This role pays more than
55%
of similar roles. Most pay
$126,800–$234,184
— the shaded band above.
At the midpoint, this role pays about
$184k
versus about
$180k
for comparable roles.
Based on 240 similar postings.
Employer
About Gen Digital
Gen Digital is a US-based consumer cybersecurity company formed by the merger of NortonLifeLock and Avast. It sells identity protection, antivirus, VPN, and privacy software under the Norton, Avast, AVG, Avira, LifeLock, ReputationDefender, and CCleaner brands.
Gen Digital currently has
23 open roles
on FindRole.
Listed pay typically runs
$172,240–$190,140
across 16 roles with salary data.
Staff Data Scientist joins the Data Science team at Engine by MoneyLion to power offer recommendations, dynamic pricing, and enhanced decisioning across a complex marketplace for financial products. This role involves owning critical model systems end-to-end, including data engineering, feature pipeline management, development, deployment, and real-time serving. The successful candidate will build production ML models for conversion prediction and lead large-scale A/B tests to drive revenue impact. Key technical requirements include proficiency in Python, SQL, and ML frameworks, alongside experience with data warehouses like Redshift or Snowflake, and tools such as dbt, Airflow, Spark, SageMaker, and MLflow. The role also requires software engineering skills in Python, Scala, or Java, along with containerization using Docker and Kubernetes to manage the lifecycle of models within a high-traffic fintech marketplace environment.
What does a Data Scientist earn in New York?
Median $173200 from 38 postings across 17 companies.
Develop production ML models for real-time recommendations, pricing, and conversion prediction across the marketplace.
Build and manage feature pipelines and data transformations using SQL, dbt, and Airflow in warehouse environments.
Design and analyze large-scale A/B tests to drive actionable product and model improvements.
Partner with engineering teams to deploy, monitor, and maintain ML models in production serving infrastructure.
Translate complex business problems from stakeholders into well-scoped data science projects.
Establish team best practices for code quality, documentation, experiment design, and reproducibility.
Contribute to the development of MLOps platforms and tools for scalable model lifecycle management.
Present technical findings and strategic recommendations to executive and non-technical stakeholders.
What we're looking for
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Physics, Economics, or a related quantitative field (or equivalent professional experience).
7+ years of experience across data science, machine learning, and data engineering.
Experience designing and shipping production ML models using Python, SQL, and ML frameworks.
Experience building real-time or near-real-time ML systems for recommendations, pricing, bidding, or similar use cases.
Proficiency with data warehouse technologies (Redshift, Snowflake, BigQuery) and pipeline tools like dbt, Airflow, and Spark.
Strong foundation in statistics, probability, experiment design, and machine learning theory.
Experience with ML platforms and infrastructure such as SageMaker, Spark, Ray, or MLflow.
Ability to perform software engineering tasks including Python/Scala/Java, Docker, Kubernetes, and CI/CD (preferred); experience in fintech or marketplace environments (preferred).