Top Tech Jobs & Startup Jobs

Leads fraud analytics for financial services clients by analyzing large payment and card datasets, identifying fraud trends and emerging risks, developing fraud strategies, and optimizing decision systems. Partners with fraud, risk, operations, and technology teams to monitor models, evaluate performance, create dashboards, and deliver actionable recommendations that reduce losses and improve customer experience.

Design, develop, and deploy scalable AI-powered applications, backend systems, APIs, integrations, and AI workflows. Translate ambiguous business needs into technical solutions, prototype AI products, contribute to architecture and engineering standards, improve scalability and reliability, collaborate cross-functionally, evaluate emerging AI tools, and mentor junior engineers.

Develop and optimize recommendation systems and machine learning models for business applications. Work with large datasets using Python and SQL, integrate LLM capabilities, and apply MLOps practices to move solutions from experimentation into production. Collaborate with data and software engineers, use cloud technologies including Docker and Kubernetes, monitor model performance, and communicate technical decisions and trade-offs.

Evaluates analytical and fraud models, compares performance, prioritizes models for implementation, and coordinates deployment into production. Manages timelines, requirements, dependencies, deliverables, and issue resolution across data science, technology, modeling, and business teams. Tracks model performance before and after implementation, analyzes results, prepares reports, and communicates recommendations to stakeholders.

Analyze campaign, customer, prospect, and audience data using Python and SQL; build and validate marketing audiences; manage campaign operations, data quality, reporting, dashboards, and automation. Partner with marketing stakeholders to optimize campaigns, investigate data issues, communicate insights, and establish scalable execution and quality-control frameworks.

Analyze campaign, customer, prospect, and audience data using Python and SQL; build and validate marketing audiences; manage campaign operations, quality control, reporting, dashboards, and automation. Develop insights for campaign optimization, maintain data mappings and metadata, troubleshoot data issues, and collaborate with marketing stakeholders to support strategy and execution.

Analyze large customer, prospect, audience, and campaign datasets using Python and SQL. Build and validate marketing audiences, manage targeting and suppression logic, ensure data quality, and support end-to-end campaign execution. Develop automated reports, dashboards, queries, and scripts to monitor campaign performance and KPIs. Collaborate with marketing and cross-functional stakeholders to resolve data issues, translate campaign objectives into analytical solutions, and present actionable insights.

Owns paid search tracking, tagging, conversion measurement, pixel governance, and QA across SA360, Campaign Manager 360, Google Ads, and Microsoft Advertising. Manages Floodlight, UET, conversion, and campaign tags through enterprise tag management systems; establishes measurement standards and documentation; troubleshoots attribution and reporting discrepancies; ensures privacy compliance; and partners with marketing, analytics, technology, governance, and compliance teams.

Develop and deploy machine learning and statistical models for customer, marketing, campaign, and business analytics. Build predictive models for targeting, propensity, response, churn, and customer behavior using Python, SQL, Databricks, and PySpark. Perform feature engineering, validation, tuning, and evaluation on large-scale datasets; translate business questions into analytical solutions; communicate insights to stakeholders; productionize solutions; and mentor junior data scientists.

Design and develop scalable backend services using C# and SQL, build cloud-native applications on AWS, and support data migrations, system integrations, reporting, and analytics. The role requires strong software architecture, database design, technical decision-making, and collaboration with cross-functional teams. Engineers will work with Snowflake, SQL Server, Terraform, and occasionally React, TypeScript, and Python while independently solving complex technical challenges.