About KrisAtWork:

KrisAtWork is an AI work companion designed for go-to-market functions to drive higher revenue and enhance efficiency. It offers real-time intelligence and step-by-step guidance, all through a single, centralized window to improve business productivity and outcomes. At KrisAtWork, we believe everything meaningful starts with people - those who build with us, and those we build for - united by a shared ambition to make work smarter.

About the Role:

We are looking for a highly analytical, 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 Youll 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.