What are the responsibilities and job description for the Digital Pricing Strategist position at Teleperformance?
About Us
Teleperformance is a global leader in digital integrated business services, delivering AI-powered, data-driven solutions that optimize customer experiences, enhance workforce efficiency, and drive operational excellence.
Our Mission
We are seeking a Pricing Lead to drive dynamic, data-driven pricing strategies for gig-based workforce solutions, AIML annotation services, and Generative AI-driven offerings. This role will be instrumental in designing scalable pricing models, optimizing revenue streams, and ensuring market competitiveness.
Your Responsibilities
- Develop and implement AI-powered pricing models for gig workforce solutions, AI data annotation, and Generative AI services.
- Create cost-to-serve models that factor in gig worker availability, AI automation efficiency, and operational scalability.
- Work closely with TP.ai Dataservices product and program teams to align pricing strategies with model development costs and cloud compute expenses.
- Build predictive pricing models to optimize profitability, margin expansion, and cost efficiency.
- Analyze gig workforce engagement trends and pricing elasticity to enhance workforce deployment strategies.
- Implement dynamic pricing mechanisms to adapt to demand fluctuations, AI training costs, and real-time customer needs.
- Monitor industry pricing trends in gig workforce, AIML data services, and Generative AI markets.
- Conduct competitive benchmarking to identify market gaps and pricing differentiation opportunities.
- Partner with Sales, Product, Finance, and Operations teams to develop tailored pricing proposals.
Requirements
- At least 5 years of experience in pricing strategy, financial modeling, or revenue optimization, ideally in gig workforce platforms, AIML, or Generative AI services.
- Strong expertise in cost modeling, pricing elasticity, and revenue forecasting for AI-powered digital solutions.
- Experience working with gig economy platforms, AI data annotation services, or crowdsourced workforce management.
- Proficiency in financial analytics tools (Excel, SQL, Power BI, Tableau, or similar platforms).
- Deep understanding of AIML operational costs, cloud computing (AWS, GCP, Azure), and model training economics.
