SAS Clinical Programmer at RangTech
New York, NY
About the Job
Our Profession is our Passion! We atRangTechnologiesspecialize in planning, implementing, managing, and staffing InformationTechnology Solutionsand Services. Our goal is to help our clients define their investments in technology, optimize processes that bring fast-track success, and deliver business-critical applications to improve performance. We value diversity. We operate as one team, bringing highly qualified and experienced software and management professionals with advanced degrees in Computer Science, Management, Engineering, Statistics and Bioinformatics.
The values that drive our success are:
Strategy:Wehelpour clients growth by achieving competitive advantage and creating value.
Quality:We believe in Quality First and Quality Must in solutions we deliver.
Support:We bring ideas and talent to empowerbusiness performancewith new-age technologies.
We take pride inbuildingstrategiclong-term relationship with our clients.
Job Description
Job description:-
Created and Reviewed SDTM specification and Annotated eCRF according to SDTM variables.
Extensive working knowledge on CDISC/SDTM standards.
Created various SDTM datasets based on the specifications and SDTM IG as per requirement and standards of the company.
Created various SDTM datasets (SDTM IG 3.2, 3.1.3) and ADAM datasets as per Specifications.
Involved in QC/validations of various SDTM and ADaM domains.
Worked with all kinds of Clinical Trials data such as Demographic data, Adverse Events (AE), Serious Adverse Events (SAE), Laboratory data (Lab data), and Vital Signs.
Involved in performing several edit checks on data and thereby make the data clean.
Worked on Open CDISC validator to check the consistency of data in various domains.
Generated tables, listings and graphs with the help of SAS/BASE and SAS/SQL in Windows environment.
Developed and Validated SAS programs to produce analysis datasets and reports including Non-standard Ad-hoc requests.
Independently investigating data issues and solving technically complex problems.
Accuracy, completeness, quality, and timeliness of clinical programming deliverables.
Generated analysis datasets with derived variables and performed statistical analysis on data as per the requirements in the SAP and Protocol.
Worked on SAS utility macros.
Qualifications
Bachelors degree in life science, statistics, mathematics or related field is required; Masters degree is preferred in Statistics or Biostatistics
8 plus years Pharmaceutical/CRO experience as a SAS Programmer supporting clinical trials for regulatory submissions with a Bachelors degree; or 5 plus years experience with a MS/MA degree.
Demonstrated proficiency in using SAS to produce derived analysis datasets and produce TFLs.
Thorough understanding of clinical data structures, relational database structures, and data exchange with alternate data formats.
Demonstrated skills in using additional software tools and applications (e.g. MS office, XML) and expertise in the handling and processing of upstream data, e.g., multiple data forms, workflow, eDC, SDTM.
Experience with Biomarker data analysis and statistical modelling Demonstrated Demonstrated expertise in providing outputs to meet downstream requirements, e.g., ADaM, Data Definition Table, e-submission.
In-depth understanding of regulatory, industry, and technology standards and requirements.
Good knowledge of statistical terminology, clinical tests, medical terminology, and protocol designs.
Demonstrated ability to work in a team environment with clinical team members.
Good interpersonal, communication, writing and organizational skills.
Additional Information
All your information will be kept co- MS in Finance, Financial Engineering, Analytics or Mathematics, Computer Science, Statistics, Industrial Engineering, Operations research, or related field.
- Good understanding of Probability of Default (PD), LGD and EAD modeling technique.
- Very good understanding of Predictive modeling techniques and their application.
- Knowledge of Credit life cycle.
- Statistics and machine learning techniques.
- Conducted and applied statistical methodologies including linear regression, logistic regression, ANOVA/ANCOVA, CHAID/CART, cluster analysis
- Team player and collaboration skills.
- Programming skills in R, SAS, and PYTHON.
- Fluency with Excel, PowerPoint and Word
- Strong written and oral presentation / communication skills must have the ability to convey complex information simply and clearly
nfidential according to EEO guidelines.