Epidemiologist

Paddington
2 weeks ago
Create job alert

Epidemiologist

Contract Position - 18 months +

Location: London - Hybrid working

Job Overview
Work closely with global clients and other functional groups by providing epidemiology leadership for real world studies on the full spectrum of designs and approaches from early clinical development through the post approval stage, which include but are not limited to the natural history of disease, external comparators, and comparative safety and effectiveness of drugs, biologics, and devices, under supervision of senior staff. Design methodologically sound studies to meet project objectives and stakeholder requirements (e.g., regulatory, payers, etc.). Provide input on the data elements and design of eCRFs (when applicable) as well as input and review of feasibility and data landscaping. Conduct and review analyses, evaluate and provide reporting of studies. Assignments range in complexity from providing input into feasibility to the development of study protocols and clinical study reports, ensuring data quality and scientific rigor across all facets of real world studies. May contribute to new business development (as appropriate) to maintain and strengthen client base.

Essential Functions

  • Leads design and implementation of epidemiology/ pharmacoepidemiology methods in real world studies and studies using real world data, including (but not limited to) drug safety and effectiveness studies and other observational or low intervention studies using real world data and/or methodology.
  • Support senior staff on specific research initiatives as needed.
  • Serves as project lead on smaller projects or in support of project lead on larger client facing or internal projects.
  • Authors of protocols, reports and other study documents with independent, critical thinking to ensure quality and completeness of output, oversees timeline for deliverables associated with analysis and reporting with input and oversight of senior staff.
  • Reviews and provides relevant epidemiological research input to statistical analysis plans and analysis output.
  • Reviews and provides epidemiology input for tasks including CRF/eCRF development, form previews and other ad hoc project tasks (e.g., regulatory responses, slide deck development).
  • Interacts with clients with senior staff involvement as needed.
  • Identifies client-related, budget-related and internal issues that may require attention or escalation.
  • Use best efforts to complete work with available budget.
  • Contributes to intra- and interdepartmental process improvement to achievebest practicesand to support effective delivery and quality of deliverables.
  • May generate content and direction for business development proposals on smaller projects with input and oversight from senior staff.
  • May represent our client externally through conference presentations.
  • May oversee or conduct statistical analysis as needed.
  • May contribute to the development of high quality proposals for new projects.
  • May contribute to the development of best practices in epidemiology and observational research and other internal initiatives.

    Qualifications
  • Masters Degree Graduate education in epidemiology, pharmacoepidemiology, public health with concentration in epidemiology, pharmacy with concentration in epidemiology or relevant scientific field and 5 years relevant experience Req Or
  • Ph.D. with 2 years relevant experience Pref
  • Sound methodological training in epidemiology, pharmacoepidemiology, public health with concentration in epidemiology, pharmacy with concentration in epidemiology or related area relevant to observational health research.
  • Ability to design, plan and conduct observational studies of comparative effectiveness and safety.
  • Excellent oral and written communication skills, medical writing experience beneficial.
  • Ability to establish and maintain effective working relationships with coworkers, managers and clients in a global and matrixed environment.
  • Exceptional attention to detail and the ability to effectively prioritize and manage multiple tasks.
  • Ability to work collaboratively with diverse team members.
  • Must be proficient in Word, Excel, PowerPoint and Edge. Ability to read outputs from SAS, R or other programming languages.
  • Must be highly organized and self-motivated with ability to determine and meet objectives

Related Jobs

View all jobs

Epidemiologist

Risk Analyst (7079 & 7073 & 7071)

!*Observational Research Manager -Leading Biotechnology - Homebased in the UK!*

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.