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Mid-level, generalist ML/data role at a known analytics firm attracts many qualified applicants.
Low: ML and data science skills are broadly transferable across industries.
Medium: technical skills expected but no strict years or mandatory certifications specified.
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Apply AI, machine learning, data engineering, and advanced analytics to solve business problems across various industry verticals (banking, insurance, healthcare, retail, etc).
Generate actionable insights using data science and business intelligence tools (e.g., SAS, SQL, R, Python, Tableau, Power BI) to support decision making in areas like marketing, risk, fraud, pricing.
Own end-to-end delivery of assigned analytical tasks including hypothesis development, solution formulation, presenting results to clients, and contributing to IP such as advanced modeling methodologies and training materials.
Proficiency or strong knowledge in analytics and statistical tools such as Python, R, SAS, SQL, Power BI, Tableau.
Strong academic background with superior analytical and quantitative skills.
Work Experience Required: Not explicitly mentioned in the JD.
Familiarity with Agile or waterfall development lifecycle and RPA platforms/workflow tools is a preference but not mandatory.
Comfortable working across diverse client industries and developing actionable, structured problem-solving approaches.
Practices ownership of end-to-end analytics delivery including technical execution and client communication.
Capable of innovating in modeling and analytics methodologies to contribute to intellectual property and advanced analytical training.