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Job Description
About KrisAtWork
Kris@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 Role
We 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 Do
Product Strategy & AI Vision
- Define & own the AI product vision and roadmap, aligned with business outcomes and measurable customer value
- Identify high-leverage prediction and generation opportunities across user workflows
- Translate ambiguous business problems into clear AI-powered product hypotheses
- Prioritize features using data, expected lift, confidence intervals & ROI modeling
Predictive AI Ownership
- Lead products powered by classification, regression, ranking, forecasting & anomaly detection models
- Define prediction targets, labels, features, and success criteria in collaboration with AI/ ML engineering teams
- Drive decisions around model refresh cadence, retraining triggers & drift detection
Generative AI & LLM-Driven Experiences
- Own GenAI use-cases including summarization, recommendations, copilots, content generation & reasoning workflows
- Design prompt strategies, retrieval-augmented generation (RAG), tool-calling & memory architectures
- Balance determinism, creativity, latency & cost across GenAI experiences
- Define evaluation frameworks for GenAI quality (faithfulness, relevance, completeness, toxicity, hallucinations)
Data-Driven Product Execution
- Write clear, technical PRDs that include data inputs & outputs, model assumptions & constraints, evaluation metrics & guardrails, failure modes & fallback strategies
- Partner deeply with ML engineers to review feature pipelines, model choices & offline vs online evaluation results
- Make informed trade-offs between rules, heuristics, classical ML & LLMs
Experimentation & Measurement
- Design and own A/B tests, holdouts & phased rollouts for AI features
- Measure incremental impact, not just accuracy (conversion lift, time saved, win-rate, retention)
- Build feedback loops to improve models using human-in-the-loop signals
- Define north-star & secondary metrics for AI success
Cross-Functional Leadership
- Act as the product glue between ML, engineering, design, sales, customer success & leadership
- Translate technical AI concepts into clear business narratives for executives
- Partner with go-to-market teams on AI positioning, trust messaging & explainability
- Influence without authority, and set direction through clarity & data
AI Ethics, Trust & Reliability
- Ensure responsible AI practices around bias detection & mitigation, explainability & transparency as well as privacy, data governance & compliance
- Define guardrails, confidence indicators, and override mechanisms for AI outputs
- Proactively identify edge cases and failure scenarios before customers do
Who You Are & What Makes You Qualified
- 4+ years of Product Management experience, with significant ownership of data and/or AI-powered products
- Proven track record shipping predictive models and/or GenAI products to production
- Strong understanding of supervised & unsupervised ML, feature engineering & data pipelines, model evaluation & experimentation
- Practical experience with LLMs (OpenAI/ Anthropic), RAG, embeddings, vector databases, prompt engineering & evaluation
- Comfortable querying data (SQL) & reading Python notebooks
- Ability to reason about model performance, trade-offs & limitations
- Deep intuition for user workflows & decision-making under uncertainty
- Ability to connect model output to user action & business outcome
- Strong prioritization skills grounded in data, not intuition alone
- Experience building 0→1 AI products or scaling AI features across large user bases
- Experience with B2B SaaS, sales tech, CRM, or revenue intelligence platforms is preferred
- Prior hands-on experience as a Data Scientist, ML Engineer, or Applied Scientist is preferred
- Exposure to real-time systems, streaming data, or low-latency inference
- Experience working with enterprise customers and regulated data
- Familiarity with AI infra tools (feature stores, model registries, monitoring)
- Excellent communication, stakeholder management & executive-level presentation & delivery skills
- Bachelor's degree in a technical, engineering, AI or related discipline; advanced degree is preferred
More Info
Key Skills
embeddings
data pipelines
LLMs
generative AI
AI product vision
vector databases
Anthropic
prompt engineering
predictive models
model evaluation
RAG
feature engineering
OpenAI
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