Four Things to Consider when Preparing your Laboratory Data for Artificial Intelligence and Machine Learning ?

Background 

The delivery of informatics systems within lab-based companies has always been a very active space. Traditionally this was focused on single systems or multiple connected systems covering a specific set of workflows. Most labs now operate at least some form of LIMS, ELN, LES, SDMS, CDS etc. The current “Big Thing” is how to use those same laboratory informatics systems to deliver data that supports Artificial Intelligence and Machine Learning (AI/ML) to derive more value and to predict outcomes of changes to the workflow to achieve specific goals. 

What do you want from AI/ML? 

This may seem like an obvious, but important point to consider. Before embarking on your AI/ML journey, make sure you have some specific and prioritized targets in mind, for example moving to environmentally friendly raw materials, reduced toxicity effects for a class of drugs, lower costs of production by optimising process conditions and so on.  This helps you to: 

    • Prioritize where to focus your efforts 

    • Estimate potential costs and expected benefits 

    • Know who in your organisation will need to be involved 

    • Know who will be impacted by the program 

    • Understand where funding is coming from 

Holding workshops with stakeholders to capture and prioritize areas of focus is a useful activity in mapping out your AI/ML journey and gathering all these attributes. It will also help you to decide where to invest in preparing your data and lab applications for the journey ahead. 

Is Digital Transformation a Necessary Step to AL/ML? 

We are often asked if it necessary to replace lab spreadsheets with a LIMS or ELN before using AI/ML and the answer is “no” – it may require more effort to normalize your data sets to make them comparable and consistent, but there are no absolutes when it comes to your data.  

Over recent years, Digital Transformation (DT) has become an area of increasing focus for businesses. DT dictates a much wider scope to laboratory systems with greater depth of functionality, increased benefits, and consequently a longer delivery time frame associated with the implementation of a programme of changes. It feels like many lab-based companies and their lab informatics teams are just starting to understand the challenges associated with DT.   

Given that AI/ML works best with more data, you may want to include digital transition as a part of your AI/ML program. An incremental/parallel delivery of DT and AI/ML coverage is often a pragmatic approach, allowing your AI/ML model scope to increase as your DT program delivers more data dimensions. 

Is My Data Consistent and Interoperable? 

For most organisations this is a difficult question to answer without analysing the state of your informatics landscape. Unless you are a green-field facility with an unlimited budget, the chances are pretty low that your scientific informatics platforms were carefully and consistently implemented from day zero:  systems evolve; get upgraded, replaced and migrated; systems from multiple vendors are used; applications have been developed in house; workflows are modified – all leading to inconsistencies in terminology and gaps in data elements. Mapping out data flows and manual transitions between systems is a great way to identify these gaps, and identify the work needed to consolidate your reference data sets. A good question to ask repeatedly during this process is ‘Which is the source of truth for this data element…?’ 

Is data perfection necessary before I develop my central data repository? 

In short, no it isn’t.  The central data repository (CDR) relies on being able to access data from each of the producing systems that will be included in the AI/ML modelling. Data is going to be transported to the data layer using either a native application tool, or an external interface such as ETL, and usually after a particular workflow has completed in the source application – for example data review and authorization. This means you are going to need a range of approaches to get the data you need in the CDR according to capabilities, data types and underlying technology of each source application. It therefore makes sense to start the CDR connectivity for each of your producing systems when the data is ready to be used, i.e. consistent, interoperable, comprehensive enough to contribute to the model. 

Need more information? 

If you’d like to read a more in-depth discussion of the issues highlighted in this blog, we have published a white paper on the subject “Machine Learning and Lab Informatics – Where to Start?” 

Send us an email at info@scimcon.com if you’d like us to help you navigating the AI/ML journey, we’d be happy to discuss your individual needs.  

Common lab informatics questions – part 1: one system or more??

As an information systems consultancy dedicated to successfully delivering lab-based information systems, we help our clients to overcome many different challenges. There are some important questions that we are frequently asked to evaluate.

In part one of this blog series, we’ll summarise the considerations to make when answering 3 common questions about lab informatics systems, all in the theme of ‘is a single system better than multiple similar systems?’

