Research Scientist (Data Scientist)

As a Research Scientist (Data Scientist) at DLH, your primary responsibilities will include designing research studies, analyzing complex health datasets, and developing advanced statistical models and machine learning algorithms. You will collaborate with teams to interpret findings and present results to various stakeholders.

The ideal candidate for the Research Scientist (Data Scientist) position at DLH will hold a Ph.D. in Epidemiology, Biomedical Sciences, Genomics, or a relevant field, along with at least five years of applicable professional experience in data management and analysis. Strong programming skills in languages such as R and Python are essential.

The Research Scientist (Data Scientist) plays a crucial role at DLH by supporting epidemiological and omic studies that address significant public health challenges. Your analysis and interpretation of complex datasets will help inform research and policies that can improve the health outcomes of populations.

Successful candidates for the Research Scientist (Data Scientist) role at DLH should possess excellent analytical and problem-solving skills, advanced knowledge of statistical modeling, and programming expertise. Additionally, strong interpersonal and communication skills are vital for presenting complex data clearly to diverse audiences.

DLH offers an attractive benefits package for its Research Scientist (Data Scientist) employees, including Personal Time Off (PTO), comprehensive medical coverage, a 401(k) retirement plan with matching, and opportunities for career development through training and access to an e-learning suite.

In answering this question, highlight specific past projects where you utilized statistical modeling and data analysis techniques. Discuss the tools you used, your methodologies, and the outcomes of your analyses in a way that showcases your expertise.

Discuss your strategies for effective collaboration, such as fostering open communication, understanding team members' expertise, and validating inputs from diverse fields. Provide examples of successful collaborations and their impact on project outcomes.

Be prepared to discuss specific models and algorithms you have experience with, such as regression models, decision trees, or clustering techniques, and explain how you've applied them in your past work to derive meaningful insights.

Describe the challenges you encountered with your dataset, whether they were related to size, complexity, or data quality issues. Detail your approach to data cleansing, management, and analysis, and what you learned from the experience.

Discuss your knowledge of regulations such as HIPAA and FISMA, and specific measures you've taken to ensure compliance, such as data anonymization, secure data storage practices, and proper access controls.

Stay informed about the latest trends in genomics, such as CRISPR advancements or genetic data analytics. Share your thoughts on how these developments could influence public health research or your work as a Data Scientist.

Use a specific example where your analysis contributed to a key decision, policy change, or successful grant application. Highlight the approach you took to analyze the data and how your insights were utilized.

Explain your approach to simplifying complex data presentations, such as using visuals, clear explanations, and relatable analogies. Provide an example when you successfully communicated complex findings to a non-technical audience.

Discuss the specific programming languages you use in your work, emphasizing how they assist you in data manipulation, analysis, and visualization, along with examples of projects where these tools were critical.

In your answer, reflect on DLH's mission of improving public health and security, and how your personal values align with the company's goals. Discuss your enthusiasm for contributing to impactful research and your passion for data science.