Senior Product Manager

About KrisAtWorkKris@Work is the new generation AI-native Go-To-Market platform. It runs a B2B company's entire revenue engine, from finding the right accounts to closing and growing them. Revenue teams today juggle 25+ disconnected tools. Kris@Work replaces them with a single system, powered by its proprietary Kris Tell Intelligence™ layer and a multi-agent architecture that thinks, decides, and acts across the revenue cycle.About the RoleWe are looking for a AI-native Product Manager who blends product thinking, data science rigor, and comes with hands-on AI fluency. This role owns the end-to-end product lifecycle for AI-driven capabilities, ranging from predictive models to generative experiences, translating complex data & ML systems into scalable, customer-impacting products.The ideal candidate has prior experience in data science or ML, deeply understands model behavior, trade-offs, and evaluation, and can partner seamlessly with engineering, ML, and business stakeholders to ship intelligent, impactful, reliable & explainable AI products.What You’ll DoProduct Strategy & AI VisionDefine & own the AI product vision and roadmap, aligned with business outcomes and measurable customer valueIdentify high-leverage prediction and generation opportunities across user workflowsTranslate ambiguous business problems into clear AI-powered product hypothesesPrioritize features using data, expected lift, confidence intervals & ROI modelingPredictive AI OwnershipLead products powered by classification, regression, ranking, forecasting & anomaly detection modelsDefine prediction targets, labels, features, and success criteria in collaboration with AI/ ML engineering teamsDrive decisions around model refresh cadence, retraining triggers & drift detectionGenerative AI & LLM-Driven ExperiencesOwn GenAI use-cases including summarization, recommendations, copilots, content generation & reasoning workflowsDesign prompt strategies, retrieval-augmented generation (RAG), tool-calling & memory architecturesBalance determinism, creativity, latency & cost across GenAI experiencesDefine evaluation frameworks for GenAI quality (faithfulness, relevance, completeness, toxicity, hallucinations)Data-Driven Product ExecutionWrite clear, technical PRDs that include data inputs & outputs, model assumptions & constraints, evaluation metrics & guardrails, failure modes & fallback strategiesPartner deeply with ML engineers to review feature pipelines, model choices & offline vs online evaluation resultsMake informed trade-offs between rules, heuristics, classical ML & LLMsExperimentation & MeasurementDesign and own A/B tests, holdouts & phased rollouts for AI featuresMeasure incremental impact, not just accuracy (conversion lift, time saved, win-rate, retention)Build feedback loops to improve models using human-in-the-loop signalsDefine north-star & secondary metrics for AI successCross-Functional LeadershipAct as the product glue between ML, engineering, design, sales, customer success & leadershipTranslate technical AI concepts into clear business narratives for executivesPartner with go-to-market teams on AI positioning, trust messaging & explainabilityInfluence without authority, and set direction through clarity & dataAI Ethics, Trust & ReliabilityEnsure responsible AI practices around bias detection & mitigation, explainability & transparency as well as privacy, data governance & complianceDefine guardrails, confidence indicators, and override mechanisms for AI outputsProactively identify edge cases and failure scenarios before customers doWho You Are & What Makes You Qualified4+ years of Product Management experience, with significant ownership of data and/or AI-powered productsProven track record shipping predictive models and/or GenAI products to productionStrong understanding of supervised & unsupervised ML, feature engineering & data pipelines, model evaluation & experimentationPractical experience with LLMs (OpenAI/ Anthropic), RAG, embeddings, vector databases, prompt engineering & evaluationComfortable querying data (SQL) & reading Python notebooksAbility to reason about model performance, trade-offs & limitationsDeep intuition for user workflows & decision-making under uncertaintyAbility to connect model output to user action & business outcomeStrong prioritization skills grounded in data, not intuition aloneExperience building 0→1 AI products or scaling AI features across large user basesExperience with B2B SaaS, sales tech, CRM, or revenue intelligence platforms is preferredPrior hands-on experience as a Data Scientist, ML Engineer, or Applied Scientist is preferredExposure to real-time systems, streaming data, or low-latency inferenceExperience working with enterprise customers and regulated dataFamiliarity with AI infra tools (feature stores, model registries, monitoring)Excellent communication, stakeholder management & executive-level presentation & delivery skillsBachelor's degree in a technical, engineering, AI or related discipline; advanced degree is preferred