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‎_layouts/podcast.html‎

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<div class="container">
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<div class="row">
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<div class="col-lg-8 mx-auto content">
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{% assign season_episodes = site.podcast | where: "season", page.season | sort: "episode" %}
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{% assign prev_episode = nil %}
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{% assign next_episode = nil %}
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{% assign prev_candidate = nil %}
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{% assign capture_next = false %}
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{% for episode in season_episodes %}
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{% if capture_next %}
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{% assign next_episode = episode %}
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{% break %}
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{% endif %}
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{% if episode.url == page.url %}
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{% assign prev_episode = prev_candidate %}
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{% assign capture_next = true %}
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{% else %}
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{% assign prev_candidate = episode %}
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{% endif %}
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{% endfor %}
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<div class="podcast-head-section">
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<div class="podcast-header">
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</div>
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{% endif %}
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<!-- Episode Navigation -->
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{% if prev_episode or next_episode %}
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<nav class="podcast-episode-nav" aria-label="Podcast episode navigation">
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{% if prev_episode %}
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<a class="episode-nav-link episode-nav-link--prev" href="{{ prev_episode.url }}">
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<span class="episode-nav-direction">← Previous episode</span>
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<span class="episode-nav-title">{{ prev_episode.title }}</span>
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{% if prev_episode.season and prev_episode.episode %}
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<span class="episode-nav-meta">Season {{ prev_episode.season }} · Episode {{ prev_episode.episode }}</span>
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{% endif %}
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</a>
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{% endif %}
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{% if next_episode %}
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<a class="episode-nav-link episode-nav-link--next" href="{{ next_episode.url }}">
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<span class="episode-nav-direction">Next episode →</span>
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<span class="episode-nav-title">{{ next_episode.title }}</span>
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{% if next_episode.season and next_episode.episode %}
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<span class="episode-nav-meta">Season {{ next_episode.season }} · Episode {{ next_episode.episode }}</span>
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{% endif %}
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</a>
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{% endif %}
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</nav>
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{% endif %}
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<!-- Newsletter Section -->
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<!-- Newsletter Section -->
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<div class="main-cta">
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<p class="newsletter-main-text">
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Join 130,000+ data professionals and get weekly updates on new podcast episodes, upcoming events, free courses, and more.

‎_podcast/s01e01-roles.md‎

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---
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title: "Data Team Roles Explained"
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title: "Data Team Roles Explained: Skills, Responsibilities, and How Teams Ship ML Products"
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short: "Roles in a Data Team"
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guests: [alexeygrigorev]
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‎_podcast/s01e02-processes.md‎

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---
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title: "CRISP-DM Methodology: Complete Guide to Data Science Project Process"
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title: "CRISP-DM Methodology for Data Science Projects: Business Understanding, Data Preparation, Modeling, Evaluation & Deployment"
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short: "Processes in a Data Science Project"
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guests: [alexeygrigorev]
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‎_podcast/s01e03-building-ds-team.md‎

