<p>Data analytics and data science are transforming the life sciences industry, including significant milestones such as Insilico Medicine&rsquo;s AI-designed drug Rentosertib, and improved clinical trial efficiency and precision medicine. With this growth comes greater career longevity and increased options for candidates, but higher scarcity for employers looking to innovate through technical-domain proficient talent.</p>
<hr>
<h3>Key Takeaways:</h3>
<ul>
<li>93% of life sciences executives planned to increase spending on data, digital and AI capabilities in 2025, indicating the demand for data science and data analysis talent in the life sciences industry.</li>
<li>Big databases, AI-based clinical decision support systems, precision medicine and genomic data analytics, clinical trial efficiency, and personalized healthcare are just a few of the areas being positively impacted by the growth of data science and data analytics.</li>
<li>Some of the most in-demand roles currently in life sciences are data scientists, bioinformatics scientists, biostatisticians, research analysts, and RWE/health data analysts.</li>
<li>Life sciences leaders estimate digital and analytics have driven $6-9bn annual global impact so far, against a potential $130-190bn if fully applied across the value chain, showing how much opportunity remains for data professionals to drive impact.</li>
</ul>
<hr>
<h2>What is Data Science?</h2>
<p>As <a href="https://www.ibm.com/think/topics/data-science" target="_blank" rel="noopener">defined by IBM</a>, data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI) and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization&rsquo;s data. These insights can be used to guide decision-making and strategic planning.</p>
<p>The opportunities arising from data science and its applications are vast. For example, in the case of human brain MRIs, big databases such as the UK Biobank have become invaluable sources of information for medical research by <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC12086459/" target="_blank" rel="noopener">utilizing data from over 500,000 participants to analyze disease risk factors and health impacts extensively</a>.</p>
<p><a href="https://www.researchgate.net/publication/394871173_Emerging_Trends_in_Data_Science_Applications_in_Life_Sciences_From_Claims_Analytics_to_Precision_Medicine" target="_blank" rel="noopener">AI-based clinical decision support systems</a> are also an emerging area in the application of data science in medicine, utilizing machine learning, natural language processing, and predictive analytics to provide evidence-based advice at the point of care. Implementation analysis indicates a mean diagnostic accuracy improvement of 23.7%, with oncology at 34.2% and emergency medicine at 28.9% for triage decision-making.</p>
<p>Additionally, data science can be applied to precision medicine and genomic data analytics, particularly through next-generation sequencing data integration with electronic health records, alongside further applications in predictive modeling, allowing for more precise risk stratification and cost prediction models.</p>
<h2>What is Data Analytics?</h2>
<p>Data analytics is the process of collecting, cleaning, and interpreting large datasets to uncover potential opportunities, trends and risks. This often means working with clinical, operational, genetic, and patient data to make more strategic and informed decisions across research, development, and care delivery.</p>
<p>Data analytics is often applied to advancing personalized healthcare, providing faster and more accurate treatment plans by integrating genomic data, EHRs, and wearable device inputs alongside real-time processing.</p>
<p>Clinical trial efficiency is another area where data analytics thrives, overcoming the traditional disadvantages of errors, delays, and increased costs by automating data validation and workflow optimization, identifying the best patient cohorts, and monitoring compliance and safety in real time. Subsequently, clinical trials utilizing data analytics result in shorter timelines, more reliable trial outcomes, and lower overall costs.</p>
<h2>Data Analytics vs. Data Science: What&rsquo;s the Difference?</h2>
