Top Tech Jobs & Startup Jobs in NYC, NY

Own and QA high-impact retail data products: design and run QA, investigate and resolve complex data quality issues, collaborate with Insights and Engineering, improve automation and product reliability, and communicate methodology and limitations to clients.

Build and own end-to-end, production-grade full‑stack features across backend, frontend, data pipelines, and infrastructure. Collaborate with stakeholders to translate business problems into shipped software, drive cross-team initiatives, and improve tooling and code quality. Use AI coding tools daily to accelerate development and maintain reliability for customer-facing products.

As a Research Analyst, you'll drive fundamental research for subscription-based data products, analyze large datasets, and collaborate with teams to offer insights and develop new products.

The Client Strategy Manager will manage client relationships, develop commercial strategies, and ensure client success through proactive engagement and support. This role includes account management, upselling, and enhancing client engagement with the firm's analytics services.

Lead an end-to-end data domain: define processing and validation, build reusable methodologies and QA/monitoring systems, partner with product and engineering to set technical direction, onboard new datasets, apply AI/automation to reduce manual work, and mentor junior analysts to scale domain capabilities.

Assess and evaluate third-party datasets, define success metrics and analytical frameworks, run quantitative analyses, and recommend product opportunities. Support new product development, collaborate cross-functionally with engineering and partnerships, leverage AI/LLMs and automation, and contribute to tooling, knowledge, and operational efficiency.

Design, validate, and deploy scalable statistical and distributional models for enterprise consumer analytics. Implement rigorous methodologies, convert ad-hoc analyses into version-controlled analytical libraries, and partner with Data Engineering to optimize performance and scale compute workloads (including cloud/Databricks). Deliver reproducible analytics powering dashboards and LLM-driven applications, with a focus on panel and transaction-level consumer data.