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---
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title: Build MLOps-Ready Data Teams & White-Box Dynamic Pricing for Startups
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title: 'How to Build and Scale ML Teams: Hiring, MLOps & Product-Driven AI for Startups'
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short: Building a Data Science Team
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guests:
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- dattran
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anchor: https://anchor.fm/datatalksclub/episodes/Building-a-Data-Science-Team---Dat-Tran-enlmef
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spotify: https://open.spotify.com/episode/0daFpY1z2J4Uop1XdMNsnY
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apple: https://podcasts.apple.com/us/podcast/building-a-data-science-team-dat-tran/id1541710331?i=1000502061864
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intro: How do you build an MLOps‑ready data team while shipping a transparent, white‑box
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dynamic pricing product for a startup? In this episode Dat Tran—Partner & CTO at
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DATANOMIQ, former Head of Data at idealo, and co‑founder of Priceloop—walks through
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the practical tradeoffs of moving from prototypes to production ML. <br><br> Dat
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traces his path from economics and early coding to production ML at Accenture, Axel
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Springer and idealo, and explains the “day‑two” operations mindset required for
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model maintenance and MLOps. We cover building a Head of Data role, hiring strategies
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for early‑stage startups (T‑shaped generalists first, specialists later), and how
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to align hiring with product uncertainty. Dat also outlines Priceloop’s white‑box
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AI approach to dynamic pricing—human‑centric systems that augment pricing managers
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rather than replace them—and the role of open research and open‑source in competitive
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advantage. <br><br> Tune in for concrete guidance on team composition (ML engineers,
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data engineers, PMs), take‑home assessments, project prioritization, retention,
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and educating leadership on realistic AI capabilities. Listeners will leave with
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actionable steps to create production‑grade MLOps teams and build transparent dynamic
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pricing solutions.
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intro: 'How do you build and scale an ML team that delivers product-driven AI without
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getting bogged down by tech debt or false promises? In this episode Dat Tran — Partner
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& CTO at DATANOMIQ and former AI lead at Axel Springer, idealo, and Pivotal — walks
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through practical strategies for hiring, MLOps, and shaping data teams for startups.
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<br><br> Dat draws on a decade of production ML experience to unpack the MLOps mindset
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(day‑two operations, model maintenance), how to hire early (T‑shaped generalists,
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take‑home assessments, key hiring signals), and when to shift to specialists as
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you scale. He also explains product-centric practices: aligning hiring to prototype
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vs. MVP needs, prioritizing impact over technical perfection, and building human‑centric
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AI (augmenting pricing managers at Priceloop). Other topics include open research
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and open source as strategic advantages, bootstrapping data capabilities, retention
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through autonomy and interesting work, and educating leadership about realistic
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AI expectations. <br><br> Listen for actionable guidance on building ML teams, hiring
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machine learning engineers, and implementing MLOps and product-driven AI in early‑stage
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startups.'
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transcript:
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- header: Podcast Introduction
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- header: Guest Overview & Career Snapshot
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sec: 3650
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time: '60:50'
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who: Alexey
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description: Build MLOps-ready data teams and white-box dynamic pricing for startups—hiring
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plan, production ML practices, and human-led pricing to boost revenue.
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description: 'Master building ML teams: hiring playbooks, MLOps day-two ops, and product-driven
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AI for startups—scale with T-shaped engineers, ship robust models.'
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---
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## Books

‎_podcast/s01e04-standing-out-as-a-data-scientist.md‎

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---
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title: 'Data Scientist Hiring Guide: Resume, Portfolio, Interview & Salary Strategies'
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title: 'Land Data Scientist Roles: Resumes, Portfolios, Interviews & Recruiter Workflow'
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short: Standing out as a Data Scientist
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guests:
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- lukewhipps
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sec: 4226
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time: '1:10:26'
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who: Alexey
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description: Master data scientist resume, portfolio & interview tactics to get interviews,
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prove business impact and negotiate higher salary with recruiter tips.
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intro: 'How do you get hired — or hire — for a data scientist role when expectations,
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titles, and hiring processes differ so widely? In this episode Luke Whipps, co‑founder
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of Neural.AI and host of the AI Game Changer podcast, draws on 8+ years recruiting
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data, analytics and AI talent to answer that question. We walk through a six‑stage
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recruitment workflow from role definition to offer, and tackle practical hiring
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and job‑seeking topics: writing a data scientist resume and CV (format, length,
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audience fit), building a portfolio that links tech stack to concrete projects,
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and shaping a career narrative that demonstrates real business impact. Luke breaks
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down shortlist and interview preparation, candidate funnel strategies, junior hiring
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tips, targeted outreach (email, LinkedIn) and focus strategies for approaching fewer
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companies. He also covers salary signals and negotiation, transitioning from academia
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or web development, job‑hopping concerns, and how to align job titles without misrepresenting
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experience. Listen to gain actionable interview preparation, portfolio and salary
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negotiation strategies for data science hiring and career progression.'
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description: Master data scientist resumes, portfolios & interviews—insider recruiter
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workflow, CV tips, portfolio impact, negotiation and outreach to land roles faster.
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intro: How do you actually land a data scientist role — from a resume that passes
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screening to a portfolio that wins interviews and an offer that closes? In this
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episode Luke Whipps, co-founder of Neural.AI and host of the AI Game Changer podcast
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with 8+ years recruiting experience, walks through the recruiter workflow and practical
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steps data scientists can use to improve hiring outcomes. <br><br> We cover Luke’s
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six‑stage recruitment process (role definition to close), how to define data scientist
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roles across companies, and recruiter expectations for CV design, information hierarchy,
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and industry/use‑case alignment. Learn how to structure portfolios to link tech
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stack to concrete projects, craft a clear career narrative that demonstrates business
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impact, and prepare for interviews and negotiations. Junior candidates will get
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guidance on choosing an industry and showing purpose; academics learn how to productize
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research for industry. You’ll also hear tactical advice on tailored applications,
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LinkedIn outreach, candidate funnel sizes, salary signals, job‑title alignment,
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and acceptable tenure patterns. <br><br> Listen to gain actionable tips for resumes,
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portfolios, interviews, and working effectively with recruiters to increase your
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chances of landing a data scientist role.
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---

