TargetSr Data Analyst
Target is hiring a Sr Data Analyst. Level rates it ; you can apply on Target.
Level's read on this job
- Level rates this data analyst role Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting mentions prompt engineering, RAG and AI safety among its tools and skills.
- Target has 14,000+ other open roles on Level.
- Posted in the last few days.
AI in this role
As a Sr Data Analyst for Target’s Data Analytics team you’ll:
Support our world-class leadership team at Target with critical data analysis that helps business team to make profitable decisions. Enable faster, smarter and more scalable decision-making to compete and win the modern retail market. Collaborate with stakeholders and understand their priorities/roadmap to drive business strategies using data.
Key Responsibilities:
- Translate business problems into well-defined analytical questions and structured approaches
- Support scenario, decision, and action modelling to evaluate business trade-offs and inform recommended actions
- Partner with Target business stakeholders to understand priorities and roadmaps, validate analytical requirements, and present insights and recommendations with clarity and impact
- Develop and deliver analytical and AI-driven solutions (including GenAI, Agent, and Agentic approaches) that enable decision support, forecasting, optimization, and automation
- Apply advanced analytics techniques, including causal, predictive, and prescriptive analytics, to drive deeper understanding of business levers and inform optimal actions
- Work with large-scale datasets using platforms such as GCP BigQuery, Spark, and SQL-based data warehouses; build and maintain reliable data pipelines using Airflow or similar orchestration tools
- Contribute to AI-driven analytical workflows, defining quality metrics (e.g., accuracy, relevance), assessing reliability, and measuring tangible business impact
- Ensure analytical outputs are accurate, scalable, and aligned with business context
- Develop strong data storytelling skills to communicate insights and recommendations to non-technical audiences
- Document analytical methodologies, assumptions, and outputs to support reuse and knowledge sharing
- Adhere to corporate data protection standards and responsible AI practices
Requirements / About You:
Experience: Overall 5-8 years exp and relevant 3-5 years exp
Qualification:
TI : B.Tech / B.E. or Masters in Statistics /Econometrics/Mathematics equivalent
US : BA/BS in a quantitative degree – Math, Statistics, Econometrics, Data Sciences, Computer Science or equivalent work experience
Skills required:
1. Extensive exposure to Structured Query Language (SQL), SQL Optimization and DW/BI concepts.
2. Proven hands-on experience in BI Visualization tool (i.e. PBI, Looker, Tableau) with ability to learn additional vendor and proprietary visualizations tools.
3. Strong knowledge of structured (i.e. Teradata, Oracle, Hive) and unstructured databases including Hadoop Distributed File System (HDFS). Exposure and extensive hands-on work with large data sets.
4. Hands on experience in R, Python, Hive or other open-source languages/database
- Hands on experience in any advanced analytical techniques like Regression, Time-series models, Classification Techniques, etc. and conceptual understanding of all the techniques mentioned above
- Git source code management & experience working in an agile environment.
7. Strong attention to detail, excellent diagnostic, and problem-solving skills
8. Highly self-motivated with a strong sense of urgency to be able to work both independently and in team settings in a fast-paced environment; capability to manage urgency timelines
9. Competent and curious to ask questions and learn to fill gaps, desire to teach and learn.
10. Excellent communication, service orientation and strong relationship building skills
11. Experience with Retail, Merchandising, Marketing will be strong addons
12. Hands-on experience leveraging Generative AI (GenAI) and LLM-based solutions (e.g., prompt engineering, Retrieval-Augmented Generation – RAG) to enhance data analysis, automate insight generation, and support business decision-making
13. 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
Know More About Us here:
- Life at Target: https://india.target.com/
- Target Tech: https://tech.target.com/
How we rate this
Sr Data Analyst at Target rates 62 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data analyst jobs at this level
Pay
- Typical data analyst job
- $110k
This posting doesn't list pay. Use the typical data analyst figure when pay comes up. See data analyst jobs
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Describe a typical day in a role like this one: which parts run through AI directly?
- If you removed AI from this role, what would be left, and how do you decide what still needs a human?
Adapt your resume
- List these exact terms on your resume: Prompt engineering, RAG, and AI Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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