PEGA Data Science and Analytics

Job Title: PEGA Data Science and Analytics
Location: Hybrid, Vienna, VA
Duration: Long-term
Schedule: Monday Friday, 40 hours/week

Description:

To enable and simplify analysis required for faster implementation of new CDH modeling features
Prioritized Deliverables
1. Library of required queries/scripts to replicate the CDH customer contextual object in external systems (databricks/asl) for deeper analysis
2. Standardize format for executing key data retrieval steps for use by the broader team
a. Interaction to outcome attribution (account opens)
b. Model data to interaction mapping (model performance, predictor performance)
c. Member Profile to interaction mapping
3. Create notebooks for the broader team to use to answer specific questions
a. Distribution Analysis
b. Arbitration Analysis
c. Channel Engagement Analysis

Skillset
The primary technical skills required would be familiarity with the databricks environment and proficiency with Python/PySpark and SQL. Pega CDH experience is preferred.
Some examples of the work as it directly relates to GEM
Initial Analysis to Support New Model Related Features
o Propensity Thresholds
Creating the back-testing approach (MDSA had no appetite at the time)
Establishing baseline KPIs
Creating the monitoring approach
o Initial Model Maturity Analysis (though Morgan s team is starting to be involved)
Establishing baseline KPIs
Gauging the impact of enabling the feature
Creating the ongoing monitoring approach
On-going Analysis
o Model Performance Monitoring
Though MDSA owns the code to run the notebooks, when changes must be made to the code GEM is heavily involved in creating the new logic
o NBI Program Model Health
This exists in some form today, but it is not in a state that is readily available to be shared with leaders in O&A, MDSA, or broader Marketing
Broader O&A Analytical Gaps (Things Red, Sumant, and Tai typically scramble to create which should be readily available)
Actionable Monitoring Data: Standardizing how we conduct this sort of analysis for consistency
o Capture when propensity scores are exceptionally low closer to real-time (1 day)
o Capture when actions are not providing value to their intended objective (acquisition, engagement)
Eligible Audience Monitoring
o Identifying Members eligible for different actions/treatments (simulation environment can help after going live to a certain extent)
o Tying interactions back to key Member demographic data for more granular analysis (this sort of analysis should be standardized so it can easily be done by all Members of O&A)