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Target Internship Drive 2024 Details :
Target Internship Drive 2024 Eligibility Criteria :
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- 2+ years of experience into Data Analyst role.
- 2 +years of ongoing break from work
- B.Tech / B.E. or Masters in Statistics /Econometrics/Mathematics equivalent
- Very Strong SQL or PL/SQL programming skills
- Big data experience
- Exposure to R, Python, Hive, or other open source languages
- Understanding of foundational mathematics and statistics
- Conceptual understanding of analytical techniques like Linear Regression, Logistic Regression, Time-series models, Classification Techniques, etc.
- Strong written and verbal communication skills to explain complex analytical methodologies to clients regardless of the clients technical expertise
- “Hands-on” experience in analytics / data science preferable
- Any experience with Retail, Merchandising, Marketing will be strong addons
Job Descriptions :
Presenting a program that provides women candidates, who’ve taken a career break, an opportunity to gain relevant work experience while being part of collaborative teams – BounceBack. Are you an analytics oriented woman professional on a career break and looking for an opportunity to re-tool your skill-set in the Data Analytics Space? This 16-week comprehensive and application-intensive Internship program is specially designed for you. At the Data Analytics India team in Target Corp., we’re solving cutting-edge problems in Retail using a combination of statistical analysis, optimization and behavioural economics.
A few problems we’re trying to solve today are: “How do I predict a noisy outcome like customer buying behaviour (or sales)?”
“What operational signals tell me if a customer is going to churn in 6 months to a year?”
“What is the financial value of making a customer “happy”? What exact initiative made a customer happy and by how much?”
“What will happen to my sales if I reduce the queues in my store by 2 people?”
“How much additional backroom space do I need, and in which stores, to maintain in-stocks during peak sales season?”
“How can I improve workload forecast for stores to optimize payroll?”
“What factors drive unavailability of products in Stores?”
