Payment Optimization Data Scientist II
Worldpay · Cincinnati, OH
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Jun 29, 2026
Cincinnati, OH
Jul 20, 2026
What this job asks for AI summary
Senior level · 4+ years · Remote · Master's required · Full-time
Advertised as Mid, but the requirements read as Senior.
“or” means any one of them counts — you don't need all of them.
Posted 2 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 7,700 people nationally plausibly meet what this posting asks for (data scientists). range 2,250–13,900
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (8)
The posting's title is 'Data Scientist II', which conventionally signals a mid-level role; however, the requirements demand 4 years of highly specialized experience (production ML, reinforcement learning, causal inference, large-scale feature engineering, A/B testing, and model drift monitoring), placing the actual scope firmly at Senior.
The degree requirement is a hard gate: a Master's degree (or foreign equivalent) in CS, Data Science, Computer Engineering, or a related field is explicitly required — equivalent experience is NOT offered as a substitute.
SAS, Python, R, Scala, and SQL are listed together as a group of 'data science tools and programming languages'; Python and SQL are the most universally required in the detailed requirements narrative, so they are emitted as standalone must-haves. SAS is emitted with R and Scala as named alternatives since the posting presents them as a set.
CatBoost and XGBoost are listed as interchangeable boosting/ensemble methods for the same requirement; XGBoost is used as the primary name with CatBoost as the alternative.
K-Means and DBSCAN are listed as interchangeable unsupervised clustering algorithms for the same segmentation requirement; K-Means is the primary with DBSCAN as the alternative.
No compensation figures are provided in the posting.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): time-series modeling.
Caller marked this a fully-remote role — scored against the national candidate pool.
Read the full posting
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