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

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.

Leads a 25+ person organization of data scientists, machine learning engineers, and analytics engineers across Identity and Financial Crime. Owns data quality, governance, lineage, domain data products, fraud and financial crime models, production performance, monitoring, and retraining. Partners with engineering, central data, risk, compliance, and legal teams, influences upstream data producers, manages incidents, and builds high-performing teams while driving strategy reported to executives and the board.

Build Boulevard’s Product Intelligence function by developing product data foundations, pipelines, dashboards, experimentation practices, and customer behavior models. Partner with Product, Engineering, and leadership to deliver actionable insights, self-serve analytics, and productionized models. Establish scalable processes, instrumentation standards, statistical workflows, and data-driven decision-making across the organization.

Build and own simulation-based sports prediction and pricing models from research through production monitoring. Apply Bayesian inference, statistical modeling, market data, and trader feedback to estimate fair value across sports and market types. Collaborate with Trading, Engineering, and Product to deploy reliable models, improve calibration and performance, and support new launches. Provide technical leadership through code reviews, modeling standards, documentation, and mentorship while using AI-assisted workflows to accelerate development.

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.

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

Build and deploy machine learning and experimentation systems for personalization, ranking, targeting, attribution, and causal measurement. Develop features and pipelines from transaction and behavioral data, design A/B tests, productionize models with engineering teams, and improve recommendation systems. Translate analytical results into business insights tied to conversion, retention, lift, and ROI while owning projects from problem framing through post-launch evaluation.

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.

Leads data science strategy across Mexico and Brazil, manages and mentors a distributed team, and remains hands-on building and deploying machine learning models. Establishes modeling standards, infrastructure, delivery practices, and technical roadmaps while partnering with credit leadership on underwriting, valuation, impact measurement, and P&L analysis. Requires strong applied machine learning expertise, prior data science leadership, business communication skills, and understanding of credit or underwriting.