Data Scientist III

Truist

. Perform sophisticated data analytics on structured and unstructured data, including data mining, inferential statistical analysis, and predictive analytics .

Posted 11/10/2026full-timeUnited StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data analytics, including data mining, inferential statistical analysis, and predictive analytics, while effectively communicating insights and recommendations to stakeholders. Proficient in designing and implementing data science solutions that enhance business outcomes and mitigate risks in the financial services domain.

Highest-signal resume keywords

Data AnalyticsPredictive AnalyticsPython ProgrammingStatistical AnalysisFraud Detection

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data MiningInferential Statistical AnalysisMachine LearningModel DevelopmentBusiness Information VisualizationStatistical PrinciplesData GovernanceData Science Solution DesignProject ManagementRisk Management

Soft Skills

Stakeholder CommunicationAnalytical ConsultingThought LeadershipCollaborationMentoring

Tools & Technologies

HadoopPigHiveNoSQLSparkSASR

Industry Keywords

BankingFinancial ServicesFintechQuantitative Risk ManagementFinancial Regulation

Tech Stack

Tools & technologies

HadoopNoSQLPythonSpark

About the role

Key responsibilities & impact

Perform sophisticated data analytics on structured and unstructured data, including data mining, inferential statistical analysis, and predictive analytics
Identify actionable insights that improve business outcomes or reduce business risk
Own end-to-end data science solution design, technical delivery, and measurable business outcomes for new, ongoing, and strategic projects
Extract and evaluate information from multiple sources, resolve data conflicts, communicate findings, and provide recommendations
Provide analytical consulting and thought leadership to business areas
Co-design scalable and reusable data science solutions for complex business problems
Implement coding standards, best practices, clear stakeholder communication, and rigorous documentation
Support data usage rules, data governance, analytics capabilities, and overall team risk management
Research and advocate adoption of emerging data science methods and technologies
Identify emerging fraud patterns and risk signals, develop and optimize fraud decisioning strategies, and balance fraud-loss mitigation with client experience
Partner with Product, Operations, Technology, and Fraud Strategy teams on platform and strategic initiatives
Communicate fraud trends and strategy performance to senior stakeholders
Provide analytical and strategic guidance to less experienced teammates

Requirements

What you’ll need

Bachelor’s degree in a quantitative field such as Finance, Mathematics, Analytics, Data Science, Computer Science, or Engineering, or equivalent education and related training
Minimum of six years of related experience
Domain expertise in banking, financial services, fintech, quantitative risk management, and/or financial regulation
In-depth knowledge of statistics, programming such as Python, R, and SAS, machine learning, model development, and business information visualization
High competency in statistical and analytical principles, tools, and techniques
Working knowledge of Hadoop, Pig, Hive, NoSQL, and/or Spark
Experience managing multiple projects with tight deadlines in a collaborative environment
English language fluency required

Benefits

Comp & perks

Medical insurance
Dental insurance
Vision insurance
Life insurance
Disability insurance
Accidental death and dismemberment coverage
Tax-preferred savings accounts
401(k) plan
No less than 10 days of vacation during the first year of employment, prorated as applicable
10 sick days, prorated as applicable
Paid holidays
Defined benefit pension plan, depending on position and division
Restricted stock units, depending on position and division
Deferred compensation plan, depending on position and division