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For Companies At a Glance
- Tasks: Join our team to enhance cancer care through data curation and machine learning.
- Company: Be part of a mission-driven organisation focused on transforming oncology with integrity and innovation.
- Benefits: Enjoy a hybrid work model, flexible office days, and opportunities for professional growth.
- Why this job: Make a real impact in healthcare while collaborating with passionate professionals in a dynamic environment.
- Qualifications: PhD or Master's in relevant fields with experience in real-world data and strong analytical skills.
- Other info: Extra credit for oncology experience and familiarity with AI/ML tools.
The predicted salary is between 28800 - 48000 £ per year.
Reimagine the infrastructure of cancer care within a community that values integrity, inspires growth, and is uniquely positioned to create a more modern, connected oncology ecosystem.
We’re looking for a Research Scientist to help us accomplish our mission to improve and extend lives by learning from the experience of every person with cancer. Are you ready to be the next changemaker in cancer care?
What You\’ll Do
In this role, you will work as a member of the Research Sciences (RS) department, primarily supporting the Data Curation team within the Core Technologies organization. This team thinks holistically about our end-to-end data collection, curation, and processing to build scaled real-world data solutions. As an RS member on this team, you will support the development and validation of new real-world variables and datasets built using various curation approaches, including human chart review and artificial intelligence/machine learning (AI/ML).
In this role you will:
- Design and execute studies to characterize the quality and inform fit-for-purpose use of RWD solutions
- Work collaboratively with cross-functional stakeholders such as project managers, ML model developers and oncologists to execute on analyses that inform variable development and quality in an accurate, effective, and timely manner
- Build subject matter expertise in real-world data curation approaches and fit-for-purpose of Flatiron panoramic data solutions for real-world evidence use cases
- Contribute to improvement initiatives that introduce efficiency into our ways of working and accelerate our ability to generate fit-for-purpose scaled RWD solutions
- Support discussions with clients about the utility of panoramic solutions for customer use cases
- Develop external analytic guidance and support communications with clients for use of AI/ML extracted data
Who You Are
You\’re a kind, passionate and collaborative problem-solver. In addition, you’re an analytical thinker and excellent communicator with experience developing real-world evidence from real-world data (e.g., healthcare claims or electronic health records). You are excited by the prospect of rolling up your sleeves to tackle meaningful problems each and every day.
- You hold a PhD degree in Epidemiology, Biostatistics, Health Economics and Outcomes Research (or a closely related field) with 1-2 years of relevant experience (can include work experience before graduate school), or a Master’s degree in one of these fields with 4 – 5 years of relevant experience
- You have a deep understanding of observational data and have a track record of success applying epidemiologic approaches to problems in public health and/or pharmacoepidemiology
- You are familiar with AI/ML tools used for unstructured data processes
- You have strong problem solving skills and have experience breaking down complex ambiguous problems and driving towards results in a complex stakeholder environment
- You are proficient programming in R
- You have strong organizational, time-management, prioritization and decision-making skills necessary to evaluate, plan and implement multiple high-visibility projects in a timely fashion
- You have the ability to work effectively in a constantly changing, diverse, and matrix environment
- You are able to quickly learn and apply new information, skills and procedures
- You are passionate about our mission to improve healthcare through technology
Extra credit
- You have oncology experience
- You have experience working in a pharmaceutical/HEOR consulting environment
Where you’ll work
In this hybrid role, you’ll have a defined work location that includes work from home and 3 office days set by you and your team. For more information on our approach to hybrid work, please visit the how we work website.
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Epidemiologist/Biostatistician, Machine Learning Quality employer: Flatiron Health
Contact Detail:
Flatiron Health Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Epidemiologist/Biostatistician, Machine Learning Quality
✨Tip Number 1
Familiarise yourself with the latest trends in real-world data (RWD) and machine learning (ML) applications in oncology. This knowledge will not only help you during interviews but also demonstrate your genuine interest in the field.
✨Tip Number 2
Network with professionals in the epidemiology and biostatistics fields, especially those who have experience in RWD and AI/ML. Attend relevant conferences or webinars to make connections that could lead to valuable insights or referrals.
✨Tip Number 3
Prepare to discuss specific projects where you've applied epidemiologic approaches to solve complex problems. Highlight your analytical thinking and problem-solving skills, as these are crucial for the role.
✨Tip Number 4
Showcase your programming skills in R by discussing any relevant projects or analyses you've conducted. Being able to demonstrate your technical proficiency will set you apart from other candidates.
We think you need these skills to ace Epidemiologist/Biostatistician, Machine Learning Quality
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in epidemiology, biostatistics, and machine learning. Emphasise any projects or roles that demonstrate your ability to work with real-world data and your familiarity with AI/ML tools.
Craft a Compelling Cover Letter: In your cover letter, express your passion for improving cancer care and how your skills align with the company's mission. Mention specific experiences that showcase your problem-solving abilities and collaborative nature.
Showcase Relevant Skills: Clearly outline your programming proficiency in R and any other relevant technical skills. Provide examples of how you've applied these skills in previous roles, particularly in relation to observational data and public health.
Highlight Collaborative Experience: Since the role involves working with cross-functional teams, include examples of past collaborations with project managers, oncologists, or data scientists. This will demonstrate your ability to thrive in a diverse and matrix environment.
How to prepare for a job interview at Flatiron Health
✨Showcase Your Analytical Skills
As an Epidemiologist/Biostatistician, it's crucial to demonstrate your analytical thinking during the interview. Be prepared to discuss specific examples of how you've applied epidemiologic approaches to real-world data problems, especially in public health or pharmacoepidemiology.
✨Familiarise Yourself with AI/ML Tools
Since the role involves working with AI/ML tools for unstructured data processes, make sure you can talk about your experience with these technologies. Highlight any projects where you've successfully integrated AI/ML into your work, as this will show your capability to adapt to modern data curation methods.
✨Prepare for Collaborative Scenarios
Collaboration is key in this role, so be ready to discuss how you've worked with cross-functional teams in the past. Think of examples where you effectively communicated with project managers, ML developers, or healthcare professionals to achieve a common goal.
✨Demonstrate Your Passion for Healthcare Improvement
The company values individuals who are passionate about improving healthcare through technology. Be sure to convey your enthusiasm for the mission and share any personal experiences or motivations that drive your interest in cancer care and real-world evidence.
