Lead Data Scientist

Mastercard

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Salt Lake City, UT
Salary
$140,000–$231,000 / yr
Posted
85 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $172k
This role $186k
$104k $245k
below market most similar roles pay here above market

This role pays more than 68% of similar roles. Most pay $135,000–$209,018 — the blue band above. At the midpoint, this role pays about $186k versus about $172k for comparable roles.

Based on 240 similar postings.

Employer

About Mastercard

Mastercard is a global technology company in the payments industry, processing transactions between financial institutions and merchants using its extensive network of credit, debit, and prepaid card products. Industry: Payments Technology & Financial Services

Mastercard currently has 155 open roles on FindRole.

Listed pay typically runs $122,000–$207,000 across 136 roles with salary data.

Most-posted roles

View all roles at Mastercard

At a glance

TL;DR · Lead Data Scientist

The Lead Data Scientist joins the Data Science and Analytics team at Finicity, a Mastercard company. In this leadership role, you will mentor the data science team, manage the tech stack, and direct machine learning projects. Your daily responsibilities include designing, implementing, and monitoring production-grade models for financial applications such as transaction classification, temporal analysis, and risk modeling from structured and unstructured data. You will utilize techniques including SVM, Random Forest, XGBoost, LightGBM, CATBoost, LSTM, RNN, Transformers, and LLMs. Required skills include Python, Sklearn, Pandas, TensorFlow, SQL, and experience with Kubernetes, Containers, Docker, and REST APIs. You will solve complex problems in the Open Banking Initiative, focusing on intelligent decisioning, financial certainty, and attribute, feature, and entity resolution.

What you'll do

  • Lead, mentor, and grow the data science team and the production-grade tech stack.
  • Plan and direct data science and machine learning projects within the team.
  • Design and implement machine learning models for transaction classification, temporal analysis, and risk modeling.
  • Measure, validate, monitor, and improve the performance of internal and external machine learning models.
  • Apply various machine learning, deep learning, and LLM techniques to solve analytical problem statements.
  • Develop scalable solutions using best practices in machine learning and data engineering.
  • Present technical problems and findings to business leaders and clients clearly and succinctly.
  • Propose creative solutions to challenges new to the company and the financial industry.

What we're looking for

  • Experience mentoring and leading data science teams.
  • Substantial experience in data science and machine learning model development and deployments.
  • Strong understanding of NLP, Statistical Modeling, Visualization, and advanced Data Science techniques.
  • Experience with Kubernetes, Containers, Docker, REST APIs, Event Streams, or other delivery mechanisms.
  • Familiarity with GenAI, LLMs, Agentic AI, TensorFlow, Python, Sklearn, and Pandas.
  • Exposure to financial transactional structured and unstructured data, transaction classification, risk evaluation, and credit risk modeling (preferred).
  • SQL / Database experience (preferred).
  • Finance and FinTech experience (preferred).
  • PhD or Master’s Degree in Computer Science, Information Technology, Engineering, Mathematics, or Statistics (preferred).

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