Model Management Analyst  

Model Scoring & Refresh

• Helps refresh the models built & produce the updated scores with new data on a regular interval
• Coordinate with EDO Operations team in the maintenance of regular scoring jobs
• Coordinates with business users on their requirements for regular scoring

Model decay tracking & analysis

• Helps in producing the regular analysis on the different models built & supports measurements of its accuracy rates, lift %s on the defined sample population
• Regular tracking of the models decay & report it back to modeling team for the necessary actions
• Support model manager in benchmarking study as per the Industry standards

Value Measurement

• Help translate model into results. Supports in drawing out and communicating useful insights, actionable interpretations, alternative approaches and solutions.
• Conduct the necessary analysis with BU’s to measure uplift %s & convert into the increase in business revenue, reduction in cost / value to serve
• Help in driving operationalization of analytic findings in collaboration with the business sponsors and the different stakeholders together with evaluation of the analytical models and the effectiveness of various customer-focused campaigns.
• Develop relationships with external data and analytics partners and interact as needed.

• Degree in quantitative discipline such as Statistics, mathematics, Operations Research, Engineering,
• Computer Science, Econometrics or Information Science such as Business Analytics or Informatics
• Has Knowledge of both structured and unstructured data
• Must possess core competencies, or understanding and relevant experience in:
• Scripting or programming experience: familiarity in programming languages with relational databases (e.g. Python, Java, Ruby, Clojure, Matlab, Pig, SQL)
• Statistical Analysis: advanced usage of off-the-shelf tools such as R, SAS, SPSS, Weka and other analytical tools or software
• Big Data: Experience with Big data tools such as HDFS, Cassandra, Storm
• Database knowledge: skilled in structured database
• Familiar with most of the following disciplines:
• Conceptual modeling: to be able to share and articulate modeling;
• Predictive modeling: most of the big data problems are towards being able to predict future outcomes;
• Hypothesis testing: being able to develop hypothesis and test them with careful experiments;
• Natural Language Processing: the interactions between computer and humans;
• Machine learning: using computers to improve as well as develop algorithms;
• Statistical analysis: to understand and work around possible limitations in models.