Staff Product Manager

AiDASH, Inc. · Bengaluru, Karnataka, India; Gurugram, Haryana, India

// classified as

Data Leadership (Heads of data, directors, managers.)

> stack

cshell

> description

<div class="content-intro">About AiDASH AiDASH is an enterprise AI company and the leading provider of vegetation risk intelligence for electric utilities. Powered by proprietary VegetationAI™ technology, AiDASH delivers a unified remote grid inspection and monitoring platform that uses a SatelliteFirst approach to identify and address vegetation and other threats to the grid. With a prevention-first strategy to mitigate wildfire risk and minimize storm impacts, AiDASH helps more than 140 utilities reduce costs, improve reliability, and lower liability across their networks. AiDASH exists to safeguard critical utility infrastructure and secure the future of humanAIty™. Learn more at <a href="https://www.aidash.com." target="_blank">www.aidash.com.</a> We are a Series C growth company backed by leading investors, including Shell Ventures, National Grid Partners, G2 Venture Partners, Duke Energy, Edison International, Lightrock, Marubeni, among others.  We have been recognized by Forbes two years in a row as one of “America’s Best Startup Employers.”  We are also proud to be one of the few  software companies in Time Magazine’s “America’s Top GreenTech Companies 2024”. <a href="https://www2.deloitte.com/us/en/pages/technology-media-and-telecommunications/topics/north-america-technology-fast-500.html?utm_source=bngaiwebsite&utm_medium=referral&utm_campaign=evolve-banner" target="_blank">Deloitte Technology Fast 500</a>™ recently ranked us at No. 12 among San Francisco Bay Area companies, and No. 59 overall in their selection of the top 500 for 2024.   Join us in Securing Tomorrow!</div><h4>The Role</h4> Reporting to the VP of Product Management & Process Excellence, you'll own how AiDash's production workflows — the pipelines that turn satellite imagery into customer-grade insights — are designed, automated, and continuously improved.  You'll start by going deep on IVMS, where the workflow is most complex and the automation upside is largest. Over time, the scope expands to AIMS and CRIS.  You won't be designing customer-facing product features. You won't be building the internal platform (that's our Platform PM, your closest peer). You'll be designing the operating model that sits between them — the steps, frameworks, and policies that determine how an insight gets produced, who or what does each step, and where the human stays in the loop. <h4>How you'll make an impact:</h4> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="1" data-aria-level="1">Workflow design across products. Define what the production workflow looks like end-to-end for each product: the sequence of steps, the cohort logic (which customers / geographies / products take which path), the handoffs, and the SLAs. </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="2" data-aria-level="1">Step-level frameworks. Author the operating frameworks for individual steps — e.g., the image acquisition framework (when do we re-order? from which vendor? what freshness threshold?), the model QC framework (what's the sampling strategy by model age, terrain, sensor?), and similar for every critical step. </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="3" data-aria-level="1">Autonomy and human-in-the-loop policy. Decide where the workflow runs autonomously and where humans intervene. Set and own the confidence thresholds at which model output is trusted enough to drop QC. The technical specifics — sampling strategies, model evaluation methods, HITL mechanics — are owned by a pod of applied AI data scientists and analysts you'll partner closely with. You own the policy decision; they own the underlying technical work that informs it. </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="4" data-aria-level="1"> Cohort logic and CS alignment. Decide which customers get which workflow flavor. CS and leadership are key stakeholders you'll bring along; </li> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="5" data-aria-level="1"> Requirements to Platform PM: Translate workflow design into clear system requirements (e.g., “at step X, capture labels with confidence scores and reviewer ID”). Platform PM owns the system spec; you own that the workflow as designed produces the data and outcomes you need  </li> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="5" data-aria-level="1">KPIs: Own the operating metrics — cost per insight, cycle time, % auto-resolved, manual touches per job, quality against SLA. Move them continuously </li> </ul> <h4> What Success looks like in 12 months,</h4> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="7" data-aria-level="1">The most consequential IVMS workflow transformation in years is shipped, stable in production, and delivering the cost and automation impact it promised </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="8" data-aria-level="1">A clear, sequenced plan exists for moving IVMS toward an autonomous-by-default workflow, with human intervention narrowed to a well-defined slice </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="9" data-aria-level="1">Manual-touch volume on IVMS is materially down vs. baseline; the cost-per-insight curve bends </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="10" data-aria-level="1">Every critical workflow step has a metric, a target, and a dashboard. The org can answer “how is the workflow performing this week?” without a Slack thread </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="11" data-aria-level="1">DS, Platform, CS, and GIS Ops treat your decisions as authoritative </li> </ul> <h4> What this role is not:</h4> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="12" data-aria-level="1">Not a feature PM role: You won't be designing what utilities see in the AiDash