1. Should R&D labs use the same informatics systems as QC?

Here the context matters. If one were to generalise, R&D labs tend to be experiment-based, answering questions like ‘What ingredient changes in the product formulation will increase effectiveness and reduce environmental impact?’. On the other hand, QC labs are more focused on samples taken from production runs, and questions such as ‘Are the % composition of key ingredients within a production batch within specification?’

If we use the above generalisation and apply lab informatics thinking, in broad terms, ELNs are centred on recording experiments and therefore more suited to R&D. LIMS, being sample, test and results biased, are generally more suitable to QC labs.

However, it is not that simple. For example, perhaps one of the R&D labs provides analytical services to various teams executing R&D experiments – this type of ‘service’ lab is often better served by LIMS than ELNs.

The type of labs involved is not the only factor to consider. For example, CDS systems are generally applicable to both R&D and QC. The methods and use of the instruments may well vary across R&D and QC, but the instrument data systems can be exactly the same.

Finally, regulatory needs, specifically for QC can also be a driving factor in answering the question. We will consider this further in one of the following questions.

2. Should we implement a single global system or several more local systems?

When Scimcon first started nearly three decades ago, the focus within large multi-national companies was on implementing large, monolithic lab systems. This approach still has its place, particularly where the distributed labs are very close in terms of operational and analytical workflows.

Current thinking, however, looks to best support the diversity of lab workflows across global sites. While this should not mean a different system in every single lab, it should ensure some flexibility in selecting systems locally. This has several benefits, including a better informatics fit for each lab, and the increased local user buy-in gained by allowing flexibility.

However, against the background of the drive to increased data aggregation, data science and analytics, and AI/ML, this local approach can be counterproductive. It is therefore important to set standards and guardrails about how these systems are implemented, and how the data is structured and linked via reference data, so that consolidation into centralised reporting tools and data lakes is facilitated.

3. Should I have different systems for GxP and non-GxP work?

There is a well-used saying within regulatory-compliant organisations: ‘If a system contains just 1% of GxP data, then the whole system is required to be implemented, managed and maintained in a regulatory compliant manner.’

This statement leaves compliant organisations questioning:

  1. Is it easier to run one regulatory compliant system, that contains both non-GxP and GXP data, and accepting that the non-GxP will also be subject to the associated GXP administrative overheads?
  2. Or is it easier to have two systems, one GxP and the other non-GxP, the latter of which is subject to less rigid controls?

The first step to answering the question is to determine the delta between administering a GxP system, and administering a non GxP system. LIMS, ELN, SDMS, CDS and other lab informatics systems are often classified by labs as mission-critical. Most organisations wouldn’t countenance a lack of system administration rigour or releasing untested changes to mission-critical systems, so this delta may be lower than it first seems.

The next step is an open conversation with QA teams about the types of data being held, and the control systems that will be put in place. In the past, we have successfully taken a two-tier approach, where the administration procedures for non-GxP are simpler than those for GxP data in the same system. However, for this type of arrangement to be viable, a detailed risk assessment is required, and the ongoing management and control of the administration has to be very well executed.

Finally, before making the decision, it’s worth considering whether there are shared services or functions involved. For example, if the GxP and non-GxP work uses the same inventory management, it might be complex to get the inventory system interfacing and updating two systems simultaneously.

Summary

Hopefully, we have illustrated the importance of being clear about what your requirements are before answering these key questions about lab informatics systems. Each case is unique, and your decision will usually be based on a wide range of influencing factors. We help organisations to consider all of the options and roll out their chosen model.

Stay tuned for part 2 of this blog series, where we will look at the key question of how you can prepare your data for AI and machine learning.

Reducing laboratory carbon footprint in biotech and pharma?

My Green Lab – a non-profit organisation that is focused on improving the sustainability of scientific research – recently reported that the carbon footprint produced by the biotech and pharmaceutical industry (including laboratories) increased from 3.9 percent in 2021 to five percent in 2022.

But, more and more companies are committing to the UN’s Race to Zero campaign, which aims to halve total carbon emissions by 2030 and reach net zero emissions by 2050.

