Top Remote Fintech Data Science Jobs in New York City, NY

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Develop and lead data science and machine learning initiatives, including exploratory analysis, predictive modeling, experimentation, recommendation systems, user classification, model monitoring, and production deployment. Partner with product, marketing, analytics, and engineering teams to convert user insights into automated services. Manage projects throughout their lifecycle, support strategic ML planning, and deliver solutions for personalization, cross-selling, and retargeting.

Lead and build a data science and analytics organization focused on operations and finance analytics. Define and execute an applied AI/ML roadmap (including LLMs and agentic systems), hire and mentor teams, partner with stakeholders to translate problems into production AI capabilities, and collaborate with data engineering and platform teams to ensure data quality, orchestration, and evaluation.

Lead a data science team focused on credit risk modeling: design, develop, deploy, and manage production machine learning models. Partner cross-functionally to improve data sources, modeling methodology, risk management, and team capabilities.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

As a Data Scientist at Sardine, you'll design and deploy data-driven solutions to prevent fraud, collaborating with clients and engineering teams to build and optimize machine learning models.