Senior Product Manager

Bluecore, Inc.

58d

About The Position

In the minute it takes you to read this job description, Bluecore has launched over 100,000 individually personalized marketing campaigns for our retail ecommerce customers! Ship measurable wins for retailers by combining data analysis, rapid AI-assisted prototyping, and crisp product execution. Build marketer-facing experiences (copilots, automations, and workflow tooling) that turn first-party shopper data into activation across channels and connected tools. This role advances the foundations that make activation work: segments definition, discovery, and management (audiences); reliable exports and paid media integrations; and measurable outcomes. You’ll partner with platform teams to ensure segments and external activation workflows work cleanly across supported identifiers and adapt as the platform evolves.

Requirements

3+ years in product management or an adjacent technical, customer-facing role (solutions/implementation/sales engineering, technical account management, MarTech/marketing ops, developer relations) in SaaS/MarTech/ecommerce or AI/agentic domains and applications.
Experience shipping workflow automation or AI-assisted features is valuable; experience shipping segmentation and activation capabilities (CDP, ESP, marketing automation, paid media/data activation) is strongly preferred.
Experience with segmentation and activation (CDP, ESP, marketing automation, paid media activation, or data activation).
Hands-on data ability: SQL (BigQuery preferred) and Python scripting (pandas/NumPy) proficiency, designing and interpreting basic cohort analyses and A/B tests, and spotting instrumentation gaps.
Builder bias: demonstrable prototypes you personally assembled (LLM-assisted is fine) using low-code workflow automation tools, AI-powered app builders, a basic web stack, or notebooks.
Retail empathy: experience building for retailers or partnering with retail clients; clear articulation of shopper and retailer pain points.
Senior presence: confident with executive and client-facing conversations, crisp narrative, clean demos.

Nice To Haves

Working knowledge of identity concepts used in marketing activation (deterministic identifiers, mapping, consent constraints, match rates, integration contracts).
Familiarity with retrieval-augmented generation (RAG) over first-party data and function calling/tool use; comfortable defining what to retrieve, how to validate it, and what to do when retrieval fails.
Experience defining agent policy and evaluation (pre-launch tests on sample data, live A/B after launch, acceptance thresholds for accuracy, refusals, and latency; fallbacks and human-in-the-loop).
Experience integrating with or executing within paid media channels, e.g., Meta, Google Ads, Snapchat, TikTok, Pinterest, and DSPs (e.g., The Trade Desk, StackAdapt, etc.).
Experience with audience sync reliability patterns (retries, backfills, drift detection, failure modes, alerting, operational tooling).
Familiarity with file-based exports (CSV, JSON/JSONL) to SFTP and cloud storage, including scheduling, schema/versioning, validation, and reliability.
Experience with ads platform audience constraints (hashing requirements, size thresholds, refresh windows, offline conversion or event-based activation constraints).

Responsibilities

Ship agentic experiences for marketers. Define the tasks automations/agents can perform (for example: segment creation and validation, activation setup and QA, sync health checks, pause/resume safeguards), the tools they can use (MCPs, APIs, webhooks, file exports), and the approval flows. Measure impact on time to value and workflow efficiency; align with Engineering on safe execution and monitoring.
Make decisions with data. Pull your own numbers, not anecdotes. Leverage existing analytic tools or directly query the warehouse datasets, build quick analyses, and turn findings into product bets and success metrics.
Prototype before you spec. Use LLMs and tools (e.g., Cursor, n8n/Make, Replit, simple Python/JS, lightweight HTML) to stand up functional prototypes including audience builders and external activation setup flows that stakeholders and clients can click, not just read, and to better inform opportunity and scope.
Ship the platform pieces. Define objectives, use cases, and user requirements, and partner with Engineering on technical requirements to deliver iterative solutions on time.
Build and evolve activation foundations for third-party destinations (exports, paid media integrations). Define the patterns that make new channels and destinations easy to add over time (schemas/contracts, integration standards, observability, failure handling, and self-serve controls).
Instrument everything. Define KPIs, logging, and experiment design. Prove value with deltas, not vibes. Iterate and retire solutions based on measurable results.
Work the retail problem. Interview key internal stakeholders (Customer Success, Support, Sales, etc.) and customers, and review (or join, as necessary) client-facing meetings to empathize and translate retail pain points into precise jobs, constraints, and success criteria.
Lead in the room. Run client demos, handle objections, and translate technical detail into clear business outcomes for executives.
Operationalize AI safely. Specify evaluation, guardrails, and human-in-the-loop points for any AI-driven feature.
Write concise PRDs that are informed by prototypes which validate the approach.
Maintain a prioritized roadmap tied to ARR impact and customer value.
Partner cross-functionally with Engineering on scope, backlog, acceptance criteria, planning and delivery, gating, instrumentation, rollout, and post-launch adoption and performance monitoring.
Collaborate with L&D, Product Marketing, Customer Success, Support, and Sales on enablement, communication plans, and overall GTM motions.
Track competitive moves in marketing technology and agents; respond with concrete experiments.