<p>You might see &ldquo;data analytics vs data science&rdquo; and wonder if there&rsquo;s a difference at all, given that the terms can often be used interchangeably.</p>
<p>Put simply, data science focuses on data collection and management of large-scale structured and unstructured data. Data analytics is the act of examining data sets to extract opportunities and value, or to answer specific questions.</p>
<p>In practice, that means that data science is often more <strong>forward-approaching</strong>, involving the building of automated pipelines, deep learning models, and predictive frameworks to manage significant amounts of data. This helps to predict future trends.</p>
<p>Data analytics, comparatively, <strong>looks backward</strong> (or at current results) and resides under the data science umbrella, interpreting existing datasets to answer targeted questions (e.g., &ldquo;why did this cell culture fail?&rdquo;), explaining past events and informing current actions.</p>
<p><strong>Data science skillsets and tools include: </strong></p>
<ul>
<li>Expansive knowledge of machine learning and AI</li>
<li>Python, SAS, R and Scala programming languages</li>
<li>Data platforms such as Hadoop or Apache Spark</li>
<li>SQL database coding</li>
</ul>
<p><strong>Data analytics skillsets and tools include: </strong></p>
<ul>
<li>Statistical analysis software</li>
<li>Database management systems (DBMS)</li>
<li>BI platforms</li>
<li>Data visualization tools and modeling aids such as QlikView, D3.js and Tableau</li>
</ul>
<p>Data science <a href="https://www.jnj.com/innovativemedicine/emea/about-us/our-responsibilities/sustainability/data-science-data-and-ai-are-revolutionizing-healthcare" target="_blank" rel="noopener">drives innovation in science and technology, increasing healthcare access, and delivering personalized information and care</a>. Comparatively, the biggest benefits of data analytics for life sciences organizations are <a href="https://www.innopharmaeducation.com/blog/data-analysis-turning-raw-numbers-into-actionable-insights" target="_blank" rel="noopener">cost and time-saving benefits in clinical trials, enhanced patient care, improved decision-making, and faster drug discovery and development</a>.</p>
<h2>How Are Data Science and Analytics Transforming Life Sciences?</h2>
<p>The economic benefits of data science and data analytics in life sciences are clear. <a href="https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality" target="_blank" rel="noopener">Data analytics can generate $60 billion to $100 billion in economic benefits for the global healthcare industry annually</a>, but what about the transformation to the life sciences industry and life sciences organizations overall?</p>
<p>Algorithms simulating biological interactions can significantly accelerate drug discovery, improving the efficiency of initial testing and opening doors to novel therapies. For example, biotech startup <a href="https://insilico.com/blog/first_phase2" target="_blank" rel="noopener">Insilico Medicine achieved a landmark milestone with its AI-designed drug Rentosertib</a> for idiopathic pulmonary fibrosis (IPF). This compound is the first drug in which both the biological target and therapeutic molecule were discovered using generative AI.&nbsp;</p>
<p>Data science is also optimizing preclinical research through computational biology and bioinformatics methods, <a href="https://intuitionlabs.ai/articles/data-science-in-life-sciences" target="_blank" rel="noopener">analyzing vast genomic and proteomic datasets to uncover drug targets and disease mechanisms</a>.</p>
<p>Predictive analytics are increasingly informing protocol design and real-time trial monitoring, which is further extended by real-world evidence (RWE). RWE is feeding regulatory submissions, pharmacovigilance and label-expansion decisions with insights drawn from routine care rather than trial settings alone.</p>
<p>Similarly, in commercial operations, data analytics is supporting more targeted engagement and forecasting, while precision medicine &ndash; <a href="https://intuitionlabs.ai/articles/data-science-in-life-sciences" target="_blank" rel="noopener">underpinned by routine genomic analysis integrated with broader health data</a> &ndash; is moving personalized treatment from concept to practice.</p>
