Die ganze Ausschreibung von Jobtailor
Das ist der Job
Able to create quick prototypes and test ideas without over-engineering.
Darum lohnt es sich
Responsibilities • Define the vision, strategy, and roadmap for AI/ML product features (JobHunt Engine). • Take full ownership of the product lifecycle from hypothesis to scaled impact, focusing on business results, not just model performance. • Translate business problems into ML hypotheses and solutions. • Work side-by-side with ML engineers and data scientists to define data requirements, evaluation frameworks (evals, RAG, agents), model monitoring, and delivery processes. • Design and execute rapid, pragmatic validation cycles. • Formulate clear hypotheses (Problem → Mechanism → Impact → Metric) and choose the right validation method (A/B test, shadow model, phased rollout). • Apply critical thinking to decompose complex, ambiguous problems. • Collaborate closely with Engineering, Data Science, and business teams. • Communicate complex ML concepts clearly and align stakeholders on goals, trade-offs, and progress.
Requirements • Strong understanding of ML/LLM fundamentals (NLP, recommendation systems, etc.). • Hands-on experience building and scaling AI-powered features (matching, ranking, personalization). • Practical knowledge of modern AI/ML concepts: evaluation frameworks, RAG, agents, model monitoring. • Ability to define data pipelines, metrics, and work processes with ML engineering teams. • Nice to have: hands-on experience in a Data Science, Data Analyst, or ML Engineer role. • 5+ years of experience in a Product Manager role, preferably in a data-intensive or ML-driven domain (HRTech experience is a strong plus). • Proven ability to formulate and rigorously test product/ML hypotheses using statistical methods (A/B testing, significance, confidence intervals). • Ability to reason about probability, causality, and data limitations to make informed decisions. • Entrepreneurial & Hands-on: "Let's build it" attitude.
Comfortable with "building with sticks and glue" to learn fast. • Outcome-Oriented: Owns the business result, not just the AI model. Pragmatic and willing to simplify or kill features that don't drive impact. • Thrives in Ambiguity: Can navigate uncertainty, contradictory model results, and noisy data.
Structured thinker who can bring clarity to complex situations. • Fluent English and Russian. Excellent ability to communicate with technical (Engineers, Data Scientists) and non-technical stakeholders.
Bereit?
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