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Afternoon Data Analyst R Programming Jobs in New York

2T Consulting

Senior Statistical Analyst

Montclair, NJ · On-site

We are seeking a Senior Statistical Analyst with strong expertise in SAS and R programming, CDISC standards, and clinical trial data analysis. The ideal candidate will collaborate with statisticians ...

2T Consulting

Senior Statistical Analyst

Bronx, NY

We are seeking a Senior Statistical Analyst with strong expertise in SAS and R programming, CDISC standards, and clinical trial data analysis. The ideal candidate will collaborate with statisticians ...

2T Consulting

Senior Statistical Analyst

Verona, NJ

We are seeking a Senior Statistical Analyst with strong expertise in SAS and R programming, CDISC standards, and clinical trial data analysis. The ideal candidate will collaborate with statisticians ...

2T Consulting

Senior Statistical Analyst

Moonachie, NJ · On-site

We are seeking a Senior Statistical Analyst with strong expertise in SAS and R programming, CDISC standards, and clinical trial data analysis. The ideal candidate will collaborate with statisticians ...

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Afternoon Data Analyst R Programming information

What is an afternoon data analyst r programming?

An Afternoon Data Analyst specializing in R Programming is a data professional who primarily works afternoon shifts and uses the R programming language to analyze, interpret, and visualize data. Their responsibilities typically include cleaning data, performing statistical analyses, and generating reports to support business decisions. They may work across various industries, collaborating with teams to provide insights and automate data processes using R. Afternoon shifts can be ideal for organizations that operate globally or require data support outside standard business hours. Proficiency in R, statistical techniques, and data visualization tools are essential skills for this role.

What are the key skills and qualifications needed to thrive as an afternoon data analyst specializing in R programming?

To thrive as an Afternoon Data Analyst specializing in R Programming, you need a strong background in statistics, data analysis, and proficiency with R, often supported by a degree in a quantitative field. Experience with data visualization tools, R packages (like tidyverse), and familiarity with databases or version control systems (such as Git) is typically required. Critical thinking, attention to detail, and effective communication are essential soft skills for interpreting results and presenting insights to stakeholders. These skills ensure accurate data-driven decisions, efficient workflow, and the ability to translate complex data into actionable business strategies.

What are some common challenges faced by afternoon data analysts working with R programming, and how can they be addressed?

Afternoon Data Analysts using R Programming often encounter challenges such as handling large datasets efficiently, ensuring code reproducibility, and collaborating with team members across different shifts. To address these, it's helpful to utilize R packages designed for big data (like data.table or dplyr), maintain clear and well-documented scripts, and use version control systems like Git for seamless collaboration. Regular communication with team members during shift handovers and leveraging collaborative tools can also enhance workflow and reduce misunderstandings.

What is the difference between Afternoon Data Analyst R Programming vs Morning Data Analyst R Programming?

AspectAfternoon Data Analyst R ProgrammingMorning Data Analyst R Programming
Required CredentialsBachelor's in Data Science, Statistics, or related field; R programming skillsBachelor's in Data Science, Statistics, or related field; R programming skills
Work EnvironmentTypically in office settings, working during afternoon hoursOffice environment, working during morning hours
Employer & Industry UsageUsed in industries with shift-based operations like finance, healthcareCommon in similar industries, often with flexible scheduling
Search & Comparison IntentPeople comparing different shift roles or schedules in data analysisSimilar search intent focusing on shift timing differences

The main difference between Afternoon Data Analyst R Programming and Morning Data Analyst R Programming lies in their work hours. Both roles require similar skills, credentials, and are used in comparable industries. The choice depends on personal schedule preferences and employer shift structures.

What are the most commonly searched types of Data Analyst R Programming jobs in New York?

The most popular types of Data Analyst R Programming jobs in New York are:

What are popular job titles related to Afternoon Data Analyst R Programming jobs in New York?

For Afternoon Data Analyst R Programming jobs in New York, the most frequently searched job titles are:

What job categories do people searching Afternoon Data Analyst R Programming jobs in New York look for?

The top searched job categories for Afternoon Data Analyst R Programming jobs in New York are:

What cities in New York are hiring for Afternoon Data Analyst R Programming jobs?

Cities in New York with the most Afternoon Data Analyst R Programming job openings:

Afternoon Data Analyst R Programming jobs near you

Senior Statistical Analyst

2T Consulting

Mount Vernon, NY

Full-time

Posted 9 days ago

Job description

We are seeking a Senior Statistical Analyst with strong expertise in SAS and R programming, CDISC standards, and clinical trial data analysis. The ideal candidate will collaborate with statisticians and cross-functional teams to develop ADaM datasets, generate TLFs, support regulatory submissions, and ensure high-quality statistical programming deliverables for clinical studies.

Primary Responsibilities

*Collaborate with statisticians to develop and implement Statistical Programming Plans (SPPs) and Statistical Analysis Plans (SAPs).
*Program, validate, and maintain ADaM datasets, statistical analyses, and programming deliverables using SAS and R.
*Generate and validate Tables, Listings, and Figures (TLFs) for clinical study reports, safety reports, and regulatory submissions.
*Create and maintain programming documentation, including specifications, annotated code, validation plans, and programming guidelines.
*Perform quality control and validation of statistical programming outputs to ensure compliance with CDISC standards and regulatory requirements.
*Support ad hoc statistical analyses, exploratory data analyses, and data visualization requests.
*Develop and maintain reusable programming macros, utilities, and automation tools to improve efficiency and consistency.
*Provide programming support during regulatory submissions, audits, and inspections.
*Collaborate with study teams to resolve programming and data-related issues while ensuring timely project delivery.
*Identify project risks, escalate issues when necessary, and provide regular status updates.
*Mentor and train junior programmers and contribute to continuous process improvement initiatives.
*Stay current with industry best practices, regulatory guidelines, and advancements in statistical programming.

Required Skills & Qualifications

*Strong proficiency in SAS and R programming for clinical trial data analysis.
*Experience programming and validating ADaM datasets, TLFs, and statistical analyses.
*Advanced knowledge of CDISC standards, including ADaM and SDTM.
*Solid understanding of clinical trial processes, statistical methodologies, and regulatory requirements.
*Experience in therapeutic areas such as Oncology, Immunology, Neuroscience, or other clinical domains.
*Strong analytical, troubleshooting, and problem-solving skills.
*Excellent attention to detail with a strong focus on quality and compliance.
*Effective written and verbal communication skills.
*Ability to collaborate with statisticians, clinical study teams, and external stakeholders.
*Proven ability to manage multiple priorities and meet project deadlines in a fast-paced environment.

Preferred Qualifications

*Experience supporting global clinical trials and regulatory submissions.
*Knowledge of clinical data visualization and exploratory data analysis.
*Experience developing standardized SAS macros and programming utilities.
*Prior experience mentoring or leading statistical programming teams.
*Familiarity with FDA, EMA, and ICH regulatory guidelines for clinical data submissions.

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