Weave is seeking a Product Manager to lead Call Intelligence within the Analytics domain. You will transform call data, transcripts, and AI-generated insights into products that help healthcare practices improve appointment conversion, staff performance, and revenue. This role involves owning strategy, discovery, prioritization, and execution while collaborating with engineering, data, design, and go-to-market teams across India and the United States. This position is based in India and operates in a remote, distributed environment.
Responsibilities
Define the product strategy, roadmap, and measurable outcomes for Call Intelligence.
Understand healthcare practice workflows regarding inbound calls, appointment conversion, and staff coaching.
Lead discovery with customers and internal stakeholders to convert evidence into product decisions.
Partner with engineering, data, ML, and design teams to ship analytics and AI-assisted workflows.
Improve activation, adoption, and retention through instrumentation and experiments.
Make technical and product trade-offs regarding data quality, model quality, and system performance.
Create alignment across distributed teams through clear communication and outcome-driven planning.
Required qualifications
3–5+ years of product management experience, preferably in B2B SaaS, analytics, AI/ML, workflow automation, or communications software.
Demonstrated ownership of customer-facing products from discovery through launch.
Strong data fluency, including proficiency in SQL, product analytics, and quantitative analysis.
Experience defining product metrics and connecting behavior to business outcomes.
Technical fluency in APIs, integrations, data flows, and the practical limits of AI/ML systems.
Proven ability to work with engineering and data teams in distributed or cross-time-zone environments.
Strong written and verbal communication skills in English.
Ability to prioritize effectively and drive execution without formal authority.
Preferred qualifications
Experience building healthcare, healthtech, dental, practice-management, or contact-center products.
Experience with AI/ML, speech analytics, LLMs, conversational systems, or model-quality evaluation.
Understanding of frontline workflows where accuracy and reliability affect revenue.
Experience with SMB SaaS go-to-market, pricing, and packaging.
Experience working with teams split between India and the United States.
For full details about this role, click “Apply on company site” to view the employer’s original posting.
Skills for this role
Product ManagementB2B SaaSAnalyticsAI/MLWorkflow AutomationSQLProduct AnalyticsAPIsData FlowsSpeech AnalyticsLLMsConversational Systems
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Driving Call Intelligence Strategy
The core mandate is to transform raw call data and transcripts into actionable AI-driven insights. You must define the roadmap and measurable outcomes that directly impact healthcare practice revenue, appointment conversion rates, and staff performance.
Cross-Functional Collaboration in Distributed Teams
Success requires aligning engineering, data, ML, and design teams across India and the United States. You must maintain clear communication and decision-making processes to ensure execution in a remote, cross-time-zone environment.
Data-Driven Product Decision Making
You are expected to use SQL and product analytics to inform product strategy. This involves balancing technical trade-offs between model quality, data accuracy, and system performance while connecting product behavior to business outcomes.
Customer-Centric Discovery and Adoption
You must lead discovery with healthcare practices to understand their specific workflows. The priority is to translate these insights into product improvements that increase activation, adoption, and retention.
Likely challenges to think through
Managing AI/ML Technical Trade-offs
You will face the difficulty of balancing model accuracy with system speed and usability. Prepare by demonstrating how you have previously managed the practical limits of AI/ML systems and how you prioritize features when model performance is imperfect.
Aligning Distributed Stakeholders
Operating across India and the US time zones can lead to communication gaps. Prepare by highlighting your experience in creating structured documentation, decision records, and outcome-driven planning to maintain alignment without relying on formal authority.
Translating Complex Data into Simple Workflows
Healthcare staff require reliable, easy-to-use tools. The challenge is simplifying complex speech analytics into intuitive interfaces. Prepare by discussing how you have previously simplified technical products for non-technical end-users while maintaining high reliability.
03
Practice your answers
Technical practice
Managing AI/ML Model Trade-offs
What to coverDiscuss how you balance model accuracy, latency, and cost when building AI-assisted workflows. Explain your approach to evaluating model performance in a production environment where reliability is critical for healthcare users.
Data Fluency and SQL Application
What to coverDescribe a scenario where you used SQL or product analytics to identify a bottleneck in user adoption or retention. Focus on how you translated raw data into a specific product decision or feature improvement.
