DataScience Show Podcast

Welcome to The DataScience Show, hosted by Mirko Peters — your daily source for everything data! Every weekday, Mirko delivers fresh insights into the exciting world of data science, artificial intelligence (AI), machine learning (ML), big data, and advanced analytics. Whether you’re new to the field or an experienced data professional, you’ll get expert interviews, real-world case studies, AI breakthroughs, tech trends, and practical career tips to keep you ahead of the curve. Mirko explores how data is reshaping industries like finance, healthcare, marketing, and technology, providing actionable knowledge you can use right away. Stay updated on the latest tools, methods, and career opportunities in the rapidly growing world of data science. If you’re passionate about data-driven innovation, AI-powered solutions, and unlocking the future of technology, The DataScience Show is your essential daily listen. Subscribe now and join Mirko Peters every weekday as he navigates the data revolu

DataScience Show Podcast is a business podcast hosted by Mirko Peters, with 139 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Mirko Peters hosts DataScience Show Podcast, a business show with 139 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

• 

  Episode #122

  From Proof to Product: The Executive Playbook for AI Product Management

  Jul 24, 20269 min

  Many enterprises stall at pilots because they treat machine learning as a project, not a product. This episode gives C-level leaders a practical playbook for building AI product management as a repeatable capability: a governance-backed lifecycle that aligns discovery, data and feature ownership, model delivery, product metrics, monetization, and cross-functional funding. Mirko walks listeners through role definitions, roadmaps, success metrics that tie to business KPIs, and organizational patterns that turn prototypes into durable business units. You’ll hear concrete trade-offs—speed vs. robustness, centralization vs. embedded teams—and real decisions leaders must make when balancing risk, cost, and time-to-value. The focus is operational: how to structure investment, define SLAs for data and features, embed product managers with engineering and domain teams, and measure causal impact. Practical, executive-level guidance for turning experimentation into predictable outcomes and measurable ROI. Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support . I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions. Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.

  ListenShow Notes
• 

  Episode #121

  Causal ROI: An Executive Playbook to Measure Real Business Impact of AI

  Jul 23, 20267 min

  Many AI initiatives report technical metrics but fail to prove business impact. This episode gives C-level leaders a practical, non-technical playbook for turning models into accountable investments by embedding causal measurement, controlled experimentation, and incremental rollout strategies into enterprise AI programs. Mirko walks listeners through real-world executive decisions: choosing causal vs. correlational evaluation, designing business-aligned A/B and quasi-experiments, instrumenting metrics and guardrails, and creating governance that insists on measurable outcomes before scale. The episode explains trade-offs between speed and statistical rigor, how to interpret heterogeneous treatment effects for different customer segments, and governance patterns that convert measurement into funding and de-risking mechanisms. Leaders will come away with concrete steps to require causal evidence, avoid common measurement traps, and structure organizations so product, analytics, and engineering jointly own ROI. Practical, tactical, and immediately actionable for executives responsible for AI investments. Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support . I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions. Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.

  ListenShow Notes
• 

  Episode #120

  Model Observability for Executives: Turning Alerts into Business Confidence

  Jul 22, 20268 min

  Executives routinely hear about model drift, data skew, and false positives, but struggle to understand which signals actually matter for the business. This episode walks senior leaders through a pragmatic, product-minded approach to model observability: how to pick the right metrics, translate technical telemetry into business KPIs, design escalation and runbooks, and fund operational controls that reduce risk and unlock value. The monologue explains concrete observability layers (data, prediction, feature, and outcome), examples of meaningful SLOs and alerts, and governance patterns that make monitoring auditable and actionable. Listeners will gain an executive checklist to hold teams accountable, a decision framework for investments in monitoring and tooling, and practical guidance on measuring ROI from reduced incidents, improved model uptime, and faster remediation. The tone is operational, strategic, and directly applicable for C-level leaders responsible for production AI. Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support . I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions. Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.

  ListenShow Notes
• 

  Episode #119

  Changing Behavior: A C-Level Playbook to Embed AI into Everyday Decisions

  Jul 21, 20268 min

  Many AI projects fail not for technical reasons but because organizations don’t change the decision architecture that surrounds models. This episode is a practical C-level monologue that lays out a playbook for embedding AI into everyday business decisions: aligning KPIs, redesigning incentives, shifting governance, and operationalizing feedback loops so models influence behavior reliably and ethically. Mirko frames the conversation around a senior guest profile—an experienced Chief Data & AI Officer at a global enterprise—and walks listeners through concrete patterns: choosing the right decision boundary, converting model outputs into operable signals, building measurement and accountability, and avoiding common behavioral failure modes. Executives will get prioritized tactics for short-term wins and an organizational roadmap that moves initiatives from pilot to repeatable impact. The emphasis is actionable: metrics to track, governance guardrails, cross-functional roles, and a stepwise rollout sequence that C-suite leaders can sponsor and audit. Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support . I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions. Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.

  ListenShow Notes
• 

  Episode #118

  Internal Pricing for AI: How Chargebacks and Product Pricing Turn Models into Sustainable Business Units

  Jul 20, 20269 min

  Many enterprise AI efforts stall not because models fail but because incentives, visibility, and funding are misaligned. This episode gives C-level leaders a pragmatic playbook for designing internal pricing and chargeback models that make AI costs transparent, encourage responsible consumption, and drive repeatable ROI. I introduce a senior AI product leader as the guest profile and walk through real-world design patterns: usage-based pricing for model inference, fixed subscription for data products, value-based pricing for decision automation, and hybrid approaches that balance experimentation with cost control. You’ll hear concrete governance rules, billing telemetry to collect, how to avoid perverse incentives, and sample KPIs that translate to executive budgets. The goal is practical: help leaders decide when to subsidize, when to charge, and how to use pricing as a lever to productize AI, prioritize scarce engineering capacity, and measure economic impact. Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support . I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions. Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.

  ListenShow Notes

Based on semantic analysis of episode topics and host coverage, this show is a strong guest fit for executives in:

Industry fit is computed by PitchCentric using vector embeddings of the show's episode catalog.