Top Tech Jobs & Startup Jobs

Leads the company’s global risk and compliance function, establishing enterprise risk frameworks, AI governance, data privacy policies, vendor risk standards, insurance programs, SOC 2 compliance, ESG reporting, and cross-regional compliance operations. Engages enterprise clients on risk matters, coordinates with legal, IT, and security teams, oversees regulatory requirements, manages risk registers and incident reviews, and reports risk posture and remediation progress to executives.

Supports email and direct mail marketing campaigns from creative routing and approvals through QA, testing, trafficking, and launch. Manages campaign workflows, timelines, file transfers, vendor coordination, stakeholder communications, and quality-control audits. Assists with dynamic email automations, beta testing, content accuracy, and adherence to email best practices while supporting project management teams across multiple concurrent campaigns.

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.

Leads the technical direction, architecture, quality, and growth of an AI Engineering practice. Designs production AI systems, guides major client engagements, conducts design reviews, resolves complex engineering challenges, shapes proposals and delivery plans, and develops senior technical talent. The role remains hands-on with Python, AI architectures, retrieval, agentic pipelines, evaluation, scalability, reliability, and observability. It also requires client-facing leadership, commercial awareness, capability planning, and experience with Databricks and Azure OpenAI.

Lead the design and development of scalable Java backend systems, distributed architectures, concurrent applications, and large-scale data pipelines. Manage Kubernetes workloads, define APIs and service contracts, implement observability, optimize performance, and troubleshoot production issues. The role requires strong expertise in Java, concurrency, distributed systems, Kubernetes, SQL, profiling, and monitoring, along with collaboration and mentoring skills. Experience with Spring, Kafka, reactive systems, advertising technology, or real-time data processing is preferred.

Design and build scalable Java backend systems, distributed architectures, concurrent applications, and big data pipelines. Manage Kubernetes workloads, define APIs and service contracts, implement observability, optimize performance, and support production deployments. The role requires troubleshooting complex distributed systems, collaborating cross-functionally, mentoring teammates, and driving continuous improvement. Experience with Spring, Kafka, reactive systems, and real-time data processing is beneficial.

Design and build scalable Java backend systems and distributed architectures for high-volume data processing. Develop concurrent and multithreaded applications, build big data pipelines, manage Kubernetes workloads, define APIs and service contracts, implement observability, and support production deployments. The role also involves troubleshooting complex distributed-system issues, optimizing performance, mentoring teammates, and collaborating cross-functionally.

Develop machine learning and statistical models for customer value, segmentation, behavior, transitions, causal analysis, and next-best-action recommendations. Analyze transactional and behavioral data, perform feature engineering and model validation, and translate findings into business recommendations. Collaborate with data scientists, engineers, and client stakeholders while working through ambiguous requirements and evolving priorities.

Develop machine learning and statistical models for customer value, segmentation, lifetime value, behavior, causal analysis, and next-best-action recommendations. Analyze transactional and behavioral data, engineer features, validate models, and communicate findings to technical and business stakeholders. Translate ambiguous customer and marketing problems into practical data science solutions while collaborating with data scientists, engineers, and client teams to drive measurable business outcomes.