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Martin Habedank
Martin Habedank

Head of Product · Agentic & LLM Products · Platform & Data Products in B2B SaaS

Martin Habedank

Berlin, Germany

I lead product in B2B SaaS: I set the strategy, build the cross-functional teams that deliver it, and make them AI-native — because that is where the full potential sits. I also ship products hands-on, which is how I know what that shift actually asks of a team.

Looking for
Head of Product · Director of Product · VP Product in digital & AI-native B2B SaaS — platform & data products. Berlin or remote, open to a fractional CPO mandate.
contact@miskatonic-analytics.com

Miskatonic Analytics

Products I've built hands-on

Two products built end to end under my own label. Not a substitute for a team — the reason I can build teams that work AI-native, and know what that actually demands of them.

  • Miskatonic Analytics

    Dagon — Product-Discovery Engine

    Problem
    Product strategy too often runs on opinion and gut feel. Turning thousands of scattered market signals into evidence-based priorities is slow, manual, and rarely done with rigor.
    Role
    Creator and product lead under Miskatonic Analytics, the product label I founded. I defined the decisions it had to support and built it end to end on an AI-native stack.
    Outcome
    Turns thousands of unstructured market signals into ranked, evidence-backed product opportunities — so roadmap and feature bets rest on data instead of opinion.
    Scope
    End-to-end discovery: from raw market signals across customer personas and segments to prioritized opportunities and a decision-ready view.
    • Aggregates 2,000+ deduplicated findings into Jobs/Pains/Gains per customer persona.
    • Ranks opportunities and maps them by Importance × Satisfaction for clear, defensible prioritization.
    • An agentic LLM pipeline end to end — the kind of build I now expect a team to be equipped for.
    • Agentic & LLM
    • Product discovery
    • Opportunity scoring
    • AI-native delivery
  • Miskatonic Analytics

    RITE — Racing Intelligence & Telemetry Engine

    Problem
    GT racing teams run race weekends on Excel and WhatsApp screenshots. Setup, run, and lap data is scattered, lost between sessions, and almost impossible to compare — so hard-won learning evaporates.
    Role
    Creator and product lead, also under Miskatonic Analytics. I own the problem, the product definition and the roadmap, and built it end to end on an AI-native stack.
    Outcome
    One structured system where engineers and drivers capture and compare an entire race weekend instead of scattered spreadsheets — with AI-assisted coaching insights as the next step.
    Scope
    From problem discovery to a shipped product covering the full race-weekend flow — events, sessions, runs, and lap/sector analysis — aimed at professional GT teams.
    • The whole race weekend in one place — events, sessions, cars, drivers, runs, laps — instead of scattered spreadsheets and chat screenshots.
    • Per-lap and per-sector deltas so teams can actually compare and improve, not just store data.
    • Hands-on proof that a product leader can stay fluent in an AI-native stack — and lead teams that work in one.
    • Data platform
    • B2B SaaS
    • Product strategy
    • AI-native delivery

Who I am

About

I am a product leader who works across strategy and delivery — setting product direction, reshaping how teams ship, and staying close enough to delivery to make sure it lands. Twice a member of the leadership team reporting directly to the CEO.

At CarTelSol I turned a project-based delivery shop into a scalable B2B platform organization: restructuring into cross-functional teams, cutting hardware iteration from over a year to under two months, and moving from years without releases to continuous delivery every two weeks. The platforms my teams shipped serve a global automotive OEM and large fleet operators — from low-code data-science tooling for engineers to an internal AI incubator whose LLM knowledge platform cut compliance research from a sprint to hours.

Under my own label, Miskatonic Analytics, I build products hands-on — Dagon, a product-discovery engine, and RITE, a race-weekend data platform for GT racing teams. That is not an alternative to leading a team; it is what keeps me fluent enough to build good ones. Teams reach their full potential when they work AI-native, and I would rather have done that work myself than ask it of people from the outside.

German
(native)
English
(fluent)

How I lead

Leadership

I build teams and give them clear outcomes and the autonomy to reach them. I am comfortable driving change through an organization as well as through the product itself.

  • Building AI-native teams

    My job is to assemble the team, not to be it. What has changed is what a good team looks like: AI-native ways of working are where the full potential sits, and a team only gets there if the change is real rather than announced. I have restructured a component-owned organization into cross-functional teams with dedicated ownership, and I stay hands-on in the tooling myself — so what I ask of a team is something I have done, not something I have read about.

  • Management by objectives & situational leadership

    I set clear objectives and give teams the autonomy to meet them, adapting my involvement to each team and individual — more direction where it helps, more space where it does not. I rolled out OKRs across engineering to make objectives explicit and shared.

  • Change management

    I led a roughly six-month transformation of a project-based delivery org into a scalable platform organization, navigating resistance by demonstrating measurable velocity gains rather than mandating process. Faster, visible results made the change self-reinforcing.

  • Strategic prioritization

    I invest in platform before features. Putting the reusable foundation first — shared ingestion, tooling, and infrastructure — is what cut hardware iteration cycles from over twelve months to under two and turned multi-year release droughts into continuous delivery.

