Top Entry Level Fintech Data Science Jobs

Graduate data scientist supporting customer-impacting projects, prioritizing product improvements, and developing machine learning solutions. The role includes applying statistical and machine learning fundamentals, experimenting with advanced algorithms, understanding Wise products and technology stacks, and proposing product ideas. Candidates must have a STEM degree, Python and SQL knowledge, statistics foundations, a personal project, and authorization to work in Austin without sponsorship.

Research and develop hypotheses using diverse real-world datasets, apply advanced methodologies to extract meaningful signals, and rigorously test theories with engineers and business stakeholders. The role involves literature reviews, quantitative data analysis, clear communication of complex ideas, and ongoing engagement with the academic research community.

Develops and owns end-to-end decision science initiatives, including predictive modeling, recommendation, optimization, experimentation, and GenAI solutions. The role applies agentic AI, transformer-based recommenders, conversational AI, and LLMs to servicing use cases. Responsibilities include building production-ready data science products, partnering with technology and business teams, measuring impact, optimizing models, leading workstreams, and communicating recommendations to stakeholders.

Develop and evaluate predictive models for personal auto pricing and demand. Responsibilities span data exploration, feature engineering, model development, validation, implementation, monitoring, regulatory support, and MLOps modernization. The role also involves maintaining analytic packages and repositories, improving workflows, ensuring reproducibility, conducting code reviews, collaborating in Agile projects, and communicating recommendations to business partners.

Develop and evaluate foundational deep learning methods, including transformers, embeddings, and neural networks, for credit modeling and loss prediction. Integrate models into ML pipelines, apply representation learning to financial data, and build specialized AI agents for data science and business workflows. Collaborate with technical and product teams, communicate findings, mentor colleagues, and measure model impact on customer and business outcomes.

Develop partner-specific pricing strategies for products and bundles, analyze volume, conversion, cost, and competitive data, and identify revenue opportunities. Build AI-powered pricing calculators, deal-sizing models, dashboards, and experimentation frameworks. Collaborate with Finance, Partnerships, Product, Compliance, and Legal to ensure commercially sound pricing. Establish scalable pricing frameworks, reduce escalations, document decisions, and communicate complex pricing recommendations to senior stakeholders and non-technical partners.

Develops statistical and machine learning solutions for consumer finance use cases. Responsibilities include analyzing complex datasets, evaluating alternative data for underwriting, assessing marketing data, generating operational insights, and communicating findings to nontechnical stakeholders. The role is designed for a current quantitative graduate student seeking hands-on experience with real-world data science and modern cloud-based infrastructure.

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 predictive models and data science solutions for automated credit-risk and lending decisions. Responsibilities include collecting, cleaning, visualizing, and analyzing large datasets; identifying patterns; presenting insights to stakeholders; translating models into business strategies; partnering with IT on implementation; and monitoring model performance.

Design, build, optimize, and deploy systematic trading models and algorithms. Analyze large datasets to identify market patterns and trading signals, collaborate with traders and technologists to productionize research, and monitor and refine models based on live performance. The role requires advanced quantitative expertise, strong programming skills in Python or C++, and the ability to solve open-ended problems independently.