<p>The organizations not opting for data science and data analytics talent are likely to be left behind. <a href="https://www.zs.com/insights/2025-survey-data-digital-ai#:~:text=Our%20survey%20shows%20no%20signs,the%20prior%20year" target="_blank" rel="noopener">93% of life sciences executives planned to increase spending on data, digital and AI capabilities in 2025</a>, and the talent needed to drive these innovations are in high demand.</p>
<h2>Which Data Roles Are Most in Demand in Life Sciences?</h2>
<ul>
<li><strong>Data scientists:</strong> <a href="https://www.biospace.com/job-trends/data-scientist-fourth-fastest-growing-u-s-job-says-bls" target="_blank" rel="noopener">BLS projects 33.5% growth from 2024-2034, making it the 4th fastest-growing US occupation</a>, with a median wage of $112,590.</li>
<li><strong>Bioinformatics scientists:</strong> Classified by BLS under computer and information research scientists, with <a href="https://research.com/careers/what-can-you-do-with-a-bioinformatics-degree" target="_blank" rel="noopener">projected growth of roughly 26% through 2033</a>, driven by genomic and precision medicine.</li>
<li><strong>Biostatisticians: </strong><a href="https://www.bls.gov/ooh/math/mathematicians-and-statisticians.htm" target="_blank" rel="noopener">8% growth for mathematicians and statisticians from 2024-2034</a> is expected, with demand concentrated in clinical trial design and drug development.</li>
<li><strong>Research analysts:</strong> Increasingly embedded in pharma and health systems for trial optimization and capacity forecasting, with a projected 21% growth from 2024-2034.</li>
<li><strong>RWE/health data analysts: </strong>Demand tracks the wider life science analytics market, <a href="https://www.precedenceresearch.com/life-science-analytics-market" target="_blank" rel="noopener">expected to grow from $11.97bn in 2025 to $26.63 by 2035</a>.</li>
</ul>
<h2>What Skills Do Employers Look for in Data Science and Analytics Professionals?</h2>
<p>The most in demand data science skills are:</p>
<ul>
<li><strong>Programming &amp; ML: </strong><a href="https://365datascience.com/career-advice/data-scientist-job-market/" target="_blank" rel="noopener">Python appears in 57% of data scientist postings, machine learning in 69%, and R in 33%</a>, emphasizing the need for candidates who can build and evaluate models, not just run them.</li>
<li><strong>Statistics &amp; data preparation: </strong>Strong statistical fluency and the ability to clean, structure, and prepare messy or unstructured datasets before modelling.</li>
<li><strong>Life sciences domain knowledge:</strong> <a href="https://intuitionlabs.ai/articles/life-sciences-job-market-2025" target="_blank" rel="noopener">The ability to handle genomic and proteomic datasets and work with bioinformatics pipelines,</a> particularly those with hybrid biology/computer science expertise, not just general-purpose data scientists.</li>
<li><strong>Business translation: </strong><a href="https://365datascience.com/career-advice/data-scientist-job-market/" target="_blank" rel="noopener">The skillset to evaluate models critically, explain results clearly, and connect technical work to business impact</a>, moving beyond a purely technical silo.</li>
</ul>
<p>The most in demand data analytics skills are:</p>
<ul>
<li><strong>SQL &amp; visualization tools:</strong> Core querying and dashboarding proficiency (SQL, Excel, BI tools) to turn structured data into decision-ready outputs.</li>
<li><strong>Stakeholder communication:</strong> <a href="https://365datascience.com/career-advice/data-analyst-job-outlook-2025/" target="_blank" rel="noopener">Appears in nearly 60% of data analyst postings</a>, highlighting the demand from employers for candidates that can translate value from technical findings into plain-language recommendations.</li>
<li><strong>Critical thinking &amp; problem framing:</strong> Questioning data at face value, pattern-identifying, and deciding which questions are worth answering. This skill is valued in tandem with the commonly used automated dashboards as an extra layer of judgement.</li>
<li><strong>Business/clinical fluency: </strong>Employers are increasingly seeking those who understand how an analysis maps onto regulatory, clinical or commercial decisions.</li>
</ul>
<h2>Is Data Science Still a Good Path in Life Sciences?</h2>