‎_podcast/s01e05-mentoring.md‎

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---
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title: How to Find a Mentor, Start Paid Mentoring & Grow as a Technical Leader
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title: 'How to Find a Mentor and Become One: Mentoring Strategies for Tech Careers'
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short: Mentoring
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guests:
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- rahuljain
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image: images/podcast/s01e05-mentoring.jpg
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description: Discover how to find a mentor, start paid mentoring and grow as a technical
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leader - cold outreach scripts, pricing, session agendas, boundaries & career growth.
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description: 'Discover practical mentoring strategies for tech careers: find mentors,
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master cold outreach, run effective sessions, start paid mentorship & boost leadership.'
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keywords: mentoring, career development, tech mentorship, finding a mentor, becoming
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a mentor, imposter syndrome, tech leadership, career advice, professional development,
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data engineering
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anchor: https://anchor.fm/datatalksclub/episodes/Mentoring---Rahul-Jain-eo7cmu
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spotify: TODO
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apple: TODO
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intro: How do you find a mentor, turn mentoring into paid work, and grow as a technical
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leader? In this episode Rahul Jain—Senior Solutions Engineer at Snowflake with 15+
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years in data and AI—walks through practical steps for mentorship and leadership
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development grounded in his career from mining engineering to data engineering and
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management. We define mentoring (purpose, types, sponsorship), explore ways to find
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a mentor via networks, cold outreach, and platforms, and share cold outreach best
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practices like specificity, background, and follow‑up. Rahul outlines how to prepare
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effective mentoring sessions (goals, agendas), compares one‑off advice to long‑term
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relationships, and covers benefits of being a mentor including listening and pattern
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recognition. Listeners will also learn people‑skills essentials (empathy, avoiding
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the “advice monster”), balancing technical work with leadership, addressing common
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mentee challenges like imposter syndrome, and when to use external coaches. Practical
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guidance on setting boundaries, starting paid mentorship, pricing and accountability,
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building reciprocal relationships, and maintaining development plans rounds out
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the episode—ideal for engineers and aspiring technical leaders seeking actionable
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mentoring and career growth strategies.
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intro: 'Struggling to find a mentor — or wondering how to become one — in a fast-moving
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tech career? In this episode Rahul Jain, a senior solutions engineer and data/AI
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leader with 15+ years driving enterprise data transformations and a career arc from
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mining engineering to data engineering and leadership, walks through practical mentoring
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strategies for tech professionals. We define mentoring (purpose, scope, types),
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explore early models like Thoughtworks’ sponsorship, and show how to find mentors
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through networks, platforms, and cold outreach — with concrete outreach best practices:
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specificity, background, and follow‑up. Rahul covers preparing mentoring sessions
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(goals, agendas), mentoring formats (one‑off advice vs long‑term relationships),
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and how to start as a mentor using simple first steps and platforms. Topics include
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benefits of mentoring, transferable workplace guidance, developing people skills
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(empathy, listening), balancing technical work and leadership, tackling imposter
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syndrome, coaching vs managing, setting boundaries and paid mentorship, and maintaining
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development plans. Listen to gain actionable steps, templates, and mindset shifts
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to both secure meaningful mentorship and build a sustainable mentoring practice
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in your tech career.'
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Today we're discussing mentoring with [Rahul Jain](/people/rahuljain.html), a technical leader with about 20 years of experience building and running software products. He currently leads the Business Intelligence and Data Engineering units at Omio, a ticket-booking company, and mentors engineers and managers through The Mentoring Club.