In addition to reducing Scope 1 emissions (direct emissions from owned or controlled sources) and Scope 2 emissions (indirect emissions from the purchase and use of electricity, steam, heating and cooling), there is a growing focus on Scope 3 emissions (indirect emissions that occur in the upstream and downstream activities of an organisation).

My Green Lab found that overall, Scope 3 emissions are 4.6 times greater than Scope 1 and 2 combined in the biotech and pharma sector. The impact of this is that pressure to reduce carbon use is being applied down the supply chain, impacting labs at every phase of development, scale-up and manufacturing.

According to CPHI’s 2023 annual survey, 93 percent of executives state that ‘visibility on supply chain partner’s sustainability record’ is either ‘extremely important’ or ‘important’.

There are a number of ways in which laboratories can demonstrate their commitment to sustainability – and help the organisations they are providing services to reduce their Scope 3 emissions – some to consider include:

  1. Obtain My Green Lab certification: considered the gold standard for laboratory sustainability best practices around the world, the program provides scientists and the teams that support laboratories with actionable ways to make meaningful change.
  2. Switch to laboratory products that have the ACT Environmental Impact Factor Label: by emphasizing Accountability, Consistency, and Transparency (ACT) around manufacturing, energy and water use, packaging, and end-of-life, ACT makes it easy to choose more sustainable products.
  3. Identify opportunities for energy efficiency in the laboratory: the Center for Energy Efficient Laboratories (CEEL) provides useful reports and advice.
  4. Join the Sustainable European Laboratories Network: a network of local sustainability teams as well as independent ‘green labs’ networks, which aims to transform the way science is done so that it better responds to the environmental challenges of our era.
  5. If your lab is part of an academic institution, consider joining the LEAF Programme, a standard set by University College London – and followed by 85 global institutions – to improve the sustainability and efficiency of laboratories.

There are many other networks, initiatives and accreditations aimed at helping labs become more sustainable. Tapping into these resources, as well as finding ways to make your lab more efficient, can help you to both reduce carbon emissions and save costs. Importantly, it will ensure your lab does not lose out in future when sustainability becomes a deciding factor in procurement.

Scimcon continues its commitment to reducing its carbon footprint, having signed up to the Science Based Targets Initiative (SBTi) and providing a target whilst also gaining an award for sustainability from Ecovadis. As we continue to add value in the complex lab informatics field, we work closely with our clients to detail Scimcon’s Scope 3 assessments and action plans. 

Ear to the ground: the latest trends in lab informatics­­­­­?

We recently sat down with Rizwan Chaudhrey, a well-connected figure in the life science and pharmaceutical industries, to discuss the changes he has seen across the lab sector in recent years; how this has been impacted by COVID; and any new trends in lab informatics.

Rizwan, tell us about yourself and your work.

I’ve been involved in this field for a long time, building up a portfolio of connections across life science, biopharma, and pharma. I have worked alongside key opinion leaders for the past 8 years, including both members of the media and decision makers within the companies themselves. I have been involved in a myriad of different projects in the industry in that time, from event management to sales strategy. I now work across the whole value chain, aiming to connect and inform people through news, interviews and other forms of content.

I speak to people across all disciplines and roles, and generally host 2 types of interview. In-person interviews, usually at an industry event,where my interviewees generally talk about the company, what they’re showcasing at an event, and any product launches that might be coming up. Video interviews are often more topical, highlighting a specific subject or industry challenge.

During my time in this industry, I’ve visited many different eventsand spoken to hundreds of companies in this space, from large organisations to smaller start-ups. In the time I have been in the industry, lab informatics has changed a lot – it is very much an industry that keeps you on your toes!

What are some of the main trends you are seeing in the laboratory science sector?

Digitalisation is obviously a topic that is heavily discussed, certainly in the events I attend and the interviews I conduct. I think there’s been an interesting shift recently though. The whole industry thought that the COVID-19 pandemic would have a significant effect and drastically speed up the rate of adoption, like we’ve seen in other industries. Everything was going to move to the cloud, and remote access requirements led everyone to believe that we were going to move towards digitalisation at a rate of knots. While we are seeing increasing use of digitised systems, the shift has not been as quick or dramatic as people expected.