<p>Yes, and at a pace that&rsquo;s growing, not slowing. <a href="https://www.mckinsey.com/industries/life-sciences/our-insights/top-ten-observations-from-2022-in-life-sciences-digital-and-analytics" target="_blank" rel="noopener">Life sciences leaders estimate digital and analytics have driven $6-9bn annual global impact so far</a>, against a potential $130-190bn if fully applied across the value chain, showing how much headroom remains for data professionals to drive impact.</p>
<p>Emerging applications back this up. A<a href="https://intuitionlabs.ai/articles/ai-discovered-drugs-clinical-trials-2026" target="_blank" rel="noopener"> peer-reviewed analysis presented at ASCO 2026 counted 117 AI-enabled therapeutic assets across 63 companies already in human clinical trials</a>, showing how AI-driven discovery has already translated into real pipelines.</p>
<p>Combined with the growth and salary data discussed earlier, there&rsquo;s a consistent signal: demand is broad, not confined to one job title. For candidates, this means that demand has never been higher and genuine career longevity is likely. For employers, it means the talent pool with the best technical and domain skill combination is strong, but highly sought-after, and a gap this scale of untapped value is only going to widen.</p>
<h2>Why Partner with Redbock?</h2>
<p>Redbock is a leading <a href="https://redbock.com/about-us/" target="_blank" rel="noopener">US life sciences consulting firm</a>, providing resourcing solutions and skilled consultants to pharmaceutical, biotechnology, and medical device companies.</p>
<p>We have over 24 years of providing to the industry, securing highly-specialized candidates across quality, clinical, regulatory/compliance and engineering. If you&rsquo;re looking to expand your team with data science and data analyticsconsultants, whether it&rsquo;s biostatisticians and data scientists or RWE analysts, we can help &mdash; <a href="https://redbock.com/contact/" target="_blank" rel="noopener">get in touch with Redbock today</a>.</p>

Data Analytics vs. Data Science: Which Roles Are in Demand in Life Sciences?

<p>Real-world evidence (RWE) is reshaping the way that the pharmaceutical industry proves value, gets treatments approved, and engages the people who prescribe and use them. As real-world data becomes embedded in how pharma proves value, medical affairs is increasing the function tasked with generating and interpreting it to translate evidence into insights, and the industry is increasingly looking for talent to drive these innovations forward.</p>
<hr>
<h3>Key Takeaways:</h3>
<ul>
<li>Real-world evidence is becoming a strategic necessity, driven in part by payers increasingly tying reimbursement to real-world outcomes, and treatments becoming more targeted at biomarker-defined patient groups.</li>
<li>Medical affairs now sits at the center of the evidence lifecycle, from designing real-world evidence generation studies to translating findings for HCPs, payers, and patients.</li>
<li>The limitations of Randomized Controlled Trials (RCTs) and trends such as the growth of precision medicine, higher demand for better patient insights, and the rise of value-based healthcare are the driving force behind the RWE demand.</li>
<li>The talent pipeline isn&rsquo;t aligned with the demand for RWE and medical affairs professionals, leaving organizations more reliant on outsourcing where possible or specialist consulting to fill gaps.</li>
</ul>
<hr>
<h2>What is Real-World Evidence (RWE)?</h2>
<p>The FDA defines RWE as &ldquo;the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of real-world data (RWD)&rdquo;. Comparatively, real-world data is defined as &ldquo;Data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources.&rdquo;</p>
<p>Data for real-world evidence can come from electronic health records, medical claims data, data from product or disease registries, and data gathered from other sources such as digital health technologies.</p>
<p>Real-world evidence contrasts significantly with evidence resulting from randomized controlled trials (RCTs), which traditionally have disadvantages such as being conducted in selective populations in tightly controlled settings, which can reduce findings seen in the broader and more diverse populations in the real world.</p>
