Technology Jobs in Bangalore, India (Now Hiring) — 843 open — Page 10

About Glance

Glance is a consumer technology platform reaching hundreds of millions of users across Android, iOS, and TV in the US and Asia. Our products surface content, commerce, and AI-powered experiences across mobile lock screens, connected televisions, and ambient surfaces — the moments where consumer attention begins. We sit at the intersection of AI, behavioural science, and consumer psychology, which means research here is never decorative: it directly shapes what gets built and how it reaches people.

About the Team

The Foresight & Behavioral Insights Unit exists to decode how consumers behave, think, and evolve — and to make that intelligence an unfair advantage for Glance. The team spans consumer understanding, market intelligence, experimentation, and Voice of Consumer programs. We work closely with Product, Growth, Marketing, and Data Science, and our output shows up in strategy decks, product roadmaps, and partner conversations alike.

Our market is the United States; the team operates in-office out of Bangalore. Bridging that geographic and cultural distance is part of the craft.

The Role

We're looking for a Senior Mixed Methods Researcher to bridge the qualitative-quantitative divide on the team and act as the senior research voice into Product, Growth, and Data Science on our most strategic questions. This is a senior individual contributor role with mentoring responsibility — you'll set the bar for how the team approaches mixed-methods work and how it integrates AI into the research craft.

What You'll Do

Strategic & Mixed-Methods Research Leadership

• Design and lead mixed-methods research programs across Consumer Understanding, experimentation, and strategic research — from problem framing through fieldwork to recommendation
• Conduct grounded theory-style strategic research where the question is open-ended and the answer needs framing as much as evidence
• Inform experiment design with behavioural science principles and lead post-experiment interpretation — why something worked, why it didn't, and what to test next

Hypothesis Generation & Data Partnership

• Generate hypotheses for Data Science from qualitative signals; investigate anomalies in product and funnel data to surface the "why" behind the numbers
• Partner with Product, Growth, and Data Science on funnel behaviour, decision heuristics, and cohort identification
• Read quantitative outputs critically rather than deferentially — cross-check the stories the data tells

Mentoring & Methodological Bar-Setting

• Mentor existing UX researchers on methodology, study design, stakeholder engagement, and AI fluency
• Set the bar for how the team approaches mixed-methods work — what good looks like, where rigour is non-negotiable, where speed wins
• Set the bar for how the team uses AI in research — evaluating tools, building or adopting workflows, teaching what good AI-augmented research looks like in practice
• Author internal frameworks and playbooks that lift the team's collective craft

How You Work With AI

AI is a daily research tool on this team and a primary signal we hire on at this level. Senior researchers here are past the experimentation phase, with non-trivial integration in their own work and the judgment to teach the team what good AI-augmented research looks like.

In interview, we will ask for specifics:

• Workflows you've built or adopted, and what they actually do for you
• A case where you caught the model getting something wrong, and how
• A case where you updated a thesis because the model was right and you were wrong
• Where AI sharpens research, and where it would degrade it if used uncritically

Bonus: you have built prompt chains, agent pipelines, or research-specific evaluations.

What We're Looking For

Required

• 7+ years in user, consumer, or behavioural research, with at least 3 years leading studies that integrate qualitative and quantitative methods
• Demonstrated cross-cultural research experience — direct US fieldwork, prior US consumer research practice, or a clear track record of bridging geographic and cultural distance in research
• Strong grounding in behavioural science — decision heuristics, judgment under uncertainty, and at least one applied framework you use rigorously
• Comfort working with data teams: able to translate qualitative signals into testable hypotheses and to read quantitative outputs critically rather than deferentially
• Track record of recommendations being adopted by product or marketing teams, with examples you can walk through
• Mentoring experience — formal or informal — with junior researchers
• A hustler's instinct, voracious curiosity, and a fast-learning habit — you don't wait for full briefs to start investigating, you read widely across methods and adjacent disciplines, and you set this tone for the team rather than just embodying it yourself

Preferred

• Background in consumer tech, commerce, or content/discovery products
• Experience with agentic commerce, conversational AI, or shopping-with-AI categories
• Direct experience researching US consumers from outside the US
• Track record of research that shaped strategic or leadership decisions, not only feature-level product execution
• Experience working closely with Data Science or ML teams — informing model evaluation, training data quality, or AI product evaluation from a research lens
• Strong command of longitudinal qualitative methods — diary studies, ethnographic research, or extended panel work
• Public research output: published papers, conference talks, or substantive contributions to research methodology
• Comfort with research stacks common to remote-heavy practice — dscout, Userlytics, Maze, Qualtrics, Lyssna, UXtweak, or equivalents

Why This Role

• You'll shape methodological practice across a growing research team — the senior research voice the team is currently missing
• Senior IC role with mentoring, no people-management overhead — your craft remains your own
• The team's AI practice is yours to set the bar on — leeway to define what good looks like, not inherit it
• Direct line into Product, Growth, and Data Science — your hypotheses get tested, your recommendations move things
• Glance operates at scale most teams never see — your work covers a user base in the hundreds of millions