advertise roles through different channels To ensure that you are reaching a diverse pool of talent, hiring managers should ensure that positions are advertised across a range of different platforms. It may be the case that highly skilled professionals from different backgrounds do not all source new positions through the same websites or streams. Improving this access will ensure that you are not selecting candidates from the same pool of talent.  Set specific diversity and inclusion goals It’s crucial to remember that taking steps to minimise and remove these biases is just one part of a much bigger challenge that organisations are facing in order to action change. Firms need to assess their long-term diversity and inclusion goals in order to ensure that removing biases is part of an embedded strategy. Internal strategies must be reviewed and assessed in order to ensure that the approach to the recruitment process provides equal and fair access and opportunities for all to thrive. In the Data and Analytics sector, it’s key for leaders to take action to mandate some core strategies to engage and include a diverse team of talent. If you're looking to make your next hire, or are searching role yourself, get in touch with our expert consultants or take a look at our latest Data & Analytics jobs here. 

3 Ways Machine Learning Is Benefiting Your Healthcare

With Data-led roles leading the list in the World Economic Forum’s ‘Jobs of the Future’ report, it is no surprise that Data Science continues to be the main driving force behind a number of technological advancements. From the Natural Language Processing (NLP) that powers your Google Assistant, to Computer Vision identifying scanning pictures for specific objects and the Deep Learning techniques exploring the capability of computers to become “human”, innovation is everywhere.  It’s unsurprising, then, that the world of healthcare is fascinated by the possibilities Data Science can offer,  possibilities which could not only make your and my life better, but also save several thousands of lives around the world. To just scrape the surface, here are three examples of how Machine Learning (ML) techniques are being used to benefit our healthcare.  COMPUTER VISION FOR IMAGING DIAGNOSTICS  Have you ever had a broken leg or arm and saw a x-ray scan of your fracture? Can you remember how the doctor described the kind of fracture to you and explained where exactly you can see it in the picture? The same thing that your doctor did a few years ago, can now be done by an algorithm that will identify the type of fracture, and provide insights into how you should treat it. And it’s not just fractures; Google's AI DeepMind can spot breast cancer as well as your radiologist. By feeding a Machine Learning model the mammograms of 76,000 British women, Google’s engineers taught the system to spot breast cancer in a screen scan. The result? A system as accurate as any radiologist.  We‘ve already reached the point where Machine Learning and AI can no longer just outsmart us at a board game, but can benefit our everyday lives, including in as sensitive use-cases as the healthcare industry. NLP AS YOUR PERSONAL HEALTH ASSISTANT  When we go to our GP, we go to see someone with a medical education and clinical understanding who can evaluate our health problems. We go there because we trust in the education of this person and their ability to give us the best information possible. However, thanks to the rise of the internet, we’ve turned to search engines and WebMD to self-diagnose online, often reading blogs and forums that will convince us we have cancer instead of a common cold.  Fortunately, technology has advanced to the point where it can assist with an on-the-spot (much more accurate) evaluation of your medical condition. By conversing with an AI, like the one from Babylon Health, we can gain insights into possible health problem, define the next steps we need to take and know whether or not we need to see a doctor in person.  There’s no need to wait for opening times or to sit bored in a waiting room. Easy access from your phone democratises the process and advice can be received by anyone, at any time.    DEEP LEARNING DRAWS CONCLUSIONS BETWEEN MEDICAL STUDIES Despite their extensive qualifications, even medical researchers can feel overwhelmed by the sheer amount of Insights and Data that are gathered around the world in hospitals, labs, and across various studies. No wonder it’s not uncommon for important Insights and Data to get forgotten in the mix. Once again, Machine Learning can help us out. Instead of getting lost in a sea of medical data, ML algorithms can dig deep and find the information medical researchers really need. By efficiently sifting a through vast amounts of medical data, combining certain datasets and providing insights, ML sources ways for treatments to be improved, medicines to be altered, and, as a result, can save lives. And this is only the beginning. As Machine Learning continues to improve we can expect huge advances in the following years, from robotic surgery to automated hospitals and beyond. If you’re an expert in Machine Learning, we may have a job for you. Take a look at our latest opportunities of get in touch with one of our expert consultants to find out more. 

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