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Fintech Data Science Jobs (NOW HIRING)

Loomis

Data Scientist

Suwanee, GA · On-site

Experience in FinTech, banking, payments, retail cash management, or operations * Experience identifying high-value data science opportunities in operational businesses * Hands-on LLM development ...

OpenFX

Data Science & Analytics Lead

Miami, FL · On-site

Role Overview We are seeking a talented Data Science & Analytics Lead to build and lead our ... Experience in a fintech or financial services environment. * Direct experience with crypto ...

Mission Lane

Director of Data Science, Credit Risk

$185K - $219K/yr

As a Director of Data Science, you'll: Lead the design, development, and deployment of machine ... Experience solving problems in consumer lending or fintech * Interest in advancing the frontiers of ...

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How much do fintech data science jobs pay per year?

As of Jul 21, 2026, the average yearly pay for fintech data science in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 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.

How do data scientists in fintech 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 are the key skills and qualifications needed to thrive as a Fintech Data Scientist, and why are they important?

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.

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.

More about Fintech Data Science jobs
What cities are hiring for Fintech Data Science jobs? Cities with the most Fintech Data Science job openings:
What states have the most Fintech Data Science jobs? States with the most job openings for Fintech Data Science jobs include:
What job categories do people searching Fintech Data Science jobs look for? The top searched job categories for Fintech Data Science jobs are:
Fintech Data Science jobs near you
Infographic showing various Fintech Data Science job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.
Data Scientist

Data Scientist

Loomis

Suwanee, GA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 14 days ago

Job description

With a network of nearly 200 branches, Loomis armored transportation, cash management centers, and cash inventory vaults keep cash flowing throughout financial institutions and retail businesses across the US. Loomis prides itself on providing employees with opportunities for career advancement and job satisfaction. In fact, many of our company's managers, vice presidents, and corporate executives started out in the branches as driver/guards and tellers. Our work can be challenging, but the thousands who have stayed with our company for decades will tell you that if you have the desire to learn and the drive to succeed, Loomis is the place to be. Come join our team!
Summary
The position of Data Scientist is for the Logicpath division within Loomis. We are a team of tech-savvy cash inventory management experts passionate about helping financial institutions succeed.
We provide a collaborative and supportive environment that values the participation and contribution of all employees. We are looking for people who want to be challenged, solve complex problems, and feel connected to a larger purpose. Our mission-focused team, collaborative nature, and commitment lead dedication to client results.
Function
The Data Scientist will play a critical role in designing, scaling, and operationalizing advanced analytics and machine learning solutions across the company's FinTech platforms. This role will lead complex forecasting initiatives, develop AI-driven use cases (including LLM-enabled support tools), and establish strong data quality and model governance practices.
This position requires a hands-on technical leader who can translate real-world operational and financial problems into robust, production-ready data science solutions, while partnering closely with engineering, product, implementation, and client-facing teams.
The ideal candidate combines strong statistical and machine learning expertise with practical engineering ability and a track record of delivering production-grade solutions in environments where communication, business processes, data quality, and operational constraints matter as much as model performance. This very technical person is capable of thinking in terms of "problem -> solution -> product -> value", not just "models".
Key Responsibilities
Forecasting & Advanced Analytics
  • Lead the design, development, and optimization of forecasting models for:

o Cash demand (branches, ATMs, retail locations, vaults)
o Labor and operational workload forecasting
  • Apply and evaluate time-series, probabilistic, and machine-learning techniques to improve forecast accuracy and stability.

  • Own model performance monitoring, drift detection, recalibration strategies, and continuous improvement.

AI, ML, & LLM Enablement
  • Design and implement LLM-based use cases to support internal teams (e.g., support, implementation, operations).

  • Develop approaches for prompt engineering, evaluation, and governance of LLM outputs.

  • Partner with engineering to integrate AI capabilities into production SaaS workflows.

  • Define metrics to measure effectiveness, accuracy, and operational impact (ROI) of AI solutions.

Data Quality, Governance & Model Risk
  • Establish data quality frameworks to detect anomalies, gaps, and integrity issues across large transactional datasets.
  • Define validation rules, thresholds, and scoring mechanisms to support data confidence and forecast reliability.
  • Contribute to model documentation, explainability, and governance practices aligned with financial services expectations.
  • Support audit, compliance, and client due diligence inquiries related to data and models.
  • Technical Leadership & Collaboration

Required Qualifications
  • 6+ years of professional experience in data science, machine learning, or advanced analytics
  • Advanced proficiency with Python and data science libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow/Torch)
  • Strong SQL skills and experience working with messy, incomplete, high-volume operational data
  • Well-rounded background in data science methods (e.g., supervised and unsupervised learning, anomaly detection, time series forecasting, survival analysis, simulation, optimization, causal analysis)
  • Familiarity with metric design
  • Demonstrated delivery of products that influenced business decisions
  • Experience collaborating with engineering teams on model deployment and monitoring.
  • Proven ability to communicate complex concepts clearly and effectively.

Preferred Qualifications
  • Experience in FinTech, banking, payments, retail cash management, or operations
  • Experience identifying high-value data science opportunities in operational businesses
  • Hands-on LLM development experience
  • Familiarity with data quality and model governance frameworks

Ideal Candidates are:
  • Comfortable with ambiguity
  • Driven to elevate themselves by elevating others
  • Curious and life-long learners
  • Able to identify valuable problems before being asked
  • Pragmatic rather than purely academically focused
  • Capable of explaining very technical ideas to non-technical stakeholders
  • Willing to challenge their own and others' assumptions with evidence
  • Open to changing their mind when presented with new evidence

What Success Looks Like
• Forecasting models that are accurate, explainable, and trusted by clients and internal teams.
• AI and LLM use cases that measurably reduce operational effort and improve response quality.
• Strong data quality visibility that proactively identifies issues before they impact forecasts.
• Clear, well-documented models and methodologies that scale across clients and use cases.
• A collaborative, high-impact partnership with engineering, product, and client
Benefits:
Loomis offers one of the most comprehensive employee benefit packages in the industry, which includes:
  • Vacation and Sick Time (PTO) as well as Paid Holidays
  • Health & Dental Insurance
  • Vision Insurance
  • 401(k) Plan
  • Basic Life Insurance Plan
  • Voluntary Life Insurance Plan
  • Flexible Spending and Health Savings Account
  • Dependent Care Account
  • Industry-leading Training and Development

Loomis is an Equal Opportunity Employer and Drug Free Workplace. Qualified applicants will receive consideration for employment without regard to their race, color, religion, national origin, sex, sexual orientation, gender identity, protected veteran status or disability.

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