<h2>Why is Real-World Evidence Becoming More Important in Pharma?</h2>
<p>Real-world evidence is becoming a strategic priority across pharmaceuticals, at a time when the global RWE solutions market is projected to <a href="https://www.gminsights.com/industry-analysis/real-world-evidence-solutions-market" target="_blank" rel="noopener">grow from $3.1bn in 2026 to $11.9bn by 2035</a>.</p>
<p>A number of trends are driving this shift:</p>
<ul>
<li><strong>Higher demand for better patient insights: </strong>Controlled trials only capture narrow populations, compared to the diverse, everyday patients captured by real-world data, which can help researchers <a href="https://pubmed.ncbi.nlm.nih.gov/42175725/" target="_blank" rel="noopener">find rare genetic targets, refine trial design, and support approvals</a> across the diverse patient groups that trials can&rsquo;t represent.</li>
<li><strong>Value-based healthcare is on the rise:</strong> Over <a href="https://www.simon-kucher.com/en/insights/innovation-revolutionizing-outcome-driven-payer-contracting-us-pharma-and-biotech" target="_blank" rel="noopener">58% of US payers are now running at least one outcomes-based contract</a>, and subsequently, manufacturers need ongoing evidence that a therapy delivers value beyond just approval.</li>
<li><strong>The growth of precision medicine: </strong>As treatments continue to target smaller, biomarker-defined patient groups, RWE is what validates them at scale. Real-world <a href="https://www.healthaffairs.org/doi/10.1377/hlthaff.2017.1579" target="_blank" rel="noopener">clinic-genomic databases already stratify cancer patients</a> by tumor genotype, showing which treatments work for which subgroups even after a trial ends.</li>
<li><strong>Better understanding of real-world treatment outcomes:</strong> Since 2016, RWE has featured in <a href="https://oncodaily.com/industry/fda-6" target="_blank" rel="noopener">35 FDA drug, biologic, and vaccine applications and over 250 device authorizations</a>, and in December 2025, the FDA removed a major barrier to submitting real-world data in reviews.</li>
</ul>
<p>As real-world data becomes embedded in how pharma proves value, medical affairs is increasing the function tasked with generating and interpreting it to translate evidence into insights. This drives an increasing demand for real-world evidence and medical affairs consulting expertise.</p>
<h2>Key Applications of Real-World Evidence</h2>
<p>When real-world data is generated, it&rsquo;s applied differently at every stage of a product&rsquo;s lifecycle, and medical affairs connects these applications together. Real-world evidence transforms raw healthcare data into clinical insights that guide treatment outside of controlled trials.</p>
<p>There are numerous applications:</p>
<ul>
<li><strong>Evidence generation:</strong> Medical affairs increasingly leads the design of observational studies and registries that <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC9815890/" target="_blank" rel="noopener">turn real-world data into usable real-world evidence</a>, filling the gaps left by controlled trials (e.g., being able to apply findings to broader populations) and supporting evidence plans from pre-launch through the end of a product&rsquo;s life.</li>
<li><strong>Health Economics and Outcomes Research (HEOR):</strong> Real-world evidence supports HEOR teams by demonstrating a <a href="https://purplelab.com/resources/how-real-world-evidence-enhances-health-economics-and-outcomes-research" target="_blank" rel="noopener">treatment&rsquo;s cost-effectiveness, potential for optimization, and comparative value</a> by using real-world outcomes and cost data, which is critical evidence for payer negotiations and HTA submissions.</li>
<li><strong>HCP engagement and education:</strong> Medical Science Liaisons use real-world data to provide patient-centered applications, analyzing clinical gaps to tailor peer-to-peer discussions, and gathering field insight that feeds back into evidence strategy.</li>
<li><strong>Post-marketing surveillance: </strong>Real-world data from EHRs, claims, and adverse event databases can easily enable continuous, population-wide safety monitoring, helping detect rate or long-term signals that pre-market trials are too limited to capture. Rather than relying on a single source, multiple data sources are utilized not just in volume, but in systemic capture.</li>
