Data Scientist 2 - Remote
DivIHN (pronounced “divine”) is a CMMI ML3-certified Technology and Talent solutions firm. Driven by a unique Purpose, Culture, and Value Delivery Model, we enable meaningful connections between talented professionals and forward-thinking organizations. Since our formation in 2002, organizations across commercial and public sectors have been trusting us to help build their teams with exceptional temporary and permanent talent. Visit us at https://divihn.com/find-a-job/ to learn more and view our open positions. Please apply or call one of us to learn more For further inquiries regarding the following opportunity, please contact our Talent Specialist, Sri at (630) 847-0953 or Remy at (224) 394-4902 Title: Data Scientist 2 - Remote Duration: 9 Months Location: Remote Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered. Meet the team: Welcome to our dynamic and innovative Behavioral Science team! We're on a mission to revolutionize patient care by applying cutting-edge insights from behavioral science to develop our strategy, product pipeline, and beyond. As a Behavioral Data Scientist, you'll join a passionate group of researchers and professionals who thrive on experimentation, collaboration, and curiosity. Our team's core focus is to enhance our stakeholders' outcomes and experiences, and your role will be instrumental in driving this mission forward. If you're excited about pushing the boundaries of medical technology, contributing to meaningful research, and working with a team that values your unique contributions, this is the place to be. Responsibilities: You will be responsible for conducting statistical analysis, data visualization and translation of results using best practices and statistical methods to provide meaningful insights to clinical, medical, and commercial teams. You will manage large and diverse datasets, including data ingestion, documentation, cleaning, quality control checks, and storage in cloud environments. You will prepare summary statistics, tables and figures for reports, presentations and publications, write descriptions of analytical methods, and draw conclusions from analysis results. You will be responsible for comprehensively synthesizing and translating research findings, and effectively communicating actionable insights and recommendations to stakeholders at all levels of the organization. You will collaborate with cross-functional teams, including product development, medical affairs, and clinical education, to translate quantitative insights for HCP- and patient- facing communications. You will be expected to stay updated on industry trends, methodologies, and best practices in quantitative research, continuously refining skills and approaches to deliver high-quality insights. You will perform other responsibilities as assigned and requested. What makes you successful: Your academic background provides a strong foundation of knowledge in statistical analysis skills with application to diabetes management, psychology, and/or physiology. You have firsthand experience working with large datasets and in big data cloud environments for quantitative analysis, statistical analysis, and statistical modeling; you are proficient in Python and/ or R programming; and you have experience visualizing data in programming environments. You have experience planning and implementing data collection, organizing collected data, and using data collection tools like Qualtrics. Experience working in a research role within a relevant industry (e.g., technology, healthcare). Your excellent written and verbal communication skills facilitate the sharing of insights with colleagues and enable staying in sync with your team and manager. You are a team player, excited to work in a collaborative data science setting on a motivated team. You are well-organized, detail-oriented, employ effective time management skills, and are proficient with software tools like Microsoft Office Suite. You are exceptionally curious and possess critical thinking abilities to help navigate research challenges, encouraging you to ask tough questions and delve deeper into topics. Required Skills: Strong foundation of knowledge in statistical analysis skills with application to diabetes management, psychology, and/or physiology Firsthand experience working with large datasets and in big data cloud environments for quantitative analysis, statistical analysis, and statistical modeling; you are proficient in Python and/ or R programming; and you have experience visualizing data in programming environments Excellent written and verbal communication skills facilitate the sharing of insights with colleagues and enable staying in sync with your team and manager Preferred Skills: Experience planning and implementing data collection, organizing collected data, and using data collection tools like Qualtrics Experience working in a research role within a relevant industry (e.g., technology, healthcare) Well-organized, team player, detail-oriented, employ effective time management skills, and are proficient with software tools like Microsoft Office Suite Software Skills: Python or R for statistical programming Education: Requires a Bachelor's degree in a technical discipline and a minimum of 2-5 years related experience, or Master's degree and 0-2 years' equivalent experience. BS or BA, preference for Masters degree About us: DivIHN, the 'IT Asset Performance Services' organization, provides Professional Consulting, Custom Projects, and Professional Resource Augmentation services to clients in the Mid-West and beyond. The strategic characteristics of the organization are Standardization, Specialization, and Collaboration. DivIHN is an equal opportunity employer. DivIHN does not and shall not discriminate against any employee or qualified applicant on the basis of race, color, religion (creed), gender, gender expression, age, national origin (ancestry), disability, marital status, sexual orientation, or military status. Python, Healthcare, Psychology, Microsoft Office Suite, R programming
