Top Tech Jobs & Startup Jobs

Performs data analysis, cleans and validates data, identifies trends and anomalies, builds dashboards and reports, and supports KPI development. Collaborates with engineers and business stakeholders to improve data quality, pipelines, reporting requirements, and analytical processes. Leads data-driven change management, communicates insights, shares best practices, and may support urgent IT incidents.

Develops, tests, evaluates, and deploys machine learning models using established architectures and frameworks. Supports feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborates with engineering and product teams on AI requirements, researches methods to improve model performance, and supports urgent IT incidents. The role requires strong Python and machine learning experience, knowledge of deep learning and generative AI, and advanced quantitative education.

Develop, test, and deploy machine learning models; support feature engineering, preprocessing, evaluation, and automated training and monitoring pipelines. Collaborate with engineering and product teams to define AI solutions, research methods to improve performance, and support urgent IT escalations. The role requires advanced quantitative education, 5+ years of AI/ML engineering experience, Python expertise, and knowledge of deep learning and generative AI.

Leads enterprise data science and AI solution delivery, including ML, generative AI, and predictive model development, deployment, monitoring, governance, and integration into business workflows. Drives AI adoption, change management, value realization, and evaluation of emerging technologies while partnering with data, engineering, and platform teams. Builds and leads AI delivery teams and ensures compliance with security and governance standards.

Build, test, deploy, and evaluate machine learning models using established frameworks. Support feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborate with engineering and product teams to define AI requirements, research performance improvements, and respond to critical IT incidents. The role requires advanced quantitative education, 5+ years of AI/ML engineering experience, Python expertise, and knowledge of deep learning and generative AI.

Serve as liaison between store operations and IT for SAP S/4 IS-Retail, SAP CAR, POS and ECOM. Gather requirements, document processes, support UAT, analyze transaction discrepancies, drive process improvements, maintain business documentation, and support store adoption of system changes to improve inventory, receiving, and POS workflows.

Leads enterprise data science and AI solution delivery, deployment, scaling, and adoption. Oversees machine learning, generative AI, and predictive model development, monitoring, governance, and integration into business workflows. Partners with technology and business stakeholders, evaluates emerging AI technologies, drives change management and value realization, and develops AI delivery teams.

Oversee end-to-end cash management and accounting operations, including store and non-store cash, reconciliations, reporting, gift cards, armored transportation, bank fees, controls, and electronic payments. Lead process improvements, vendor and cross-functional partnerships, offshore team performance, and financial submission reviews. Ensure accurate reporting, governance, compliance, fraud prevention, budget control, and timely completion of activities across legal entities and bank accounts.

Performs data analysis, statistical analysis, dashboard and report development, KPI measurement, data quality monitoring, and data modeling support. Collaborates with engineers and business stakeholders to define reporting requirements, improve data pipelines, communicate insights, and guide data-driven process and tooling changes. Provides peer coaching and may support urgent IT incidents.

This co-op develops analytical solutions for fulfillment performance reporting. Responsibilities include understanding fulfillment processes, consulting on data pipelines, developing data models and dashboards, analyzing trends, and providing insights and recommendations. The role collaborates with product managers, visualization analysts, data scientists, IT, technical teams, and fulfillment operations.