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Fintech Data Science Jobs in Seattle, WA (NOW HIRING)

Experience in data science, data engineering, or analytics roles in large-scale environments * Experience in payments, fraud, risk, disputes, fintech, or transaction-heavy domains * Experience ...

Experience in data science, data engineering, or analytics roles in large-scale environments * Experience in payments, fraud, risk, disputes, fintech, or transaction-heavy domains * Experience ...

Applied Scientist

Seattle, WA · On-site

$120 - $140/hr

... science. What you'll do * Research & Analysis: Perform data research and analysis using Grid ... FinTech products. 120000 - 140000 USD a year Benefits * Medical * Dental * Vision * 401K * Life ...

Applied Scientist

Seattle, WA · On-site

$120 - $140/hr

... science. What you'll do * Research & Analysis: Perform data research and analysis using Grid ... FinTech products. $120,000 - $140,000 a year Benefits * Medical * Dental * Vision * 401K * Life ...

... science. What you'll do * Research & Analysis: Perform data research and analysis using Grid ... FinTech products. Benefits Medical Dental Vision 401K Life Insurance Salary Range $120,000 - $140 ...

Applied Scientist

Seattle, WA · On-site

$120K - $140K/yr

... science. What you'll do * Research & Analysis: Perform data research and analysis using Grid ... FinTech products. $120,000 - $140,000 a year Benefits Medical Dental Vision 401K Life Insurance ...

... fintech organization. You will be responsible for designing and evaluating experiments ... You Have: * 7+ years of experience in Product Analytics, Data Science, Decision Science, or a ...

... science. What you'll do * Research & Analysis: Perform data research and analysis using Grid ... FinTech products. Benefits Medical Dental Vision 401K Life Insurance Salary Range $150,000 - $220 ...

... science. What you'll do * Research & Analysis: Perform data research and analysis using Grid ... FinTech products. $150,000 - $220,000 a year Benefits Medical Dental Vision 401K Life Insurance ...

We partner deeply with growth, data science, analytics, data engineering, creative, finance and ... Experience in B2B SaaS, developer tools, fintech, or high-growth technology companies * Track ...

Head of Credit Products

Seattle, WA · On-site

$285K - $310K/yr

Partner with Data Science, Engineering, and Data teams to design scalable product capabilities ... fintech, or financial services products. * 5+ years of experience leading product managers or ...

Sr. Machine Learning Engineer

Seattle, WA · On-site

$118K - $163K/yr

Required : • Bachelor's or advanced degree in Computer Science, Mathematics, Data Science, or a ... Prior exposure to fintech or financial data platforms is a strong advantage • Experience ...

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Fintech Data Science information

See Seattle, WA salary details

$42.7K

$139.7K

$223.6K

How much do fintech data science jobs pay per year?

As of Aug 22, 2026, the average yearly pay for fintech data science in Seattle, WA is $139,680.00, according to ZipRecruiter salary data. Most workers in this role earn between $112,100.00 and $154,800.00 per year, depending on experience, location, and employer.

What is a fintech data scientist?

A Fintech Data Scientist is a professional who uses data analysis, machine learning, and statistical techniques to solve problems and create value within the financial technology (fintech) industry. They work with large amounts of financial data to develop predictive models, detect fraud, assess risk, and optimize financial products or services. Their expertise combines knowledge of finance, programming, and advanced analytics to help fintech companies make data-driven decisions and innovate in areas such as payments, lending, and investment. Fintech Data Scientists often collaborate with engineers, product managers, and business stakeholders to deliver actionable insights that drive business growth.

What are the key skills and qualifications needed to thrive as a fintech data scientist?

To thrive as a Fintech Data Scientist, you need a strong background in statistics, machine learning, and programming (often with a degree in computer science, mathematics, or a related field). Familiarity with tools such as Python, R, SQL, cloud computing platforms, and experience with financial data modeling or relevant certifications are typically required. Strong problem-solving skills, attention to detail, and effective communication are important soft skills that set top professionals apart. These skills and qualities are vital for extracting actionable insights from complex financial data, driving innovation, and ensuring regulatory compliance in the fast-evolving fintech industry.

How do fintech data scientists typically collaborate with product and engineering teams to develop new financial products?

In fintech, data scientists often work closely with product managers and engineering teams throughout the lifecycle of a financial product. They analyze user data and market trends to provide actionable insights during the product design phase, and collaborate with engineers to implement machine learning models into the product infrastructure. Regular cross-functional meetings and agile workflows are common, allowing data scientists to iterate on models based on feedback and evolving requirements. This collaborative environment ensures that data-driven solutions are robust, scalable, and aligned with business goals.

What is the difference between Fintech Data Science vs Fintech Data Analyst?

