Staff Data Scientist

Glocomms; New York (NY); In loco; USD 150.000 - 230.000; Tempo pieno

Vantaggi offerti da questo lavoro

Comprehensive benefits package

Equity opportunity

Descrizione del lavoro

Glocomms is seeking a Staff Data Scientist in New York to advance a document verification and fraud prevention platform used across highly scaled digital environments. You will blend applied machine learning, experimentation, analytics, and product-focused problem solving, partnering with engineering and product teams to build impactful models.

You will explore data signals, design rigorous model assessments, and create tools to streamline evaluation and investigations, contributing to product

Competenze

• Advanced degree or equivalent in Data Science, CS, Statistics, Mathematics, or related field.
• 5+ years in ML, analytics, fraud, risk, or data science.
• Proven success building, validating, and improving ML models in production.
• Strong Python and SQL proficiency.
• Experience with modern data/ML platforms such as Databricks, Spark, SageMaker.

Mansioni

• Develop and enhance ML solutions for identity verification, document analysis, fraud detection, and related use cases.
• Explore new signals, modeling techniques, and detection strategies to improve accuracy and risk outcomes.
• Collaborate with engineering to launch, monitor, and refine models in live environments.
• Analyze fraud patterns and customer behavior using large-scale datasets.
• Design model assessments, performance studies, and monitoring to track impact.
• Create internal tools to streamline evaluation workflows and investigations.
• Partner with stakeholders to translate business challenges into data-driven solutions.
• Present findings to guide product strategy and ML investments.

Conoscenze

Python

SQL

Experimentation

Statistics

Formazione

Advanced degree or equivalent experience

Strumenti

Databricks

Spark

SageMaker

Descrizione del lavoro

We're seeking a Staff Data Scientist to support the continued evolution of a document verification and fraud prevention platform used across highly scaled digital environments. This role blends applied machine learning, experimentation, analytics, and product-focused problem solving. You'll work closely with engineering, product, and operations teams to develop models, uncover insights, and build frameworks that improve both performance and decision-making.

Key Responsibilities

• Develop and enhance machine learning solutions focused on identity verification, document analysis, fraud detection, image assessment, and biometric-related use cases.


• Explore new data signals, modeling techniques, and detection strategies to improve prediction accuracy and risk outcomes.


• Collaborate with engineering teams to launch, monitor, and refine models operating in live environments.


• Investigate fraud patterns, customer behaviors, and emerging threats using large-scale datasets.


• Design rigorous model assessments and performance studies using offline and production data.


• Establish metrics, reporting frameworks, and monitoring capabilities to track model effectiveness and business impact.


• Create internal tools that streamline evaluation workflows, investigations, experimentation, and operational processes.


• Partner with stakeholders across product, operations, and technology teams to translate business challenges into data-driven solutions.


• Present findings and recommendations that help guide product strategy and machine learning investments.

Requirements

• Advanced degree or equivalent industry experience in Data Science, Computer Science, Statistics, Mathematics, or a related technical discipline.


• 5+ years of experience working in machine learning, advanced analytics, fraud, risk, or data science environments.


• Demonstrated success building, validating, and improving machine learning models used in production.


• Strong proficiency with Python and SQL.


• Experience leveraging modern data and ML platforms such as Databricks, Spark, SageMaker, or similar ecosystems.


• Solid foundation in experimentation, statistical methods, model measurement, and performance analysis.


• Ability to communicate complex analytical findings to both technical and non-technical audiences.

Compensation: Competitive base salary, equity opportunity, and comprehensive benefits package.