  • Stakeholder management

    I work effectively across enterprise engineering teams and middle management at a global OEM customer, aligning technical and business stakeholders around shared outcomes and translating between them so decisions actually stick.

  • Company as Code

    Engineering organizations drift without shared standards, repeatable scaffolding, and enforceable quality gates — quality and velocity become person-dependent. I encode the operating model instead: org-wide engineering standards, templates, and automated quality gates baked into the delivery workflow, so good practice is the default rather than tribal knowledge.

Track record

Experience

  1. Jul 2025 – present

    Head of Product · CarTelSol GmbH

    Member of the leadership team (1 of 4), reporting to the CEO/owner — 3 direct reports, ~20 across product & engineering.

    • Redesigned a component-owned organization into 4 cross-functional product teams (Hardware, Firmware, Application, UX), each led by a dedicated Product Owner — owning the operating model end to end.
    • Drove a 6-month transformation that cut hardware iteration from 12+ months to under 2, and release cadence from 3+ years without releases to continuous delivery every 2 weeks.
    • Won executive and engineering buy-in for foundational platform investment over short-term features.
    • Introduced OKRs across the engineering organization, measurably improving alignment and team satisfaction.
    • Platform org
    • Change management
    • OKRs
    • B2B SaaS

  2. Jun 2022 – Jul 2025

    Product Owner / Product Manager · CarTelSol GmbH

    Owned product strategy for the data and telemetry platforms behind an enterprise customer base — Volkswagen and large fleet operators.

    • Delivered a fleet-utilization platform tracking 8,000+ test vehicles (a multi-billion-€ asset pool), enabling a ~50% reduction in fleet investment by surfacing underutilized vehicles.
    • Set strategy and shipped a low-code data-science platform letting engineers analyze vehicle data without programming, cutting iteration from 2 months to under 1 week.
    • Secured government innovation funding off a remote ECU-flashing proof of concept, establishing a recurring innovation-funding function.
    • Co-founded an internal AI incubator; its LLM-powered knowledge platform cut compliance research from ~1 sprint to hours.
    • Data platforms
    • LLM products
    • Enterprise B2B
    • Low-code tooling

  3. 2019–2021

    Chief Marketing Officer · Spiele-Palast GmbH

    Leadership team, reporting to the CEO — owned a €600k marketing budget, built & led a team of 4, drove ~60% of €5–6M annual revenue.

    • Owned marketing P&L and channel strategy across a portfolio of mobile titles; hired and built the marketing team from scratch.
    • Cut marketing-investment payback from ~18 to 6–8 months through cohort and attribution modeling — enabling fast, confident go/no-go decisions on spend.
    • Built the A/B-testing and forecasting engine behind sustained organic and paid growth and seasonal revenue planning.
    • P&L ownership
    • Growth
    • Attribution
    • Team building

  4. 2015–2018

    Marketing Analyst · Spiele-Palast GmbH

    • Established the company's marketing-analytics practice and drove App Store Optimization that measurably grew organic downloads — the foundation that earned the CMO mandate.
    • Analytics
    • ASO

  5. 2012–2015

    Performance Engineer (Telemetry) & Race Operations · Mücke Motorsport GmbH

    • Ran race telemetry and driver performance analysis, feeding real-time insight back to drivers during sessions.
    • Owned race-weekend operations — logistics, scheduling, parts/cost settlement, and warehouse digitization that delivered seven-figure cost savings.
    • Motorsport
    • Data analysis
    • Operations

Education

  • B.Sc. Data Science & Business Analytics · Digital Business University of Applied Sciences · 2021 – present · in progress alongside work · grade average 1.2 (sehr gut)
  • B.Sc. Transportation Systems · TU Berlin · 2008–2013

What I bring

Skills

Leadership & Management

  • Organizational design & scaling
  • Product strategy & roadmapping
  • Change & transformation management
  • OKRs / Management by Objectives
  • Executive & enterprise stakeholder management
  • Data-driven / KPI-led decision-making

Platforms & Data

  • LLMs & agentic / RAG systems
  • Cloud platforms (AWS, Google Cloud)
  • Event-driven & streaming (Kafka / MQTT)
  • Time-series & vector data stores
  • APIs & microservices

Domains

  • Agentic & LLM products
  • Platform & data products
  • B2B SaaS
  • API platforms
  • IoT / Telemetry
  • Automotive
  • Fleet management

Beyond work

Community

  • Co-Founder and Co-Chair, Berlin Chapter

    2024–present

    German Data Science Society

    Co-founded and co-lead the Berlin chapter — building the local data-science community through events and programming.

  • Co-Maintainer

    2024–2025

    Ludwig (open-source deep-learning framework)

    Co-maintained an open-source declarative deep-learning framework, supporting contributors and the community.

  • Co-Founder

    2013–2019

    „Kinderträume" e.V. (German-Russian student initiative)

    Co-founded a student initiative supporting children.

Contact

Let's talk

The fastest way to reach me is email — happy to talk through any of the above in more depth.

contact@miskatonic-analytics.comLinkedInGitHub