R Programmer

We are looking for a talented R Analytics professional to join our analytics team. You should have a strong background in data analysis, statistical modeling, and proficiency in the R programming language. Your main responsibilities will include analyzing complex datasets, providing insights, and developing statistical models to support business decisions. You will be expected to utilize R programming to analyze large and complex datasets, perform data cleaning, transformation, and analysis. Additionally, you will be responsible for developing and implementing statistical models such as regression, time series, and classification to provide actionable insights. Conducting exploratory data analysis (EDA) to identify trends, patterns, and anomalies is also a key part of the role. Furthermore, you will need to visualize data through plots, charts, and dashboards to effectively communicate results to stakeholders. Collaboration with cross-functional teams to define business problems and develop analytical solutions is essential. Building and maintaining R scripts and automation workflows for repetitive tasks and analysis is also part of the job. You should stay updated with the latest developments in R packages and data science techniques. Presenting findings and insights to stakeholders through clear, concise reports and presentations is crucial. Providing technical support and guidance to data analysts and scientists on R-related issues, troubleshooting and resolving R code errors and performance issues, developing and maintaining R packages and scripts to support data analysis and reporting, and collaborating with data analysts and scientists to design and implement data visualizations and reports are also part of the responsibilities. Qualifications: - Bachelor's/Master's degree in Statistics, Mathematics, Data Science, Computer Science, or a related field. - Minimum 3-5 years of experience in a senior role specifically focusing on R Language, R Studio, and SQL. - Strong knowledge of statistical techniques (regression, clustering, hypothesis testing, etc.). - Experience with data visualization tools like ggplot2, shiny, or plotly. - Familiarity with SQL and database management systems. - Knowledge of machine learning algorithms and their implementation in R. - Ability to interpret complex data and communicate insights clearly to non-technical stakeholders. - Strong problem-solving skills and attention to detail. - Familiarity with version control tools like Git is a plus.,