Data Analyst-2

Target is an iconic brand, a Fortune 50 company and one of America’s leading retailers.

A role with **Data Science and Analytics (DSA)** means being a part of the team that works closely with the business and identifies problems / opportunities for improved decision-making through better data analysis. This covers the whole gamut from simple descriptive analysis to more complex predictive and prescriptive analytics, using advanced modeling and machine learning techniques primarily using open source technologies and big data platforms. The emphasis is on actionable insights, which is possible through a combination of technical skills and business understanding.

As Data Analyst, DSA you will work closely with business/product teams and understand their priorities/roadmap. Based on this understanding, you are expected to identify appropriate metrics that will drive the right decisions for the business, and then build reporting solutions to deliver these metrics at the required frequency in an optimal and reliable fashion. You will also answer ad-hoc questions from your business users by conducting quick analysis on relevant data, identify trends and correlations, and form hypotheses to explain the observations. Some of these will lead to bigger analytical projects of increasing complexity, where you will work initially as a part of a bigger team, but also work independently as you gain more experience. Finally, you are expected to always adhere to project schedule and technical rigor as well as requirements for documentation, code versioning, etc.

Core responsibilities are described within this job description. Job duties may change at any time due to business needs.
- Drive a continuous improvement mindset by seeking out new ways to solve problems through formal trainings, peer interactions and industry publications to continually improve technically, implement best practises and analytical acumen
- Be expert in specific business domain, self-directed and drive execution towards outcomes, understand business inter-dependencies, conduct detailed problem solving, remediate obstacles, use independent judgement and decision making to deliver as per product scope, provide inputs to establish product/ project timelines
- Participate in learning forums, or be a buddy to help increase awareness and adoption of current technical topics relevant for analytics competency e.g. Tools (R, Python); exploratory & descriptive techniques ( basic statistics and modelling)
- Champion participation in internal meetups, hackathons; presents in internal conferences, relevant to analytics competency
- Contribute the evaluation and design of relevant technical guides and tools to hire great talent by partnering with talent acquisition
- Participate in Agile ceremonies to keep the team up-to-date on task progress, as needed
- Develop and analyse data reports/Dashboards/pipelines, do RCA and troubleshooting of issues that arise using exploratory and systemic techniques

**About you**:

- B.E/B.Tech (2-3 years of relevant exp), M.Tech, M.Sc., MCA (+2 years of relevant exp)
- Strong data understanding inference of patterns, root cause, statistical analysis, understanding forecasting/predictive modelling,, etc.
- Advanced SQL experience writing complex queries
- Hands on experience with analytics tools: Hadoop, Hive, Spark, Python, R, Domo and/or equivalent technologies
- Experience working with Product teams and business leaders to develop product roadmaps and feature development
- Able to support conclusions with analytical evidence using descriptive stats, inferential stats and data visualizations
- Strong analytical, problem solving, and conceptual skills.
- Demonstrated ability to work with ambiguous problem definitions, recognize dependencies and deliver impact solutions through logical problem solving and technical ideations
- Excellent communication skills with the ability to speak to both business and technical teams, and translate ideas between them
- Intellectually curious, high energy and a strong work ethic
- Comfort with ambiguity and open-ended problems in support of supply chain operations