From my discussions with lab-based organisations, it appears that one of the big barriers to following through on digital transformation is not knowing where to start. At present, it doesn’t appear as though one vendor has “cracked it” and developed an all-in-one solution that addresses every lab’s needs – there are many different companies offering an array of services and solutions, which can be daunting for a lab-based organisation that is stuck somewhere on its digitalisation journey. For example, major vendors might offer solutions and software packages for their own instruments, but on another level you can look at platforms that focus on specific therapies – there are so many layers to the topic, which is why I believe there are still so many shows with exhibitors talking about what they can bring to the table.

AI/ML is a hot topic at present. Is this something that has come up in your interviews?

Certainly – you can’t avoid artificial intelligence as a topic at the moment! And you can understand why, it has plenty of advantages for labs.

AI can help labs not only generate insights from millions of cells, but also interpret that data and help identify the most valuable results. Machine learning (ML) also provides clear benefits in terms of equipment servicing, as ML-enabled instruments can help engineers and customers through self-diagnosis and troubleshooting. It also facilitates lab automation, through features like automatic refill notifications.

Are there any other trends you have identified?

Post-COVID, we’ve definitely seen a rise in collaboration. Organisations and scientists seem more willing than ever before to share information. We’re also seeing a shift towards automated processes in the lab, with systems using learnt information to lessen the need for human intervention.

In the current environmental climate, sustainability is naturally a big talking point too. Every company I speak to is keen to showcase their ESG practices, especially considering the impact the life science and pharmaceutical industry has on the environment.

Navigating the fog

We agree with Rizwan that  the field of lab informatics is at an exciting crossroads. Still emerging from the madness of COVID, and with the growing promise of AI seeming more inevitable by the day, the industry is facing a period of unpredictability.

As scientists ourselves, the team at Scimcon is well-placed to help lab-based companies address their challenges. Find out more about how Scimcon can help you navigate the fog by visiting our website.

What’s trending in lab informatics? SmartLab Exchange 2024 highlights key themes?

Scimcon is proud to have sponsored both the SmartLab Exchange Europe and US events again this year, which took place from 21-22 February 2024 in Amsterdam, the Netherlands, and 8-9 April 2024 in Florida, USA.

The annual invite-only events facilitate one-to-one meetings and foster collaboration between experts across the lab informatics industry, from R&D to Quality Assurance and Quality Control (QA/QC) decision makers.

Our team led panel discussions and also chaired the European event. From our conversations with delegates and industry experts, we have identified key trends across the lab informatics industry this year, and are sharing what we learned about user priorities in this rapidly evolving sector.

About SmartLab Exchange

In attendance at this years’ events was our co-founder and lead consultant Geoff Parker, who moderated the opening panel discussion that asked the SmartLab community ‘what does digital transformation mean to you?’. At both the Europe and US events, users across the industry shared their insights on digital transformation and where they are in their journey, where they’re planning to be, and what tools / solutions are making these goals achievable.

We informally surveyed 112 delegates at this years’ events (65 from Europe, 47 from the US) to understand what decision makers are prioritising this year, and what areas of lab informatics will be most in-demand.

Investment priorities

Part of our discussions with delegates includes asking what informatics tools and capabilities they are prioritising investment in over the coming 12 months. The following themes emerged from these discussions:

LIMS remains a mainstay solution, with 17% of delegates confirming they plan to invest in a new LIMS or expand their current LIMS over the next 12 months. AI and automation are close second and third priorities, which is perhaps unsurprising. We expect more users to explore AI and automation as lab managers invest in these technologies to streamline operations, simplify and automate processes, and minimise the risk of human error. However, preparedness for implementing AI needs careful attention if labs are to capitalise on its promise.

There are no shortage of informatics tools on this list, such as ELN, QMS, and scheduling tools, so it’s understandable that connectivity remains a key priority for delegates too, as interconnected instrument networks are central to productivity and ease of data transfer.

Solution priorities

We also provided attendees with a list of lab informatics solutions and asked them to identify which of these were high and medium priority for their organisation.

