Data Science Manager

Austin, Texas, United States

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Not SpecifiedCompensation

Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Financial Services, Identity Verification, Risk SolutionsIndustries

Requirements

Candidates should possess 10+ years of relevant work experience with a relevant Master's degree, or 7+ years with a relevant PhD. A minimum of 2-5 years of direct experience managing a team of data scientists, preferably in a startup, and at least 3 years of startup experience are required. Excellent communication and teamwork skills are essential, along with a proven ability to solve complex business problems using data science and machine learning solutions. Experience presenting outcomes and progress to senior management and stakeholders is also necessary, as is strong proficiency in end-to-end data science development, including planning, defining success criteria and metrics, gaining buy-in, and developing solutions.

Responsibilities

The Data Science Manager will directly manage a team of 2-3 data scientists, growing to 5-6, and serve as a technical mentor providing detailed direction. Responsibilities include leading planning, resourcing, and communications with senior leadership, product, and engineering teams. The manager will guide the team to deliver performant data science solutions on aggressive timelines and develop SentiLink’s fraud detection models throughout their entire lifecycle, from data acquisition to monitoring. This includes researching new fraud types, developing new identity verification products, writing production-ready code for real-time decision-making, and designing/presenting analyses to inform business priorities.

Skills

Data Science

Model Development

Analysis

Production Code

Technical Leadership

Mentorship

Financial Risk

Fraud Detection

SentiLink

Machine learning solutions for identity fraud detection

About SentiLink

SentiLink provides solutions to help financial institutions prevent identity fraud. Their main product uses machine learning models to detect fraudulent activities during the application process. By analyzing data and reviewing cases with a team of risk analysts, SentiLink offers insights that help clients make informed decisions about approving customers. What sets SentiLink apart from competitors is their ability to adapt their products to various types of fraud and customize them to meet the specific needs of each client. The goal of SentiLink is to enable financial institutions to minimize fraud losses while maintaining a positive customer experience.

San Francisco, CaliforniaHeadquarters

2017Year Founded

$81.7MTotal Funding

SERIES_BCompany Stage

Fintech, AI & Machine Learning, Financial ServicesIndustries

51-200Employees

Benefits

Health Insurance

401(k) Retirement Plan

401(k) Company Match

Unlimited Paid Time Off

Home Office Stipend

Risks

Sophisticated fraud techniques may outpace SentiLink's current detection capabilities.

Dependence on high-quality data could impact fraud detection effectiveness if data quality declines.

Intense competition in the fintech sector may threaten SentiLink's market position.

Differentiation

SentiLink specializes in detecting synthetic identities using proprietary machine learning models.

The company offers customizable solutions that integrate seamlessly with clients' existing systems.

SentiLink's Facets tool provides feature-specific intelligence to enhance fraud detection accuracy.

Upsides

Growing demand for synthetic identity fraud detection boosts SentiLink's market potential.

Partnerships with Scienaptic AI and Persona enhance SentiLink's fraud detection capabilities.

API-based solutions allow SentiLink to offer seamless integration and customization.

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