Staff Product Designer (B2C, UI/UX)

Role & Responsibilities

• UX/UI Matchmaker: Lead end-to-end design for apps/web – from profile swipes to wedding planning flows. Turn single users into success stories with intuitive, delightful interfaces!
• Design System Guru: Build, own & evolve our Shaadi Design System (Figma-based) – components, tokens, guidelines that scale across products. No more design debt drama!
• B2C Superpowers: Deep-dive user research, A/B tests, & analytics in high-traffic consumer apps. Personalize journeys for 35M+ users – think AI matches, chat boosts & premium perks!
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  Cross-Func Collab: Partner with PMs, Engg, & Data teams to ship fast. Mentor juniors, run critiques, & evangelize design thinking. Be the design doctor for sticky features!

Ideal Candidate

• Strong Product Designer, with focus on B2C Products
• Mandatory (Experience 1): Must have 5+ YOE in Product Design (both UI and UX) with atleast 2 year in B2C companies
• Mandatory (Experience 2): Must have atleast 1 year experience building or working with highly scaled Design systems in a B2C company (non-negotiable)
• Mandatory (Skills): Must have strong knowledge and experience of design with Ai tools(Figma Cloud, Chatgpt)

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  Mandatory (Portfolio): Must have strong product design portfolio of Good B2C Products, with at least 2 detailed case studies
• Mandatory (Company): B2C Product companies, B2B experience with prior exposure to B2C is also acceptable (High scaled company, mostly unicorns)
• Mandatory (Stability): Must have stayed for a minimum of 2 years with each of the previous companies
• Mandatory (Note 1): CV must have portfolio link with at least 2 detailed case study
• Mandatory (Note 2): This will be a complete IC role
• Mandatory (Note 3): CTC is inclusive of 5%variable
• Preferred (Education): Graduate from Tier-1 colleges (IIT, NID, NIFT, BITS etc)
• Preferred (Skills 1): Experience in subscription-based or marketplace products, CRO, and international market localization
• Preferred (Skills 2): Exposure to AI-powered personalization and recommendation systems