Lab automation is once again high on the list of priorities for delegates, with 82 saying it was a high priority and 18 considering it a medium priority. Data quality/integrity also remains key for delegates, so solutions that ensure data quality is maintained and data are standardised are poised to remain popular. Data integration and connectivity also remain important, which again highlights that decision makers prioritise instruments that can communicate with each other and the lab informatics systems seamlessly, while maintaining data integrity and streamlining lab processes.

Looking ahead – what’s next for lab informatics?

The insights gleaned from both SmartLab Exchange events reflect what is happening across the wider industry.

Technology is constantly evolving. As solution providers and instrument vendors innovate with new systems that aim to alleviate the challenges faced by labs and improve processes, we expect more and more users to invest in technologies that automate repetitive and time-consuming processes. By also ensuring that data quality is standardised and stored securely, scientists will have more time to focus on the science that matters.

We’re proud to support labs with the next step of their digitalisation journey, and are excited to see what else the future has in store for our industry.

For more information about how we can support your next lab informatics project, contact us.

From Scotland to Suffolk: A 350-mile ‘cycle to work’ to help fight blood cancer?

Scimcon’s Geoff Parker and Joscelin Smith will be cycling an impressive 350 miles in just three days in early June – but this isn’t merely a fitness exercise. Scimcon is fundraising for Blood Cancer UK, a charity that provides vital services and support for all those affected by the disease. Through research, care and outreach, the charity is committed to the fight against leukaemia, lymphoma, myeloma, and all other types of blood cancer.

This is a cause particularly close to Scimcon’s heart, as a member of the organisation has a personal connection to blood cancer – find out more about their story here. This link has made the team even more determined to raise as much money as possible for charity, even if it results in some sore legs!

The route

The pair will be starting in Scotland, heading past Hadrian’s wall and into Cumbria, before cycling south-east through the Yorkshire Dales – we hope the weather co-operates and the two are treated to some picturesque views.

Both of our Scimcon cyclists have been putting themselves through their paces through various training sessions recently, and by the time they hit the midlands, we suspect they’ll be glad that they did! Once they have gone past Cambridge, however, they’ll know they are onto the final straight. Finally arriving at Scimcon HQ in Newmarket, the intrepid travellers will be grateful of a sit down.

Good luck to both, we hope the practice pays off!

It’s time to beat blood cancer

Scimcon has committed to Blood Cancer UK as our company charity since 2021, to support the charity’s drive towards life-saving research.

Blood Cancer UK has been dedicated to beating blood cancer for the last 64 years, investing more than £500 million in research to transform treatments and save lives. And there is no doubt that the money raised has changed the world for those affected by blood cancer. In the early 2000’s, only 41% of people diagnosed with leukaemia survived five years – today, the figure stands at 52%. Even greater improvements have been recorded in survival rates for non-Hodgkin lymphoma and myeloma.

Support a good cause

Scimcon has set up a Just Giving page to allow for contributions to Blood Cancer UK, with the aim of raising £5,000. If you would like to contribute and support a worthwhile charity, or if you would like more information about the bike ride, visit the page and make a donation today: https://www.justgiving.com/page/scimcon-1709028074656.

Navigating change successfully in the lab?

Change is both inevitable and constant in the modern lab – evolving regulatory requirements demand greater analytical sensitivity or more rigorous reporting; new instruments are launched to tackle increasing analytical problems faced by scientists; and digital transformation is now considered a necessary step for labs processing and communicating huge amounts of data between systems and software.

Despite its importance, change can be daunting for scientists. Change management is crucial to implementing meaningful and successful change, yet it is often neglected or not applied in full across the lab.

Our infographic highlights the key considerations labs should take when embarking on change, and how to ensure that change is managed successfully across all facets of the lab environment.

Scimcon describes key change management factors for lab managers to consider when undertaking an informatics software implementation.

For more recommendations about how to successfully manage change in an informatics software project, visit our blog: Breaking the change management mould – leading successful laboratory information system projects and digital transformations – Scimcon

Breaking the change management mould – leading successful laboratory information system projects and digital transformations?

Laboratory-based organisations have consistently undergone change, whether provisioning new analytical techniques, instrumentation, information system implementations, or incorporating new regulatory requirements. This is especially true today, when we are undertaking initiatives such as digital transformation and the introduction of AI/ML. In fact, one definition of transformation is ‘a radical change’.

