Qualification: Bachelor’s degree in Mathematics, Statistics, Computer Science or a related discipline
Experience: 7 plus years with 3-5 years of experience in statistical analysis or data science.
Skills: Python, R, PHP, Hadoop/Big data, Data Science/Data Analytics, Mathematics, Statistics, Algorithms and probability engines
- Experience manipulating large datasets and using databases with experience in general purpose programming language such as Hadoop.
- Ability to develop solutions to loosely defined business or research problems by leveraging pattern detection over large data sets.
- Experience using machine learning algorithms.
- Strong interpersonal skills with the ability to communicate with technical and non-technical internal and stakeholders.
- Ability to organize, analyze and correlate data information.
Role & Responsibilities:
- Construct advanced predictive models, algorithms and probability engines to support data analysis or product functions; verify model and algorithm effectiveness based on real-world results.
- Design experiments and methodologies to generate and collect data for business use.
- Identify what data is available and relevant, including internal and external data sources, leveraging new data collection processes such as sensors, open data, and social media feeds.
- Design experiments, test hypotheses, and build models to explore complex business and safety science systems.
- Conduct data analyses using traditional and complex statistical methods. Design algorithms and implements software to perform analyses.
- Aggregate, transform and integrate multiple data sets to test hypothesis, model complex systems and identify patterns. Prepare and analyze program/model output to allow for efficient data analysis.
- Transform complex data and results into creative graphs, tables, charts and other graphics to present and/or share the data in a compelling and concise manner.
- Write methodology, analysis and data insights for research papers, proposals and presentations. Synthesize all aspects of a data science project to lead non-technical audiences through the goals, methods and implications of the project.
- Work with business leaders and researchers to suggest other projects and initiatives that will advance the organizations goals.