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Common analytics/ML skillset attracts moderate candidate competition.
Technical ML and analytics skills are broadly transferable across industries.
Requires specific ML, statistical and analytics skills but lacks explicit years or certifications.
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Solve business problems for clients across industries using AI, ML, data engineering, and advanced analytics methods.
Develop insights and strategic solutions for decision-making in areas like marketing, pricing, risk, and fraud using tools like SAS, SQL, R, Python, Tableau, and Power BI.
Own and deliver all assigned tasks including problem structuring, hypothesis development, modeling, and presenting results to clients and internal teams.
Experience Required: Not explicitly mentioned in the JD.
Strong academic performance with superior analytical and quantitative skills.
Knowledge or certification in analytics tools such as Python, R, SAS, SQL, Power BI is preferred.
Understanding Agile or waterfall lifecycle and experience with RPA platforms or workflow tools is a plus, but not mandatory.
Able to structure and solve complex business problems analytically using advanced data science and statistical tools.
Comfortable working independently to deliver accurate analytics outputs with minimal supervision.
Effective communicator capable of presenting technical analysis and strategic recommendations to clients and stakeholders.