Target—Sr Data Analyst

Job description

Description: About Target As a Fortune 50 company with more than 400,000 team members worldwide, Target is an iconic brand and one of America's leading retailers. Joining Target means promoting a culture of mutual care and respect and striving to make the most meaningful and positive impact. At Target, we have a timeless purpose and a proven strategy. Some of the best minds from different backgrounds come together to redefine retail in an inclusive learning environment that values people and delivers world-class outcomes. Target in India operates as a fully integrated part of Target's global team and supports the company's global strategy and operations. About the team The IT Data Platform (ITDP) team enables data-driven management of Target's technology ecosystem by bringing together trusted data and insights across technology assets, software delivery, infrastructure, reliability, security, engineering effectiveness, and technology operations. The Analytics team within ITDP transforms this data into metrics, analytical products, dashboards, predictive insights, and decision-support capabilities that help technology teams understand what is happening, why it is happening, where risk may be emerging, and where action is needed. The team is building toward an analytics capability that progresses from descriptive and diagnostic analytics to predictive and prescriptive insights, using statistical methods, applied data science, and GenAI where each approach is appropriate. As a Senior Data Analyst for Target's IT Data Platform Analytics team you'll: Support Target Technology leaders and teams with critical data analysis and insights that enable faster, smarter and more scalable decisions across Target's technology ecosystem. Collaborate with stakeholders to understand priorities, roadmap, and outcomes, and use data, analytics, and AI to improve technology visibility, reliability, risk management, and operational decision-making. - Interface with Target Technology representatives, Product Managers, and engineering partners to validate business and technology requirements for analysis and present final analytical results. - Design, develop, and deliver analytical and AI (GenAI, Agent, Agentic) driven solutions that result in decision support, predictive insights, and scalable technology intelligence. - Work with large-scale technology datasets using tools such as SQL-based warehouses, GCP BigQuery, Spark, or similar data platforms and pipelines. - Build and maintain analytical dashboards and self-service experiences primarily in Looker (with Power BI or similar tools as needed), using governed metrics and reusable analytical models. - Evaluate and monitor AI-driven analytical workflows, including defining quality metrics such as accuracy and relevance, assessing reliability, and measuring business impact of AI-enabled solutions. - Gather required data and perform exploratory, diagnostic, and statistical analysis to identify trends, anomalies, relationships, root causes, and emerging technology risks. - Apply advanced analytical techniques such as regression, time-series analysis, classification, forecasting, anomaly detection, segmentation, or other appropriate methods to technology problems. - Contribute to the development, validation, and monitoring of analytical frameworks such as Change Risk Score, predictive alerting, technology health signals, and other proactive insight capabilities. - Use Python, R, or similar analytical tools to prototype models, automate analysis, engineer features, and evaluate statistical or machine-learning approaches. - Translate analytical and scientific methodologies into clear business and technology terms, and present findings in a manner that technology partners and leaders can understand and act upon. - Document AI solutions, analytical methodologies, assumptions, metric definitions, and model evaluation approaches used in analytical projects. - Participate in knowledge sharing and iterative model-building practices, contributing reusable analytical patterns, documentation, and lessons learned. - Ensure compliance with corporate data protection standards and incorporate responsible AI practices, including data privacy, bias mitigation, explainability, and appropriate use of AI-driven solutions. - Partner with data engineering and platform teams to understand data lineage, quality, dependencies, and source-system context, and improve trust in analytical outputs. - Keep up to date on technology analytics, data science, GenAI, agentic AI, and emerging methodologies, and identify opportunities to apply them to ITDP use cases. About You Experience: Overall 5-8 years exp and relevant 3-5 years exp Qualification: B.Tech / B.E. or Masters in Statistics /Econometrics/Mathematics equivalent - Extensive exposure to Structured Query Language (SQL), SQL optimization, data warehousing, and BI concepts. - Proven hands-on experience in BI visualization tools (i.e. Looker, Power BI, Tableau) with ability to learn additional vendor and proprietary visualization tools. - Strong knowledge of structured and unstructured data stores and extensive hands-on experience working with large, complex datasets. - Hands-on experience in Python, R, or other open-source languages and analytical environments - Hands-on experience with advanced analytical techniques such as Regression, Time-series models, Classification, Forecasting, Anomaly Detection, or similar methods, with conceptual understanding of when to apply each technique. - Git source code management and experience working in an agile environment. - Strong attention to detail with excellent diagnostic, analytical, and problem-solving skills; ability to work through ambiguous technology problems using structured thinking. - Highly self-motivated with a strong sense of urgency and ability to work independently and in team settings in a fast-paced environment. - Competent and curious to ask questions, learn quickly, fill gaps in domain knowledge, and share knowledge with others. - Excellent communication, service orientation, storytelling, and relationship-building skills, with ability to translate complex analytical findings to technical and non-technical audiences. - Experience working with technology, software engineering, infrastructure, reliability, cybersecurity, cloud, or other IT-related data will be a strong addon. - Hands-on experience leveraging Generative AI (GenAI) and LLM-based solutions (e.g. prompt engineering, RAG, structured outputs, embeddings) to enhance data analysis, automate insight generation, and support decision-making. - Ability to integrate AI-driven tools and AI-agent workflows into end-to-end analytical processes, enabling automation, improving productivity, and scaling analytics use cases. Useful Links- Life at Target- https://india.target.com/ Benefits- https://india.target.com/life-at-target/workplace/benefits