What’s clear is that change is constant. However, managing change effectively is essential to success when undergoing these types of projects. Well-run lab informatics projects manage change within the software project lifecycle. Examples of project change include adjusting functional scope; raising change requests as functionality is demonstrated; and variation of costs. Yet, one key area of change is often neglected.

The problem arises when change management for lab informatics projects focuses solely on the technical delivery of the software. In these cases, very little effort is allocated to the change that will need to occur within the laboratory to accommodate the new system. If lab change management is considered, it is often dealt with ad-hoc and separately from the software delivery part of the project, leading to misalignment, misunderstanding, and missed timelines.

75% of the lab is indifferent to your project.

Lab Manager reports that in a typical change environment, 25% of staff will be early adopters, 25% will actively resist change, and about 50% will be ‘on the fence’ in the early stages.1

These statistics are backed up by experience. Scimcon is often called in to resolve issues within ‘in-flight’ informatics projects. All too often, the route cause analysis reveals the lab community only understood the true impact of the new system too late to adopt it, adapt lab workflow, and change procedures. Rectifying the issues after the fact is seldom quick or low-cost.

Informatics projects don’t operate in a vacuum.

Informatics software does not function in isolation, so change management needs to consider the physical working procedures, workflows, SOPs, job roles, quality system, and other areas that will be impacted within the laboratory.

For example, the implementation of a new LIMS could trigger changes such as:

Given that a lab informatics project will generate a large number of change items similar to the above examples, they must be managed appropriately.

In many respects, these changes are very similar to a system’s user requirements, except they are related to the lab processes as opposed to software functionality. With this in mind, they need to be handled in a similar fashion. Create a team with a project lead and subject matter experts who represent the laboratory. The lab change team should be tasked with actively gathering and maintaining the backlog of change items throughout the project life cycle. Each change should be assessed for impact and priority, added to the change management plan, and allocated to team members to be actioned.

Planning for change starts early.

Before making any significant lab Informatics investment within an organisation, it is likely a business case will be required. If you are serious about managing all aspects of change this is where you should begin. Business cases generally do an excellent job of covering benefits, costs, and ROI – however, change management, specifically within the physical lab, is often not called out in terms of impact, approach or importantly the resources and associated costs.

Not highlighting the lab change management process, resources and costs at this stage will make it considerably more difficult for change management to become embedded in your project at a later stage.

Benefits of effective change management.

The benefits of effectively integrating laboratory change management alongside traditional change management for lab informatics project cannot be ignored. New systems can get up and running faster, and can, importantly, deliver improved lab processes and be met with enthusiasm rather than reluctance, scepticism, or apprehension.

Scimcon consultants are on-hand to support lab leaders overseeing change. As many of our consultants have lab experience themselves, they have seen first-hand the impact of change in the lab, and can provide in-depth knowledge on how to ensure success.

For more information about how Scimcon can support your next big project, contact us.

References:

  1. ‘A Guide to Successful Change Management’ Lab Manager, https://www.labmanager.com/a-guide-to-successful-change-management-27457 [accessed 02/11/23]
Digital Transformation in the lab: where to begin??

Digital transformation is not a new concept, it is just expanding the use of technology as it advances. Today’s laboratory users expect a certain level of usability and synchronicity. After all, in other aspects of their daily lives they are accustomed to having, for example, a seamless digital shopping experience via Amazon.

So, with demand for digital transformation coming from the lab users themselves, and often from the organisation, establishing what it really means to you and what’s achievable, as well as where you are already on the path to digital transformation, is a useful starting point.

What is digital transformation in the lab?

Digital transformation requires constantly improving the environment and the platforms in the lab to give the scientists the best tools possible and make their lives easier. It’s not a single project or something that will be completed in a year, or two.

For some organisations, the first step on their digital transformation might be putting in a new LIMS or ELN – which drastically improves their operations, but could be a huge undertaking depending on the scale of the organisation and the legacy infrastructure. For others, it might be establishing the tools and connections to enable the online monitoring of instrument status, automatic ordering of consumables, reserving instrument time and auto-tracking utilisation, for example. Plus, there are many iterations in between.