Designing for Healthcare Workflows
What to coverHow do you ensure that AI-generated insights or transcripts are actionable for busy healthcare staff? Address the importance of trust, accuracy, and minimizing friction in existing practice management workflows.
API and Integration Strategy
What to coverExplain your process for managing data flows and integrations between disparate systems. Discuss how you handle technical constraints when integrating new analytics features into existing communication software.
Defining Success Metrics for Call Intelligence
What to coverWhat key performance indicators (KPIs) would you track to measure the success of a new call intelligence feature? Explain how you connect these metrics to broader business outcomes like appointment conversion and revenue.
Experimentation and Instrumentation
What to coverDescribe your methodology for running experiments to improve product adoption. Detail how you instrument features to gather meaningful data and how you decide when to iterate versus pivot based on experimental results.
Handling Ambiguity in Product Discovery
What to coverWhen faced with conflicting feedback from sales, support, and customers, how do you synthesize this into a clear roadmap? Suggestion: Focus on your framework for prioritization and how you validate assumptions before committing engineering resources.
Technical Communication
What to coverHow do you explain the limitations of AI/ML systems to non-technical stakeholders? Provide an example of how you have managed expectations regarding system performance or data quality.
Behavioural practice
Driving Execution Without Formal Authority
What to coverDescribe a time you had to align cross-functional teams (engineering, design, data) to deliver a complex project. Use the STAR method to highlight how you built consensus and maintained momentum.
Collaborating in Distributed Teams
What to coverShare an example of how you successfully managed a project across different time zones (e.g., India and the US). Focus on your communication strategies, such as documentation and decision records, to keep teams aligned.
Customer-Obsessed Discovery
What to coverTell me about a time you conducted discovery that fundamentally changed your product strategy. Explain how you gathered the evidence and how you ensured the final solution addressed the user's core pain point.
Handling Product Failure or Setbacks
What to coverDescribe a situation where a feature launch did not meet expectations or performance goals. Explain how you analyzed the failure, communicated it to stakeholders, and what you learned for future iterations.
02
Sharpen your knowledge
Topics to prepare
AI/ML System Trade-offs
The role requires balancing model quality, data accuracy, and system performance. Exercise: Draft a decision framework for when to prioritize model precision over recall in a healthcare context, specifically considering the impact on staff trust and revenue.
Healthcare Workflow Integration
Understanding how call data impacts practice revenue is critical. Exercise: Map the lifecycle of a patient call in a dental practice and identify three specific points where AI-generated insights could improve appointment conversion rates.
Distributed Team Alignment
The role involves working across India and the US. Exercise: Create a template for a 'Decision Record' that effectively communicates product trade-offs to cross-functional stakeholders in different time zones, ensuring clarity and alignment.
Skills to revise
SQL and Quantitative Analysis
Strong data fluency is required for decision-making. Exercise: Write a SQL query to calculate the conversion rate of inbound calls to appointments, segmented by staff member, to identify coaching opportunities.
Product Instrumentation and Experimentation
You must improve adoption through data-driven experiments. Exercise: Design an A/B test for a new AI-assisted workflow feature, defining the primary metric, the hypothesis, and the instrumentation required to measure success.
Technical Fluency in APIs and Data Flows
You will partner with engineering on integrations. Exercise: Diagram a high-level data flow showing how a call transcript moves from a telephony system through an LLM service to a final dashboard, identifying potential latency or data quality bottlenecks.
04
Your preparation checklist
Prepare a case study on AI-driven workflow automation
Develop a concise walkthrough of a product you managed that utilized AI or analytics to improve user outcomes. Highlight how you handled trade-offs between model accuracy and system performance, and explain how you measured success through specific product metrics.
Demonstrate data fluency with SQL and analytics
Prepare to discuss how you use SQL and product analytics to drive decision-making. Be ready to explain a time you used quantitative data to identify a friction point in a user workflow and how your subsequent product changes improved adoption or retention.
Draft a strategy for cross-time-zone collaboration
Prepare specific examples of how you have maintained alignment in distributed teams across India and the US. Focus on your methods for clear written communication, such as decision records or documentation, to ensure execution without formal authority.
Formulate questions about healthcare practice workflows
Prepare thoughtful questions regarding the specific challenges of appointment conversion and staff coaching in healthcare practices. Focus on how Weave balances the need for high-reliability AI insights with the fast-paced, frontline nature of dental or medical office environments.
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