<li><strong>Regulatory submissions and label expansion:</strong> <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC12446098/" target="_blank" rel="noopener">RWE featured in a quarter of FDA labelling expansions granted between 2022 and 2024</a>, most commonly in oncology, indicating the shift towards regulators&rsquo; willingness to accept RWE alongside trial data.</li>
<li><strong>Clinical trial design:</strong> Real-world data is now used to build external control arms for single-arm trials, with <a href="https://pubmed.ncbi.nlm.nih.gov/31175098/" target="_blank" rel="noopener">one glioblastoma study finding this approach cut false-positive error rates up to 30%</a> compared with fixed historical thresholds.</li>
</ul>
<p>These applications explain the way that medical affairs have become the function best placed to translate real-world evidence into decisions that matter, but more importantly, why organizations are increasingly seeking medical affairs and RWE expertise to spearhead these applications as they evolve.</p>
<p><span style="color: rgb(255, 255, 255);"><strong><a class="primaryBtn" style="color: rgb(255, 255, 255);" href="https://redbock.com/contact/" target="_blank" rel="noopener">Need Medical Affairs Expertise? Contact Redbock&nbsp;</a></strong></span></p>
<h2>How Does Evidence Generation Impact Medical Affairs Strategies?</h2>
<p>Beyond the numerous applications of real-world evidence, RWE feeds directly into the core areas of medical affairs strategy.</p>
<p>In scientific communication, <a href="https://medicalaffairsspecialist.org/blog/harnessing-the-power-of-real-world-evidence-to-transform-medical-affairs-and-msl-engagement-strategies" target="_blank" rel="noopener">MSLs increasingly integrate real-world evidence into HCP dialogue</a>, using tools such as Kaplan-Meier survival curves and forest plots to present findings from diverse patient populations alongside trial data, rather than in place of it. This same evidence feeds publication planning, expanding findings beyond a single trial redout and providing a pipeline of real-world outcomes and subgroup data across the product lifecycle.</p>
<p>Real-world data also shapes medical strategy development, identifying gaps early and deciding which studies, registries, or partnerships to prioritize as a product matures. Subsequently, treatment value is also demonstrated clearly. <a href="https://purplelab.com/resources/pharmaceutical-medical-affairs-and-the-role-of-real-world-evidence" target="_blank" rel="noopener">Real-world evidence lets medical affairs show a therapy&rsquo;s real-world performance</a> to HCPs and payers, underpinning value-based conversations that trial data alone can&rsquo;t carry. Additionally, RWE gives medical affairs a shared and credible evidence base for stakeholder engagement.</p>
<p>With this in mind, medical affairs is the bridge between clinical development, healthcare professionals, and patient outcomes. By translating real-world evidence into strategy, rather than simply reporting it, medical affairs is the critical foundation.</p>
<h2>How Can Real-World Data Improve Clinical Trial Outcomes?</h2>
<p>Real-world data has a <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC9815890/" target="_blank" rel="noopener">multitude of advantages for clinical trial outcomes</a>, many of which overcome the traditional barriers of RCTs:</p>
<ul>
<li>No strict eligibility criteria, with fewer chances of no exclusions based on concomitant medications and comorbidities.</li>
<li>Quicker, more cost-effective processes, with less time required for patient recruitment/enrollment and completing the research.</li>
<li>Possibility of undertaking research that can&rsquo;t be done with RCT, such as on high-risk groups like pregnant women and children.</li>
<li>Ability to track real-world patient behavior, with rapid and more straightforward retrieval of and access to data.</li>
</ul>
<p>These benefits align with broader clinical development trends: shorter timelines, more representative enrollment, and evidence packages built around real-world data from the beginning.</p>
<h2>How Does Real-World Evidence Impact Drug Approval Processes?</h2>