product. That's owned by our IVMS / AIMS / CRIS PMs </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="13" data-aria-level="1">Not a platform or internal-tools PM role: You won't be writing PRDs for the labeling tool, QC dashboard, or routing engine. That's owned by our Platform PM — your peer, not your scope </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="14" data-aria-level="1">Not an operations role. You won't run the workflow at scale. CS is the operating muscle that runs the workflow you design; GIS Ops is a separate execution function </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="15" data-aria-level="1">Not a process consultant or Op Excellence role: You don't map AS-IS / TO-BE flows and hand them over. You own the workflow as a product — its design, metrics, and evolution — and you're accountable to outcomes </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="16" data-aria-level="1">Not a data science role: You don't build models or design technical model evaluation strategies. You partner with the applied AI pod that does, and you make the policy calls their work informs </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="17" data-aria-level="1">Not customer-facing: You won't be in front of customers directly. The customer voice reaches you through CS and feature PMs </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="18" data-aria-level="1">Not a pure strategy or architect role: You'll be hands-on with frameworks, specs, and decisions and driving adoption of the workflow </li> </ul> <h4> What we're looking for:</h4> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="19" data-aria-level="1">9-13 years total, with at least 4-6 years as a Product Manager </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="20" data-aria-level="1">At least one tour owning an internal, operational, or platform-style product — not exclusively customer-facing feature PM </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="21" data-aria-level="1">Has owned a meaningful cross-functional outcome at scale — not just shipped projects, but shaped how an organization made decisions in a domain </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="22" data-aria-level="1">Demonstrated ownership of operating KPIs (cost, cycle time, throughput, automation %, quality) under real accountability </li> </ul> How You Operate  <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="23" data-aria-level="1">Shapes the question before deciding the answer. When handed an ambiguous problem, you don't just solve it as posed — you reframe it, sharpen the metric, and tell us when we're optimizing for the wrong thing </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="24" data-aria-level="1">First-principles process thinker. You can look at a 20-step workflow, ask why step 7 exists, and not lose nuance in the process </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="25" data-aria-level="1">Metric-native. You reach for a measurement before a meeting. You don't ship a workflow without a way to tell whether it's working </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="26" data-aria-level="1">Comfortable being the decider. When cohort logic or automation policy is contested, you make the call and defend it. You don't outsource hard calls upward by default </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="27" data-aria-level="1">Credible with technical peers. DS, Platform PM, and engineering find you a strong partner. You can hold a substantive conversation about model performance, confidence thresholds, and system constraints </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="28" data-aria-level="1">Writes well, thinks in writing. Your primary artifacts are documents — workflow charters, frameworks, decision logs </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="29" data-aria-level="1">Influences without authority. CS, GIS Ops, the applied AI pod, and Platform don't report to you. You move them through clarity, credibility, and shared metrics </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="30" data-aria-level="1">Owner mentality. When the workflow underperforms, you don't say “the model was off” or “execution slipped.” You own the outcome and find where the design failed</li> </ul> <h4>Nice to have:</h4> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="31" data-aria-level="1">Experience working on workflows or products that combine model output with human review, and is comfortable making decisions that depend on model performance. </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="32" data-aria-level="1">Background in geospatial / remote sensing / satellite imagery, utilities, climate-tech, or labeling pipelines. </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="33" data-aria-level="1">Have been on the receiving end of a badly designed workflow at some point. Scar tissue produces better designers. </li> </ul> <ul> <li data-leveltext="•" data-font="" data-listid="2" data-list-defn-props="{"335552541":1,"335559685":540,"335559991":270,"469769242":[8226],"469777803":"left","469777804":"•","469777815":"hybridMultilevel"}" data-aria-posinset="34" data-aria-level="1">Experience scaling a function from zero at a Series B–D company in a resource-constrained environment. </li> </ul> We are proud to be an equal-opportunity employer. We are committed to embracing diversity and inclusion in our hiring practices, and we promote a work environment where everyone, from any race, color, religion, sex, sexual orientation, gender identity, or national origin, can do their best work. <div class="content-conclusion"><div>We are committed to providing an inclusive and accessible interview experience for all candidates. Please let us know if you require any accommodation during the interview process, and we will make every effort to meet your needs. 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