ConcertAI is looking to revolutionize healthcare with AI and data solutions to accelerate insights, advance research, and improve patient outcomes in oncology and life sciences. The Product Insights & Evidence (PIE) team needs to support the analytical utilization of ConcertAI data products.
Requirements
- Proficiency with statistical modeling methods, including time-to-event analysis (TTE), and methodology expertise with observational datasets
- Strong proficiency in querying databases with SQL
- Strong proficiency in programming languages (R preferred)
- Experienced conducting studies with medical coding sets (e.g. HCPCS, ICD, CPT, SNOMED, LOINC, and NDC)
- Experience working with EHR-based RWD is a plus
- Familiarity with oncology data and studies is a plus
- Experience working with either Hematological Malignancy or Immunological Disorder is a plus
Responsibilities
- Interpretation and analysis of clinical real world evidence studies
- Contribute to analyses on epidemiological research projects with potential to write and publish abstracts and manuscripts and participate in poster presentations
- Conduct in-depth data investigations across the ConcertAI data pipeline to maintain a high level of data quality, completeness, and usability of ConcertAI data products
- Present key findings to internal and external stakeholders to provide guidance on the usability of ConcertAI data products
- Publish reproducible reports and develop applications to Posit Connect and/or Github for company-wide consumption
- Contribute functions, algorithms, and business rules to ConcertAI R packages and Github repositories to increase efficiency and reproducibility of analyses
Other
- Advanced degree in Epidemiology/Biostatistics or 2+ years of epidemiological research in a health care, pharmaceutical, or health-related academic field
- 2+ years of relevant work experience in epidemiological research, oncology and/or RWD
- Proactive and enjoys the data discovery process, digging deep and getting hands-on with data
- Has an inquisitive mindset to constantly test assumptions and effectively communicate findings to both a technical and non-technical audience
- Oncology work experience
