Product Lifecycle Analyst

Job Description

Description: POSITION SUMMARY: Product Lifecycle analyst is responsible for developing analytical models for product usage rates for various WAI product categories for different regions and incorporating those models to prioritize new product development, determine market sizing and estimate revenue and inventory positions ESSENTIAL DUTIES AND RESPONSIBILITIES: Utilize latest analytical methods to determine product usage/ replacement rate models Establish predictive analytics for prioritization of research, revenue estimation, annual forecasts of potential new products and product category market sizing Develop and maintain key KPI visualizations using power BI Lead product development prioritization sessions Employ ML/AI improve effectiveness of above Requirements: QUALIFICATIONS (Education, Experience, & Skills required) Education: Bachelor’s degree in Engineering, Computing, Analytics or related field. Masters preferred. Experience: 5+ years of experience in analytical modeling, visualization and data insights gathering Experience in data analytics suite including but not limited to R, Python, SQL, Power BI, MATLAB, data connectors and database automation Proven track record in ROI projects in analytics Product Knowledge Familiarity with automotive aftermarket, parts distribution, or adjacent sectors (e.g., industrial goods, ecommerce B2B/B2C) is strongly preferred. Knowledge of product and catalog management tools Soft Skills Strong work ethic and self driven Comfortable with data-driven decision-making, performance scorecards, and KPIs. Able to operate in a fast-moving, matrixed, and remote-first environment. Strong interpersonal, communication, and influencing skills at all organizational levels. Analytical and problem-solving skills with a data-driven approach to business strategy. Experience working with data and content via various systems/formats including ERP, PIM, ACES/PIES and TecDoc. Proficiency in project management tools, MDM platforms, and data analytics. Experience in data governance, master data clean-up, and ownership frameworks. Change management experience, particularly in transitioning from decentralized to centralized models.