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Trainee Fintech Data Scientist Jobs (NOW HIRING)

CWCapital

Fintech Data Integration Analyst

Irving, TX · On-site

$75K - $85K/yr

We're looking for a Fintech Data Integration Analyst to join our team. This role is ideal for ... Bachelor's degree in information systems, Finance, Computer Science, or a related field, or ...

BillGO, Inc.

Data Scientist AI/ML

Fort Collins, CO · On-site

$102K - $146K/yr

We're looking for a Data Scientist with deep AI and machine learning expertise to help shape the ... In a fintech environment where trust, speed, and compliance are critical, this role ensures that ...

Kikoff

Data Scientist Operations

San Francisco, CA · On-site

$160K - $260K/yr

Kikoff: The Fintech Powering Financial Security at Scale Kikoff is a profitable, pre-IPO fintech ... About the Role As a Operations Data Scientist at Kikoff, you work closely with cross functional ...

BillGO, Inc.

Data Scientist AI/ML

Fort Collins, CO

$102K - $146K/yr

We're looking for a Data Scientist with deep AI and machine learning expertise to help shape the ... In a fintech environment where trust, speed, and compliance are critical, this role ensures that ...

Kikoff

Senior Data Scientist

San Francisco, CA · On-site

$226K - $254K/yr

Kikoff: The Fintech Powering Financial Security at Scale Kikoff is a profitable, pre-IPO fintech ... About the Role As a Senior Data Scientist at Kikoff, you work closely with cross functional teams ...

Highbeam, Inc

Data Scientist

New York, NY · On-site

$200K - $225K/yr

This role blends data science, customer analytics, financial analysis, and market insights to ... Experience with fintech, e-commerce, lending, or D2C businesses * Background in FP&A or consumer ...

Russell Tobin

Data Scientist

Russell Tobin is seeking a Data Scientist for a client in the Travel industry. The role involves ... Preferred : • Experience supporting Treasury, Finance, FP&A, Payments, FinTech, Marketplace, or ...

Kikoff

Marketing Data Scientist

San Francisco, CA · On-site

$160K - $200K/yr

Kikoff: The Fintech Powering Financial Security at Scale Kikoff is a profitable, pre-IPO fintech ... and our Head of Marketing Data Science. Responsibilities * Own marketing measurement and ...

Quanta Search

Data Scientist

Chicago, IL · Remote

The data science team consists of highly motivated and experienced data scientists and they are a ... FinTech professionals A fun, highly collaborative environment.

Quanta Search

Data Scientist

Chicago, IL · On-site

The data science team consists of highly motivated and experienced data scientists and they are a ... FinTech professionals • A fun, highly collaborative environment. Thank you for illuminating ...

Current

Senior Data Scientist

New York, NY · On-site

$170K - $220K/yr

Senior Data Scientist About Current Current is a leading U ... S. fintech serving people who have been overlooked by traditional banks. We are one of the fastest ...

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Trainee Fintech Data Scientist information

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$37.5K

$122.7K

$196.5K

How much do trainee fintech data scientist jobs pay per year?

As of Jun 14, 2026, the average yearly pay for trainee fintech data scientist 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 kinds of projects and collaborations can a Trainee Fintech Data Scientist expect to be involved in during their first year?

As a Trainee Fintech Data Scientist, you'll typically work on real-world data projects such as analyzing transaction patterns, detecting fraud, or building predictive models for credit scoring. You'll collaborate closely with experienced data scientists, software engineers, and business analysts, often participating in agile teams and cross-functional meetings. It's common to have mentorship opportunities and structured training sessions to help you ramp up on both technical skills and domain knowledge. Over time, you'll gain experience presenting your findings to both technical and non-technical stakeholders, which is key for career growth in fintech.

What are the key skills and qualifications needed to thrive as a Trainee Fintech Data Scientist, and why are they important?

To thrive as a Trainee Fintech Data Scientist, you need a solid grounding in statistics, data analysis, and programming (often with Python or R), typically supported by an academic background in a quantitative field. Familiarity with data visualization tools, SQL databases, and introductory machine learning libraries is highly beneficial, as is exposure to fintech platforms or certifications like Data Science or Fintech Foundations. Strong analytical thinking, attention to detail, and effective communication skills help you interpret data insights and collaborate with cross-functional teams. These competencies are crucial for extracting meaningful value from complex financial data and driving innovation in a rapidly evolving fintech landscape.

