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    About

    I came into product through operations and growth.

    I’m a Berlin-based Product Manager with nearly 3 years of product experience in B2B SaaS and applied AI. I have worked across AI-assisted operational workflows, product analytics, AI search, ranking and multi-stakeholder workflows.

    I entered product through business operations and growth marketing. Operations taught me to understand the complete workflow around a customer problem: supply, constraints, hand-offs and commercial reality. Growth taught me to connect decisions with measurable user behaviour. Product became the place where those perspectives could be combined with technology, discovery and execution.

    That background is why I tend to work from the operating problem outward rather than from the feature inward.

    Operations → Growth → Product

    2017–2019

    Operations

    Internship → Business Operations Manager

    Working in business operations taught me to see a problem as part of a larger system: customer needs, inventory, pricing, dependencies and operational trade-offs.

    FocusWorkflows · Customers · Inventory · Operational constraints · Commercial trade-offs

    2022–2023

    Growth

    Growth marketing added a measurement layer: understand behaviour, form hypotheses, test messaging and channels, and adjust based on evidence rather than intuition alone.

    FocusUser behaviour · Acquisition · Messaging · Measurement · Experimentation · Commercial communication

    2024–Present

    Product

    Product brought those perspectives together. I now work across discovery, technical prototyping, stakeholder alignment, delivery and post-launch iteration, particularly in B2B SaaS and applied-AI products.

    FocusDiscovery · Prioritisation · Technical collaboration · Product decisions · Delivery · Iteration

    How I work

    01

    Understand the operating context first

    Before discussing a feature, I want to understand the user, the existing process, the dependencies, and what happens before and after the interaction.

    02

    Separate evidence from assumptions

    Customer input, product data, technical constraints, and stakeholder beliefs are different types of information. I try to make those distinctions explicit before committing to a direction.

    03

    Make product behaviour concrete early

    Prototypes, SQL queries, realistic examples, and edge cases help expose weak assumptions before they become expensive implementation decisions.

    04

    Treat launch as another learning point

    Adoption, quality, feedback and operational behaviour matter more than treating release as the finish line.

    What I’m looking for

    I’m targeting Product Manager and AI Product Manager roles in Germany, with Berlin as my primary location. The strongest fit is B2B SaaS involving applied AI, operational workflows, analytics, search, marketplaces or other multi-stakeholder products.

    Want the evidence behind the story?

    The Profile page covers my experience and credentials. Selected Work goes deeper into the product decisions and outcomes behind the products I’ve worked on.