AspectFintech Data ScienceFintech Data Analyst
Required SkillsAdvanced statistical, programming, and machine learning skillsData interpretation, reporting, and basic analytics
CertificationsData Science certifications, programming coursesData analysis or business intelligence certifications
Work EnvironmentDeveloping models, algorithms, and predictive analyticsData reporting, dashboards, and data cleaning
Industry UsageCreating predictive models for risk, fraud detection, and customer insightsGenerating reports, supporting decision-making with data

Fintech Data Science involves building complex models and applying machine learning techniques, requiring advanced skills and certifications. Fintech Data Analysts focus on interpreting data, creating reports, and supporting business decisions with less technical complexity. Both roles are essential in the fintech industry but differ in technical depth and responsibilities.

Is data science good for fintech?

Data science is highly valuable in fintech, as it enables the development of algorithms for risk assessment, fraud detection, and personalized financial services. Fintech companies often rely on data analysis, machine learning, and statistical modeling to improve decision-making and customer experience. Skills in programming, data manipulation, and financial knowledge are essential for data scientists in this field.

Is fintech data science a high paying career?

Fintech data science is generally a high-paying career due to the demand for advanced analytics and machine learning skills in financial technology companies. Salaries often depend on experience, education, and technical expertise in tools like Python, R, and SQL, with senior roles earning significantly more. The field offers competitive compensation compared to many other data science roles across industries.
Infographic showing various Fintech Data Science job openings in Seattle, WA as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $139,680 per year, or $67.2 per hour.

Head of Data Science - Identity & Compliance

Socure

Seattle, WA • On-site

Full-time

Re-posted 14 days ago


Job description

Job Summary:
Socure is building the identity trust infrastructure for the digital economy, and they are seeking a Head of Data Science to lead the development of AI-powered identity and compliance solutions. This role involves owning the data science vision, leading a high-performing team, and driving innovation in machine learning and compliance strategies.
Responsibilities:
• Own and drive the data science vision for Identity & Compliance, delivering measurable improvements in accuracy, coverage, latency, and customer impact across all products in scope.
• Lead, build, and develop a high-performing team of data scientists and applied researchers, fostering a culture of technical excellence, speed, and accountability.
• Architect and deploy advanced machine learning systems across identity verification, entity resolution, sanctions screening, and compliance risk modeling.
• Drive the development of graph-based intelligence, including large-scale graph neural networks (GNNs) and link analysis models to power Identity Graph, fraud detection, and watchlist matching.
• Lead the design and implementation of agent-based AI systems, enabling automated decisioning, case triage, investigation workflows, and adaptive compliance strategies.
• Advance state-of-the-art modeling approaches, including deep learning, representation learning, graph learning, and multimodal fusion across structured and unstructured data.
• Drive innovation in Identity Graph and Prefill systems, improving identity resolution, linking, and enrichment capabilities across global datasets.
• Partner closely with Product, Engineering, Risk, and Go-to-Market teams to translate business needs into scalable AI solutions that deliver customer value.
• Own the end-to-end model lifecycle, including data strategy, feature engineering, model development, evaluation, deployment, and monitoring.
• Ensure alignment with regulatory and compliance requirements, balancing model performance with explainability, auditability, and governance.
• Continuously raise the bar on experimentation and execution velocity, enabling rapid iteration while maintaining high standards for reliability and impact.
• Own customer communication and stakeholder management, serving as a trusted technical leader in engagements with customers, partners, and internal stakeholders; clearly articulate model behavior, agentic systems, performance trade-offs, and roadmap decisions while building strong, long-term relationships.
• Represent Socure externally as a thought leader in identity, compliance, and applied AI.
Qualifications:
Required:
• Advanced degree (MS/PhD preferred) in Computer Science, Statistics, Mathematics, Engineering, or a related field.
• 10+ years of experience in data science and machine learning, with a strong track record of delivering production-grade AI systems at scale.
• Significant experience in identity verification, KYC/AML, fraud detection, or risk modeling in fintech or adjacent domains.
• Proven leadership experience managing and scaling high-performing data science teams.
• Deep expertise in modern machine learning techniques, including deep learning, graph neural networks, entity resolution, and large-scale data systems.
• Strong understanding of agentic system design, including agent skills, orchestration/harness frameworks, and real-world deployment of autonomous or human-in-the-loop agents.
• Strong experience working with heterogeneous data sources, including structured data, text, network/graph data, and third-party identity signals.
• Demonstrated ability to drive ambiguous, high-impact problems to production, balancing speed, rigor, and business outcomes.
• Hands-on experience with Apache Spark and large-scale distributed data systems.
• Proficiency in Python and modern ML frameworks (e.g., PyTorch), and familiarity with graph ML frameworks is a plus.
• Proven ability to engage with customers and external stakeholders, clearly explaining complex AI systems, trade-offs, and outcomes to both technical and non-technical audiences.
Preferred:
• Experience representing organizations in customer-facing discussions, executive briefings, or public speaking engagements is strongly preferred.
Company:
Socure is a predictive analytics platform for digital identity verification of consumers. Founded in 2012, the company is headquartered in Incline Village, USA, with a team of 501-1000 employees. The company is currently Late Stage.