What is a Trainee Fintech Data Scientist?

A Trainee Fintech Data Scientist is an entry-level professional who works with financial technology (fintech) companies to analyze and interpret complex financial data. This role involves learning to use data science tools and techniques such as machine learning, programming, and statistical analysis to solve business problems in areas like risk assessment, fraud detection, and customer insights. Trainees typically work under the supervision of experienced data scientists to gain hands-on experience and develop their technical and domain expertise in the fintech sector.

What is the difference between Trainee Fintech Data Scientist vs Junior Data Analyst?

AspectTrainee Fintech Data ScientistJunior Data Analyst
Required CredentialsTypically a degree in data science, computer science, or related field; some certifications beneficialOften a degree in statistics, mathematics, or related field; certifications are optional
Work EnvironmentFintech companies, startups, or financial institutions; focus on developing models and algorithmsVarious industries including finance, retail, or healthcare; focus on data reporting and analysis
Employer & Industry UsageCommonly used in fintech for developing predictive models and algorithmsUsed across industries for data reporting, visualization, and basic analysis

The Trainee Fintech Data Scientist focuses on developing advanced models and algorithms within the fintech industry, often requiring programming skills and statistical knowledge. In contrast, a Junior Data Analyst primarily handles data reporting and visualization tasks. While both roles require a background in data-related fields, the trainee data scientist role emphasizes machine learning and model development, making it more technical and specialized.

More about Trainee Fintech Data Scientist jobs
What cities are hiring for Trainee Fintech Data Scientist jobs? Cities with the most Trainee Fintech Data Scientist job openings:
What are the most commonly searched types of Fintech Data Scientist jobs? The most popular types of Fintech Data Scientist jobs are:
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Trainee Fintech Data Scientist jobs near you
Infographic showing various Trainee Fintech Data Scientist job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, and 99% Full Time. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.
Fraud & Risk Data Analyst Fintech & Data Insights *** Direct End Client ***

Fraud & Risk Data Analyst Fintech & Data Insights *** Direct End Client ***

Projas Technologies, LLC

Mountain View, CA

Other

Posted 16 days ago

Job description

Staff Fraud & Risk Analyst Fintech & Data Insights

As a Fraud & Risk Data Analyst, you ll work at the intersection of analytics, compliance, and product innovation. This role is part of a dynamic Risk Insights and Experimentation team that influences billions of dollars in transactions annually. You ll partner with cross-functional teams to uncover insights, design experiments, and shape strategies that drive secure, scalable growth.

Domain expertise in Security, Risk & Fraud in Fintech space (consumer Lending, Fraud & Risk Policy, Compliance, Marketing, Product, Finance is required)

Key Responsibilities:
  • Deliver clear, persuasive insights to senior leadership through data storytelling and executive-ready reports.
  • Build intuitive dashboards and visualizations that guide strategic and operational decisions.
  • Design and interpret A/B tests to optimize product performance and risk strategies.
  • Collaborate with product, compliance, finance, and data science teams to define scalable analytics frameworks.
  • Analyze large-scale transactional and behavioral data to detect trends, anomalies, and fraud patterns.
  • Evaluate compliance effectiveness and recommend improvements to onboarding and remediation flows.
  • Translate ambiguous business questions into structured analytical approaches using SQL, Python, and visualization tools.
  • Investigate root causes of performance gaps and propose timely corrective actions.
  • Maintain clean, reliable data pipelines and address data quality issues proactively.
Qualifications:
  • 6+ years in analytics, data science, or decision strategy roles, ideally in Financial Services or Fintech.
  • Advanced SQL skills and working knowledge of Python for data manipulation and automation.
  • Experience with data visualization tools (Tableau, Quicksight, Qlik) and dashboard development.
  • Strong understanding of A/B testing principles and experimental design.
  • Proven ability to influence decisions through data-driven recommendations.
  • Excellent communication skills for translating complex data into actionable insights.
  • Bachelor s degree in a quantitative field (Economics, Math, Science) or equivalent experience.

fraud analyst, risk analyst, fintech analytics, SQL, Python, A/B testing, data visualization, Tableau, Quicksight, Qlik, compliance analytics, risk management, dashboard development, data science, financial services, experimentation, policy analytics, fraud detection, risk strategy, KPIs, OKRs, data storytelling

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