דרושים Senior Product Manager – Analysis Physical Layer בתל אביב

Exodigo is the leading underground mapping solution for non-intrusive discovery. Our platforms combine multi-sensor fusion, 3D imaging, and AI technologies to create complete, accurate underground maps that enable confident decision-making for customers across the built world. We transform the project lifecycle for our customers, who include key community stakeholders in the utilities, transportation and government sectors.

We are experiencing sky-rocketing growth and closed a historically large $96M Series B round in July of 2025.

Job description:

As the Product Manager for Analysis (Physical Layer), you will own the product that transforms raw sensor indications into reliable, map-ready intelligence.

This product sits at the heart of Exodigo’s workflow: it ensures that field data becomes a consistent, high-quality Physical Layer that mapping, algorithms, and customer-facing products depend on.

You will define the product vision, quality standards, and data handover interfaces; work closely with domain experts, Signal Leads, and R&D; and drive the roadmap that improves accuracy, automation, and operational efficiency.

This role is highly cross-functional and ideal for someone who thrives at the intersection of data, geospatial workflows, machine learning, and field operations.

**What You Will Do

**Product Ownership & Strategy

• Own the end-to-end roadmap for the Analysis product, prioritizing features and algorithm work based on business impact and technical feasibility.
• Define what the Physical Layer must deliver to downstream mapping and platform teams, including structure, attributes, and confidence scoring.
• Translate domain workflows (field → sensor indications → analysis outputs) into clear product requirements and engineering stories.

Quality, Validation & Data Standards

• Set quality gates and validation rules, including acceptance criteria, numeric thresholds, and the balance between automated vs. manual checks.
• Define the Analysis → Mapping handover package: required files, formats, CRS/versioning, confidence metrics, PQA summary, and known-issues documentation.
• Establish and maintain data standards that ensure consistency and reliability at scale.

Collaboration & Execution

• Work closely with Signal Leads and R&D to define epics, experiments, and measurable success criteria (e.g., precision/recall targets).
• Partner with FieldOps and Mapping teams to collect feedback, understand workflows, and iteratively improve the product.
• Support pilots, new capabilities, and production rollouts to ensure smooth adoption.

KPIs & Operational Excellence

• Define and track key product metrics (e.g., Automation Yield, Re-Analysis Rate, Acres per Day) and report progress to product and operations leadership.
• Identify error modes, edge cases, and remediation paths, including how manual corrections feed back into model improvement.

Requirements:

Requirements

• 6+ years of relevant experience in product management, software engineering, technical program management, or a similar role.
• 3+ years of experience as a Product Manager for B2B SaaS, data platforms, or analytics products.
• Hands-on experience with sensor data, geospatial data, or signal-processing workflows (experience with GPR/EM/LiDAR is a strong plus).
• Proven ability to write clear, detailed product specifications and translate domain requirements into engineering deliverables.
• Experience setting quality gates, defining validation pipelines, and enabling high-scale product quality.
• Strong collaboration skills across R&D, operations, and domain experts.
• Solid understanding of data formats, APIs, versioning practices, and production reliability.
• Strong analytical skills and comfort defining and interpreting KPIs.
• Excellent communication skills with technical and non-technical stakeholders.
• Familiarity with modern product tools (Jira, Aha!, Figma or similar).

Nice to Have

• Background in geospatial systems (GIS), CAD/DWG workflows, BIM, or digital twin concepts.
• Experience with machine-learning pipelines and feedback loops from manual corrections to model retraining.
• Bachelor’s degree in engineering, computer science, geospatial sciences, or a related field.
• Experience with infrastructure, utilities, or construction environments.