Role Summary

- ConcertAI is a fast-growing healthcare research organization, leading the market in oncology healthcare data analytics. Our dynamic, fun, and highly experienced team is looking for a Senior Statistical Analyst to join us. As a Senior Statistical Analyst in the Real-World Evidence Sciences (RWES) business unit, you will be responsible for statistical analyses conducted using ConcertAI's industry-leading and cutting-edge healthcare data resources. Our team operates in a cross-functional environment with representatives from other functions such as our Scientific Management, Data Curation, Data Products, and Data Science business units.

- As a Senior Statistical Analyst on our team, you will be reporting to an Associate Director in the RWES business unit and will provide high quality analyses and summaries for studies supporting regulatory submissions and health economics and outcomes research (HEOR) studies. Your responsibilities will involve preparing, linking, and manipulating data as well as performing statistical analyses for research projects dedicated to improving our understanding of the patient journey and treatment outcomes in the oncology space, as well as making meaningful impacts on patients' lives. Statistical Analysts are expected to work with a greater degree of independence and require less oversight from the Biostatistics leadership team while developing databases, analyzing data, generating results, and delivering data. Statistical Analysts will also participate in data quality control and review results. The Statistical Analyst will contribute to, and support, corporate goals to progress the company's portfolio of products.

Responsibilities

- Collaborates with Project Managers, Principal Investigators, and other scientific staff to design appropriate study analyses based on project scope and client objectives.

- Reviews and revises study protocols for accuracy, consistency, thoroughness, and quality of statistical methods and presentation.

- Drafts and reviews Statistical Analysis Plans (SAPs) to define eligibility criteria, study measures, and statistical methodologies.

- Creates data structures by determining patient or disease cohorts, establishing study samples, and structuring data files according to research objectives and study design.

- Prepares analysis-ready data by loading, extracting, and transforming data in several databases, as well as searching in schemas, cleaning outbound files, and merging data tables.

- Executes quality control checks of data for anomalies, frequency, and distribution of data points for accuracy and consistency; determines root causes of errors, recommends solutions, and resolves data issues through queries and programming scripts.

- Performs statistical analysis in accordance with SAPs and generates analytic reports, tables, graphics, and slides.

- Writes methodology and results sections of study deliverables such as protocols, summary reports, abstracts, and manuscripts to ensure accuracy of the programming and statistical descriptions.

- Interfaces with Scientific Management and Data Curation team to clarify data requests, extract data sets, and review case report forms, as well as the Data Operations team to assemble and clean data sets.

- Joins client meetings and contributes to the discussion of findings as the statistical lead on assigned projects.

- Manages task timelines and communicates status updates with project team members regarding project requirements, deadlines, and priorities.

- Follows company policy and procedures regarding quality control, data security, and the ethical conduct of research involving human subjects, as well as the provisions of the HIPAA security and privacy rules.

- Participates in other projects as assigned including statistical support roles and contributing to internal initiatives.

Requirements

- Doctoral degree, Master's degree and two years of related programming and statistical experience, or Bachelor's degree and five years of related programming and statistical experience with an area of study in quantitative science such as Statistics, Biostatistics, Analytics, Biometrics, Econometrics, Psychometrics, Operations Research, Engineering, or Data Science.

- Background in scientific research study design and methodology, data analysis, and statistical programming using patient-level datasets.

- Proficiency with a programming language such as:

- Expertise in SQL is required

- Expertise with Pyhon and PySpark

- Expertise with R

- Expertise with SAS (or the ability to quicly learn SAS) and its applications using healthcare data, such as claims or electronic medical records, or patient-reported outcomes is required.

- Experince with GitHub is preferred.

- Experience applying statistical methodologies and advanced mathematical concepts such as ANOVA, linear regression, mixed models, time-to-event analyses, correlation analysis, sampling theory, analysis of categorical data, and appropriate transformations and permutations.

- Experience integrating and processing data (e.g., extracting, transforming, loading, scrubbing).

- Ability to collaborate on multiple projects and deadlines, establish priorities for work activity, and solve practical problems.

- Exceptional verbal and written communication skills with a proven ability to clearly and convincingly present information to a wide range of internal and external audiences.

- Aptitude for understanding and applying best practices from documents such as safety rules, operating and maintenance instructions, procedure manuals, and correspondence.

- Familiarity with basic productivity software (e.g., Microsoft Excel, Microsoft Word, Web Conferencing Applications).

- Detail-oriented, highly motivated, results-driven, and flexible to work in a scaling environment.

Particular consideration will be given to applicants with the following qualifications

- Proficiency with Python.

- Research history within the oncology space related to one or more specific solid tumor types, or to hematological malignancies.

- Working knowledge of external control arms or other use cases of real-world evidence to support regulatory decision-making.

- Understanding of FDA regulatory requirements, ICH guidelines, and GCP.

- Publication track record preferred.