What’s important for any lab embarking on, or evolving, a digital transformation journey, is to determine where they are, what their goals are and what’s achievable.

How Scimcon can help

We understand the scale of the digital transformation challenge, as well as what is needed to overcome limitations and ensure improvements are made. Our team of experienced consultants – scientists themselves – are ideally placed to help you define and progress your digital transformation journey.

Efforts will continue in the coming years to achieve a truly digital laboratory. However, this will not be a linear journey. Advancements are constantly emerging and the latest technology will build upon the success of others, meaning the ‘latest thing’ is always evolving. Navigating this process successfully will allow laboratories to achieve increased productivity and optimised workflows – giving scientists back more time to spend on getting results.  

Advancing your digital transformation journey can be a challenge, but, if done well, can transform your lab and its results. Through a wealth of experience in this area, Scimcon can help you to identify your digital transformation goals and help make them a reality in the short, medium, and long term.

Contact us today to learn more about how we can help you with your digital transformation journey.

Introducing Joscelin Smith: an insight into Scimcon’s graduate recruitment scheme?

Earlier this year, Scimcon announced the launch of a new Graduate Recruitment Programme, aiming to attract new talent to our team. We’ve partnered with Sanctuary Graduates, a recruitment agency specialising in sourcing talented graduates for suitable roles within a variety of industries.

Joscelin Smith is one of our newest recruits, and Scimcon’s first graduate consultant to join us through the programme. We sat down with Joscelin to discuss her background, what led her to Scimcon, and what her experience has been like as a graduate joining the Scimcon team.

Can you tell us about your background and what interested you about Scimcon?

Science has always been a passion of mine, so after studying Biochemistry at Bristol University, I went on to work as a Research Assistant at Cambridge University, where I focused on Immunology. I then travelled to Auckland, to complete my PhD on the cardiac nervous system.

It was during this time that I started experimenting with software and coding, which really piqued my interest. This shifted my career trajectory towards a role that incorporated both science and technology, which is of course something I’ve been able to explore working at Scimcon.

How did you find your experience with Sanctuary Graduates?

I had a good idea of the type of role I was after, so after talking to and sending my CV to Sanctuary Graduates, the team put me in touch with Scimcon, who really matched what I was looking for. The interview was quickly set up, and the whole process was very smooth and painless, with a frequent channel of dialogue and updates from the Sanctuary end.

How would you describe your role at Scimcon?

As a Graduate Information Systems Consultant, a large part of my role is helping clients implement various systems and software, such as SDMS and LIMS. I also help clients to problem-solve and alleviate any issues they are having with this process. I have been working in this role for around 6 months, which has mostly been a training period so far, shadowing multiple people across various roles. This has included working with Geoff, Scimcon’s Co-Founder and Principle Consultant, on a digital transformation strategy day, during the early stages of our work with a new client. I found this fascinating as it showed me how Scimcon can add real strategic value to clients. I have also worked with our Informatics Project Manager Lynda Weller, as well as Jon Fielding – one of the Project leads here at Scimcon. Being able to work with different colleagues has been very interesting and provided extremely useful insights into the role, as well as Scimcon in general.

What do you enjoy most about working at Scimcon?

The prospect of problem-solving first attracted me to this role, and being involved in the resolution of a particular issue for a client has been really rewarding so far. I didn’t know exactly what to expect but the project management has also emerged as a really enjoyable aspect of the job. Having worked in the lab myself, I really see the value in Scimcon’s mission to help make laboratory workflows more efficient.

As I’m familiar with a lot of the systems we work on, I can translate my experience in the lab to my role at Scimcon, working on design and implementation.

I am finding it incredibly fulfilling working for a company which is trying to bridge that gap and give more time back to scientists. I believe this process is invaluable and is something I am proud to be working on.

What do you hope to achieve at Scimcon?

My previous lab experience was helpful to evaluate different career paths, and ultimately I am pleased that it has led me to my role as a Graduate Information Systems Consultant for Scimcon. I am really looking forward to advancing my career within the company and in the short term I am hoping to gain more exposure to different projects and the different systems we work with.

To read more about how Sanctuary Graduates are helping to provide Scimcon with talented candidates to add to our expertise in data informatics, read our previous blog.

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