‎_podcast/s02e01-writing.md‎

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title: 'Master Weekly Developer Blogging: Outline-First Writing, Tools & Technical
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Portfolio Tips'
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short: The Importance of Writing in a Tech Career
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title: 'Master Technical Writing: 7-Day Workflow to Accelerate Your Data Science Career'
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short: 'Master Technical Writing: 7-Day Workflow to Accelerate Your Data Science Career'
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guests:
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- eugeneyan
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image: images/podcast/s02e01-writing.jpg
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description: Master developer blogging with an outline-first workflow, weekly writing
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cadence and technical portfolio tips—tools, titles, repo READMEs to get hired.
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description: 'Master technical writing for data science with a practical 7-day workflow:
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outline-first cadence, portfolio tips, docs & distribution to accelerate your career.'
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keywords: technical writing, data science career, ML engineer writing, documentation
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skills, technical communication, data science blog, career growth, writing process,
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Amazon data scientist, Eugene Yan, technical documentation, data science portfolio,
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anchor: https://anchor.fm/datatalksclub/episodes/The-Importance-of-Writing-in-a-Tech-Career---Eugene-Yan-ep17du
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spotify: TODO
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apple: TODO
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intro: How do you publish developer-focused posts weekly without sacrificing depth
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or your day job? In this episode Eugene Yan — an Applied Scientist at Amazon who
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builds pragmatic ML systems and previously led data science teams at Lazada and
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uCare.ai — walks through a practical, outline-first approach to sustainable developer
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blogging and building a technical portfolio. <br><br> We cover Eugene’s career pivot
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into public writing, motivations for sharing knowledge, and how to target readers,
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peers, and future teammates. Listen for his 7-day weekly writing cadence, time-budgeting
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advice (including tips to avoid over-editing), and the outline-first method for
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filtering ideas and rewriting from memory. He also breaks down idea sourcing, title
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and length decisions, getting started tactics, and recommended blogging tools (Medium,
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Substack, WordPress, Jekyll/GitHub Pages). You’ll hear routines for morning reps
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and weekend deep work, distribution strategies via Twitter and LinkedIn, and how
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to translate work artifacts into press-release-style docs, decision logs, and clearer
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technical documentation. Plus, actionable portfolio best practices—clear README,
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quick-start guide, and repo tours—to make your code and writing discoverable. <br><br>
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Tune in to learn a repeatable workflow for weekly developer blogging, technical
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writing, and portfolio building that scales with your career.
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intro: How can technical writing accelerate your data science career in just one week?
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In this episode Eugene Yan — an Applied Scientist at Amazon who previously led data
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science teams at Lazada and uCare.ai and writes about ML in production and career
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growth — walks through a practical, repeatable 7-day workflow for technical writing
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tailored to data scientists. <br><br> We cover Eugene’s career transition and first
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public writing, motivations for sharing work, and how to target readers (peers,
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future teammates, and hiring managers). He frames writing as a product with a weekly
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shipping cadence, explains the outline-first method for filtering ideas, and outlines
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a realistic time budget and editing limits. You’ll get concrete guidance on idea
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sourcing, title crafting, article length, blogging tools (Medium, Substack, WordPress,
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Jekyll), writing habits, distribution via Twitter and LinkedIn, and writing at work
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(press releases, design docs, decision logs). Practical portfolio advice — clear
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README, quick start, repo tour — and tips to iterate outlines and ship weekly round
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out the episode. <br><br> Listen to learn a concrete 7-day workflow, documentation
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and portfolio best practices, and distribution tactics to boost your technical writing
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and advance your data science career.
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Today we're discussing technical writing, logging, documentation, and more. Our special guest is [Eugene Yan](/people/eugeneyan). Eugene works at the intersection of machine learning and product, building pragmatic ML systems while writing and speaking about effective data science, ML in production, and career growth.

‎_podcast/s02e02-developer-advocacy.md‎

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title: 'DevRel for Data Science: Community Growth, Reproducibility & Content Strategy'
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title: "DevRel for Data Science: Build Community, Create Content, and Grow Your Career"
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short: Developer Advocacy for Data Science
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guests:
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- elleobrien

‎_podcast/s02e03-open-source.md‎

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title: 'Contribute to Open Source ML: scikit-learn Pipelines, PRs, Docs & Rasa Conversational
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AI'
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title: 'Contribute to Open Source ML: scikit-learn Pipelines, PRs, Docs & Rasa Conversational AI'
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short: Getting Started with Open Source
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description: 'Learn open source contribution tactics for scikit-learn pipelines and
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Rasa: make solid PRs, write docs & tests, boost your OSS skills and career visibility.'

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