Job Description

Purpose of Job

Uses advanced techniques that integrate traditional and non-traditional datasets to enable analytical solutions. Applies predictive analytics, machine learning, simulation, and optimization techniques to generate management insights and enable customer-facing applications; Builds analytical solutions leveraging internal and external applications to deliver value and create competitive advantage. Translates complex analytical and technical concepts to non-technical employees.

Job Requirements

Uses advanced techniques that integrate traditional and non-traditional datasets to enable analytical solutions. Applies predictive analytics, machine learning, simulation, and optimization techniques to generate management insights and enable customer-facing applications; Builds analytical solutions leveraging internal and external applications to deliver value and create competitive advantage. Translates complex analytical and technical concepts to non-technical employees.

Tasks:

Identifies and manages existing and emerging risks that stem from business activities and the job role.

Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled.

Follows written risk and compliance policies and procedures for business activities.

Partners with business leaders across the organization to define business problems and research questions; serves as key resource and Subject Matter Expert (SME) on cross functional, matrixed teams to solve highly complex work critical to the organization.

Builds large and complex information sets; Integrates and extracts relevant information from large amounts of both structured and unstructured data (internal and external) to enable analytical solutions.

Conducts advanced analytics leveraging predictive modeling, machine learning, simulation, optimization and other techniques to deliver insights or develop analytical solutions to achieve business objectives.

Serves as key resource and Subject Matter Expert (SME) on efforts to develop scalable, efficient, automated solutions for large scale data analyses, model development, model validation and model implementation.

Works with IT to translate prototypes into new products, services, and features.

Builds and maintains a robust library of reusable algorithms and supporting code, such that research efforts are based on the highest quality data, are transparently conducted, and can be productionized and are reproducible.

Translates complex analytical and technical concepts to non-technical employees to enable understanding and drive informed business decisions.

Maintains academic and industry relationships for the purposes of research; maintains expertise and awareness of cutting-edge techniques, technologies and potential business solutions.

Minimum Requirements:

Master's degree in Computer Science, Applied Mathematics, Quantitative Economics, Statistics, or related field. 6 additional years of related experience beyond the minimum required may be substituted in lieu of a degree.

6 years of experience in predictive modeling, large data analysis and computer science.

Experience in stochastic modeling, machine learning, and other advanced mathematical techniques (e.g., neural nets, simulation, graph analysis).

Solid experience in at least one compiled language (e.g., Java, C or more or more) and one dynamic scripting language (Python, PERL, Ruby).

Experience in data mining and statistical analysis.

Excellent pattern recognition and predictive modeling skills.

Knowledge and application of Data Science principals and experience with data science methodologies.

Experience with the following statistical and predictive modeling approaches: Gaussian Process; Markov Models; Hierarchical Clustering; K-Means; Linear Regression; Logistic Regression; Monte Carlo Simulation; Neural Networks.

Technical skills, consulting experience, and business savvy to interface with all levels and disciplines within our customers’ organization.

Demonstrated ability to communicate ideas with team members and/or business leaders to convey and present very technical information to an audience that may have little or no understanding of technical concepts in data science.