Job Description
Designs, develops and applies programs, methodologies and systems based on advanced analytic models (e.g. advanced statistics, operations research, computer science, process) to transform structured and unstructured data into meaningful and actionable information insights that drive decision making.
Uses visualization techniques to translate analytic insights into understandable business stories (eg. descriptive, inferential and predictive insights).
Embeds analytics into client’s business processes and applications. Combines business acumen and scientific methods to solve business problems.
Requirements
• Defines and develops the value proposition to lead the formulation and definition of analytics solution objectives and technical requirements based on user needs, an understanding of business value industry requirements and advanced analytic models (statistical, operations research, computing, process).
• Conceptualizes, builds, develops, and enhances a client's analytic model. Determines the right modeling methodology to the use case, available structured and unstructured data, cost and timing constraints to solve the large and complex business issues and delivers compelling and clear business focused insights.
• Embeds analytic models into enhanced large scale business processes and operational systems by collaborating with Application Developers.
• As a recognized authority, applies analytic methods and adds to problem domains.
• Using unique visualization techniques, condenses large volumes of complex ideas into elegant and simple visual models.
• Influences a client's strategic decisions by using deep industry expertise and deploying innovative analytics solutions in the operational systems.
Education and Experience Required:
•PhD degree in Statistics, Operations Research, Computer Science or equivalent and 5+ years of relevant experience. Or Master´s Degree in these areas and at least 8 years of relevant experience.
Knowledge and Skills:
• Extensive knowledge of data science methodologies including but not limited to classical regression, neural nets, CHAID, CART, association rules, sequence analysis, cluster analysis, and text mining.
• Ability to translate business requirements into mathematical models and data science objectives to achieve measurable business outcomes.
• Extensive understanding of analytics software (eg. R, SAS, SPSS, Python). Advanced understanding of analytics deployment architectures.
• Extensive machine learning, data integration and mathematical modeling skills and ETL tools (Informatica, AbInitio, Talend).
• Advanced communication and presentation skills.
• Excellent interpersonal skills and effectiveness in working across geographical boundaries.
• In-depth knowledge of programming languages such as Python, SQL, R, SAS, Java, Unix Shell scripting. In-depth knowledge of Hadoop framework desired.
• In-depth knowledge of data visualization techniques and software tools (eg. Spotfire, SAS, R, Qlikview, Tableau, HTML5, D3).
Any Graduate