Staff Data Scientist

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Staff Data Scientist role at MoneyLion, focusing on developing ML models for real-time solutions and optimizing data-driven strategies within a hybrid work environment.

Posted 7/28/2026full-timeNew York City • New York • 🇺🇸 United StatesLead💰 $176,000 - $191,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying production ML models, managing data pipelines, and conducting A/B testing to drive actionable insights. Proficient in collaborating with cross-functional teams to translate complex business challenges into data-driven solutions.

Highest-signal resume keywords

Production ML Model DevelopmentData Pipeline ManagementA/B Testing and Experiment DesignPython and SQL ProficiencyMLOps Platform Contribution

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData EngineeringStatisticsExperiment DesignPythonSQLML FrameworksData WarehousingReal-Time ML SystemsSoftware Engineering

Soft Skills

Excellent Communication

Tools & Technologies

RedshiftSnowflakeDbtAirflowSageMakerSparkRayMLflowDockerKubernetes

Industry Keywords

FintechFinancial ServicesMarketplaceAuction Environments

Tech Stack

Tools & technologies

AirflowAmazon RedshiftBigQueryDockerJavaKubernetesPythonRayScalaSparkSQL

About the role

Key responsibilities & impact

Own and develop production ML models for real-time recommendations, pricing, and conversion prediction across the Engine marketplace.
Design, build, and manage feature pipelines and data transformations in our data warehouse (e.g., Redshift, Snowflake) using tools like dbt, Airflow, and SQL to ensure high-quality, timely features for model training and serving.
Lead the design, execution, and analysis of large-scale A/B tests and experiments, translating results into actionable product and model improvements.
Collaborate closely with product managers, partner managers, and business stakeholders to translate complex business problems into well-scoped data science projects.
Work hand-in-hand with engineering teams to deploy, monitor, and maintain ML models in production—including real-time serving infrastructure.
Drive best practices across the team in model development, code quality, documentation, experiment design, and reproducibility.
Contribute to the evolution of our MLOps platform and tooling, ensuring scalable and reliable model lifecycle management.
Present findings, model insights, and strategic recommendations to executive and non-technical stakeholders with clarity and business context

Requirements

What you’ll need

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.
Designing and shipping production ML models and advanced analytics in applied, production-oriented settings using Python, SQL, and ML frameworks.
Building real-time or near-real-time ML systems for recommendations, pricing, bidding, or similar use cases.
Working with data warehouse technologies (Redshift, Snowflake, BigQuery) and building/managing data pipelines (dbt, Airflow, Spark).
Strong foundation in statistics, probability, experiment design, and machine learning theory.
Experience working with ML platforms and infrastructure (SageMaker, Spark, Ray, MLflow, or equivalent).
Comfortable doing software engineering when needed—writing application code in Python/Scala/Java, contributing to APIs, containerizing services (Docker, Kubernetes), or building CI/CD for model deployments.
Excellent communication skills—effective with both technical and non-technical audiences.
Experience in fintech, financial services, or marketplace/auction environments is a strong plus.

Benefits

Comp & perks

flexible working options
time off
competitive pay
benefits
well-being programs