A Data Scientist's main mission is to collaborate with the business operations team to answer business questions by using data mining techniques, including pattern detection, graph analysis or statistical analysis. This position is an advanced technical professional role responsible for exploring and analyzing data across the enterprise and presenting information and key insights that supports the organization's strategies.
Requirements
- At least 4 years of experience designing, managing, developing and analyzing multiple large complex data models spanning multiple disparate sources.
- Proven experience in conducting statistical analysis and building models with advanced scripting languages
- Experience leading projects that apply ML and data science to business functions
- Specialization in text analytics, image recognition, graph analysis, or other specialized ML techniques, such as deep learning, is preferred
- Fluency in multiple programming languages and statistical analysis tools such as Python, C++, JavaScript, R, SAS, Excel, SQL, MATLAB, SPSS
- Knowledge of statistical and data mining techniques such as generalized linear model (GLM)/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, convolutional neural network (CNN), recurrent neural network (RNN)
- Strong understanding of AI, its potential roles in solving business problems, and the future trajectory of generative AI models
Responsibilities
- Performs large-scale experimentation and builds data-driven models to answer business questions
- Proactively mines data warehouses to identify trends and patterns and generates insights for business units and senior leadership
- Performs large-scale experimentation to identify hidden relationships between variables in large datasets
- Researches and implements cutting-edge techniques and tools in machine learning/deep learning/artificial intelligence to make data analysis more efficient
- Apples statistical analysis and visualization techniques to various data, such as hierarchical clustering, T-distributed stochastic neighbor embedding (t-SNE), or principal components analysis (PCA) machine learning
- Designs and conducts data analyses with the highest standard of rigor and scientific accuracy; this includes study design, methodology, algorithms, and statistical modeling
Other
- Translates business needs into analytics/reporting requirements to support executive decisions and workflows with required information
- Ability to communicate complex projects, models, and results to a diverse audience with a wide range of understanding
- Ability to work in diverse, cross-functional teams in a dynamic business environment
- Superior presentation skills, including storytelling and other techniques to guide and inspire.
- Willingness and ability to learn new technologies on the job
