Role Overview
- Defines and develops the value proposition to lead the formulation and definition of analytics solution objectives and technical requirements based on user needs, business value, industry demands, and advanced analytic models.
- Conceptualizes, builds, develops, and enhances client-specific analytic models by selecting the appropriate modeling methodology considering data types, constraints, and business needs.
- Embeds analytic models into large-scale business processes and operational systems by collaborating with application development teams.
- Applies expert-level analytic methods and contributes to expanding problem domains.
- Uses advanced visualization techniques to simplify and present large volumes of complex data into elegant visual models.
- Influences strategic decisions through innovative analytics solutions and deep industry expertise.
Education and Experience Required
- PhD in Statistics, Operations Research, Computer Science, or equivalent with 5+ years of relevant experience
- OR
- Master's Degree in the same fields with at least 8 years of relevant experience
Knowledge and Skills
- Extensive knowledge of data science methodologies such as regression, neural networks, CHAID, CART, association rules, sequence analysis, cluster analysis, text mining
- Strong ability to translate business requirements into mathematical models and measurable outcomes
- Proficient in analytics software including R, SAS, SPSS, Python
- Advanced understanding of analytics deployment architectures
- Expertise in machine learning, data integration, mathematical modeling, and ETL tools (Informatica, Ab Initio, Talend)
- Strong communication and presentation skills
- Effective in cross-functional and cross-geographical collaboration
- In-depth programming skills in Python, SQL, R, SAS, Java, Unix Shell scripting
- Knowledge of Hadoop framework is desirable
- Proficient in data visualization tools such as Spotfire, SAS, R, QlikView, Tableau, HTML5, D3
