Assistant Manager-Analytics

The Smart Cube, a WNS company, is seeking Assistant Managers who will collaborate with the Project Lead to design effective analytical frameworks aligned with client objectives. The Assistant Managers will translate requirements into clear deliverables, manage data preparation, perform quality checks, and ensure analysis readiness. They should possess expertise in implementing analytical techniques and machine learning methods such as regression, decision trees, segmentation, forecasting, and algorithms like Random Forest, SVM, and ANN. Additionally, they are responsible for sanity checks, quality control, and interpreting results in a business context to identify actionable insights. Assistant Managers will independently handle client communications, interact with onsite leads, and manage the entire project lifecycle from initiation to delivery. This includes translating business requirements into technical specifications, overseeing data teams, ensuring data integrity, and facilitating communication between business and technical stakeholders. They will lead process improvements in analytics and act as project leads for cross-functional coordination. In terms of client management, Assistant Managers will serve as client leads, maintain strong relationships, participate in deliverable discussions, and guide project teams on execution strategies. Proficiency in connecting databases with Knime, understanding SQL concepts, and designing Knime ETL workflows to support BI tools is required. They must also be proficient in PowerBI for building dashboards and supporting data-driven decision-making. Knowledge of leading analytics projects using PowerBI, Python, and SQL to generate insights is essential. Ideal candidates should have 4-7 years of experience in advanced analytics across Marketing, CRM, or Pricing in Retail or CPG. Experience in other B2C domains is also acceptable. Proficiency in handling large datasets using Python, R, or SAS, and experience with multiple analytics or machine learning techniques is required. Candidates should have a good understanding of consumer sectors such as Retail, CPG, or Telecom, and experience with various data formats and platforms including flat files, RDBMS, Knime workflows and server, SQL Server, Teradata, Hadoop, and Spark. Strong written and verbal communication skills are essential for creating client-ready deliverables using Excel and PowerPoint. Basic knowledge of statistical and machine learning techniques like regression, clustering, decision trees, forecasting, and other ML models is also necessary. Knowledge of optimization methods, supply chain concepts, VBA, Excel Macros, Tableau, and Qlikview will be an added advantage. Qualifications: - Engineers from top tier institutes (IITs, DCE/NSIT, NITs) or Post Graduates in Maths/Statistics/OR from top Tier Colleges/Universities - MBA from top tier B-schools,