<p>Since 2016, RWE has been featured in 25 FDA drug, biologic, and vaccine applications and over 250 device authorizations, with the <a href="https://oncodaily.com/industry/fda-6" target="_blank" rel="noopener">FDA removing a major barrier to submitting real-world data in reviews back in December 2025</a>.</p>
<p>Real-world evidence is filling gaps that trials can&rsquo;t, whether it&rsquo;s rare subgroups or real-world dosing patterns, and subsequently, RWE is supplementing clinical trial data. Research has indicated that most approvals where <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC9299054/" target="_blank" rel="noopener">RWE studies affected decision making </a>were in oncology, infectious disease, and neuroscience. There is substantial drug development effort in each of these areas, and the issues around small patient populations, impracticality of randomized trials, and ethical issues of placebo control arms make RWE use much more favorable.</p>
<p>Long-term safety is another area of drug approval processes where RWE is emerging as a potential solution. Regulators are increasingly relying on real-world data from EHRs and claims to track a treatment&rsquo;s safety profile beyond the initial trial period, helping them to catch signals that only emerge at scale or over time.</p>
<p>Regulatory answers are faster, too, with <a href="https://www.ema.europa.eu/en/documents/report/real-world-evidence-framework-support-eu-regulatory-decision-making-3rd-report-experience-gained-regulator-led-studies-february-2024-february-2025_en.pdf" target="_blank" rel="noopener">DARWIN EU studies now taking a median of four months from protocol approval to results</a>, which is faster than traditional RWE approaches and supports more opportune integration into regulatory timelines.</p>
<h2>The Growing Demand for Medical Affairs and RWE Expertise</h2>
<p><a href="https://redbock.com/expertise/drug-safety-and-medical-affairs/" target="_blank" rel="noopener">Medical affairs </a>and real-world evidence roles are two of <a href="https://redbock.com/news/which-life-sciences-roles-are-most-in-demand-right-now/" target="_blank" rel="noopener">the most in-demand life sciences roles in 2026</a>, with a demand that is outpacing the supply, particularly for specialists such as epidemiologists, biostatisticians, RWE specialists, MSLs, and medical affairs professionals.</p>
<p>US Bureau of Labor Statistics data projects <a href="https://www.bls.gov/ooh/life-physical-and-social-science/epidemiologists.htm" target="_blank" rel="noopener">epidemiologist roles to grow 19% between 2025 and 2035</a>, and <a href="https://www.bls.gov/ooh/math/mathematicians-and-statisticians.htm" target="_blank" rel="noopener">statistician roles, including biostatisticians, by 10%</a>. This outpacing of average job growth isn&rsquo;t exclusive to in-house roles, either. <a href="https://www.towardshealthcare.com/insights/medical-affairs-outsourcing-market-sizing" target="_blank" rel="noopener">The global medical affairs outsourcing market</a>, spanning MSL support, medical writing, and medical information, is projected to grow from $2.9 billion in 2026 to $9.6 billion in 2035, as organizations look to fill talent gaps by outsourcing evidence-generation expertise.</p>
<p>It&rsquo;s never been more essential to understand the current hiring landscape for these highly-skilled roles in order to secure the talent to maintain the increasing growth industry-wide.</p>
<h2>Why Partner with Redbock?</h2>
<p>Redbock is a leading <a href="https://redbock.com/about-us/" target="_blank" rel="noopener">life science consulting firm</a>; we provide a specialist approach to resourcing solutions, with over 600+ active consultants across the world, backed by a global framework. We have the experience and resources needed to expand your team even during high-demand and low-supply hiring markets.</p>
<p>If talent bottlenecks are making it difficult for you to source specialist talent such as medical affairs professionals, MSLs, epidemiologists, and RWE specialists, <a href="https://redbock.com/contact/">get in touch with Redbock today</a> to fill gaps and expand your team.</p>

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Data Analytics vs. Data Science: Which Roles Are in Demand in Life Sciences?

How Real-World Evidence is Changing the Role of Medical Affairs