Product management is in its Cambrian explosion phase. What used to be a generalist role, often filled by talented switchers from anywhere, such as ex-marketers, software engineers, and lawyers, has become a “full-stack” craft that blends strategy, systems thinking, data fluency, and hands-on technical depth. If the “what” has changed, so has the “who”. Skills are turning over at record speed; Lightcast estimates that 32% of the skills required for the average job are different today than in 2021, with STEM roles leading the shift. Translation: PMs who can read the room and the repo are in demand.
This evolution arrived in 3 waves. Wave 1: The Translator (2000s to early 2010s) bridged business and engineering with roadmaps and iteration; deep technical skill was optional. Wave 2: The Growth Operator (mid-2010s to early 2020s) rode mobile, cloud, and product-led growth; data literacy and experimentation became essential. Wave 3: The Full-Stack Builder (now) is shaped by AI, edge computing, and rising security expectations; PMs are expected to architect systems, design with data pipelines in mind, and deliver end-to-end outcomes from prototype to production.
Siddhant’s path traces that evolution. After working at a top-tier management consulting firm, Bain & Company, he pursued graduate studies in engineering and business at Dartmouth (Thayer + Tuck), an Ivy League institution, where he was awarded a tuition award. He then moved into IoT products at one of the world’s largest telematics providers in the U.S., helping spearhead hardware–software innovations in commercial mobility, including solar-powered IoT devices and AI-driven risk analytics used by major US insurers and fleets.
“What I love is the dual impact,” Siddhant says. “We create measurable value for insurers, fleets, and drivers, and we make roads safer. That is the outcome that, as a product leader, I want to optimize for.”
Why “consultant → builder” is a superpower now
“(Early at Bain) I kept seeing big strategic bets converge on product, C-suite executives left big decisions to their Product leads,” he recalls. “The needle moved where product decisions met customer reality.”
Consulting trained him to structure ambiguity, pressure-test hypotheses with data, and maintain a relentless focus on user impact. In the AI era, the modern PM is not merely translating requirements; the work is to design systems that produce durable results; technical systems, organizational systems, and ethical guardrails that scale.
Siddhant’s technical roots run deep: an engineering undergraduate degree; leadership in IEEE and university tech communities; hands-on robotics as a technical PM for a Mars rover prototype in the University Rover Challenge. Beyond campus, he represented Indian delegations at Harvard Model UN and Singapore National Model UN, won laurels, and mentored future teams; this became practical training for complex stakeholder negotiation. On the research side, he has authored more than 16 papers across IoT, AI, and machine learning with more than 230 citations in the last 5 years; he also reviews for journals in the IEEE and Springer Nature families, which is uncommon among PMs and signals subject-matter rigor.
Dartmouth College’s interdisciplinary Master of Engineering Management helped him fuse technical and managerial strands. The program blends graduate-level engineering with MBA-style coursework. Siddhant points to Data Analytics Project Lab and Platform Strategy as pivotal.
“Those courses pushed us beyond cases to actual builds,” he says. “Data pipelines, experiments, pricing logic; that is where a full-stack mindset becomes muscle memory.”
What’s top of mind for full-stack PMs: Two shifts sit squarely on Siddhant’s roadmap. First, agentic AI is moving from copilots to co-workers; expect automation of claims triage, policy servicing, and risk alerts with auditable guardrails. Second, sensor-to-insight latency is collapsing via edge inference paired with energy-harvesting hardware; for commercial mobility, this means faster on-device detection such as crash and tamper events, lower backhaul costs, and devices that stay healthy in the field. Long-haul fleets depend on uptime; the products that win will not only report reality, they will improve it.
Why this matters—from Siddhant’s perspective: The market has raised the bar and narrowed the funnel. After the 2022 peak, PM postings cooled through 2024 and into mid-2025, yet stayed above pre-pandemic levels; demand exists, but it is concentrated and choosier. In practice, hiring managers expect product leaders who are conversant in models, data, and systems; they also expect proof of outcomes. Interviews reflect that shift. Beyond classic product sense and execution, candidates now routinely face:
Technical design walkthroughs that tie APIs, data models, and integration trade-offs to business goals;
Live or lightweight SQL or coding screens that validate analytical rigor, even for PM roles;
Take-home PRDs, case studies, and roadmaps that connect user value to measurable impact;
Stakeholder role-plays that test influence, negotiation, and crisp decision-making.
Siddhant’s advice to emerging PMs:
Be deliberately full-stack. Cross-train on purpose. “I balanced IEEE and Debating Society leadership, Bain and academic research, and then Dartmouth’s engineering-plus-business program. Range matters; depth matters too.”
Be audit-ready. “Treat data governance, bias and drift monitoring, and failure-mode instrumentation as core product surfaces, not legal footnotes. Recent conversations with AI leaders, incl. my last year’s round table with Open AI CTO, reinforced how essential this is for GenAI and LLMs.”
Build systems that prove themselves. “Ship features that move hard outcomes such as claim-cycle time, crash reduction, device uptime, and loss-ratio impact; skip vanity dashboards.”
Show your work in interviews. “Be ready to whiteboard an event-driven architecture, write the metrics spec and the SQL to validate it, and defend the unit economics to leadership; that is the job on Monday morning.”
The full-stack PM is not a unicorn, it is the operating system for product organizations that want durable, measurable outcomes delivered safely, repeatedly, and at scale.
About Siddhant Banyal
Siddhant Banyal is an accomplished Product Manager with over 5 years of experience driving innovation in IoT, AI, and enterprise technology. Previously an Associate at Bain & Company, he holds a Master of Engineering Management from Dartmouth College and a Bachelor of Engineering degree from Delhi University. Siddhant has led cross-functional teams to launch products generating millions in revenue, including IoT devices for usage-based insurance and AI-powered enterprise solutions. His expertise spans machine learning, data analytics, and strategic consulting, with certifications in AI and Product Management. He excels at translating complex technical concepts into market-ready products across IoT and enterprise software domains.