Senior Medical Statistician/Medical Statistician, RWE

The Senior Medical Statistician at Holmusk is responsible for designing and conducting studies using real-world data to evaluate clinical effectiveness in psychiatry. This includes ensuring data quality, collaborating with a multidisciplinary team, writing scientific proposals and reports, and interpreting results from analyses. The role requires a proactive approach to communicate complex concepts effectively.

Candidates for the Senior Medical Statistician role at Holmusk need a relevant post-graduate qualification, such as an MSc or PhD in Statistics, Epidemiology, or a related discipline. Additionally, at least 3 years of programming experience with R and/or Python, along with a solid understanding of SQL and observational research methods, is essential.

For the Senior Medical Statistician position at Holmusk, experience in epidemiological and medical statistics methods is crucial. This includes knowledge of survival analyses and propensity score methods. Those with backgrounds in psychometrics and health economics will find their expertise highly valued.

At Holmusk, Senior Medical Statisticians will work in a mature start-up environment that promotes collaboration and innovation. You will be part of a diverse team passionate about solving complex problems in mental health. Furthermore, the company supports remote work with a flexible schedule to accommodate in-person meetings.

The role of Senior Medical Statistician at Holmusk provides significant opportunities for personal and professional growth. As the company scales, you will have the chance to take ownership of projects, collaborate with large pharmaceutical companies, and contribute to impactful real-world evidence generation.

In my previous roles, I utilized R for data analysis, implementing various statistical methodologies relevant to mental health research. This included creating robust datasets and using R for data visualization to communicate findings to both technical and non-technical stakeholders.

To ensure data quality, I adopt a systematic approach that includes validating raw data, implementing checks for consistency and completeness, and documenting my assumptions and methodologies. This not only enhances reliability but also facilitates reproducibility in research.

Collaboration in a multidisciplinary team requires effective communication and respect for diverse perspectives. I prioritize regular check-ins, clear definitions of roles and deliverables, and an openness to feedback, which encourages a cohesive working environment.

One challenging problem involved dealing with incomplete datasets. I applied imputation techniques to estimate missing values while exploring the impact on study validity. This approach allowed me to maintain the integrity of the analysis while proceeding with the study timeline.

I focus on tailoring my communication style based on the audience. For technical audiences, I delve into statistical details and methodologies, while ensuring clarity for non-technical audiences by summarizing key findings with visuals and plain language.

I frequently used SQL to retrieve and manipulate large datasets from relational databases. Using SQL allowed me to perform necessary data cleaning and transformation before conducting any statistical analysis, ensuring that my datasets were analysis-ready.

To manage tight deadlines effectively, I prioritize tasks based on urgency and importance, employing project management tools to track progress. I also engage with my team to delegate responsibilities, ensuring that we maintain quality while adhering to timelines.

I have substantial experience with observational research methods, particularly in mental health studies. Techniques such as propensity score matching and survival analyses have been integral to evaluating the effectiveness of interventions in real-world settings.

I stay current by regularly reading relevant literature, attending workshops and webinars, and participating in professional networks. This ongoing education ensures that I am informed about the latest statistical techniques and best practices in epidemiological research.

To design a study using real-world data in psychiatry, I would first identify a relevant research question, ensuring it addresses a significant gap in existing literature. I would collaborate with stakeholders to define the methodology and select appropriate datasets, followed by developing a clear analysis plan to interpret the findings.