DataTalks.Club

We talked about Geo’s background Technical Product Manager Building ML platform Working on internal projects Prioritizing the backlog Defining the problems Observability metrics Avoiding jumping into “solution mode” Breaking down the problem Important skills for product managers The importance of a technical background Data Lead vs Staff Data Scientist vs Data PM Approvals and rollout Engineering/platform teams Data scientists’ role in the engineering team Scrum and Agile in data science Transitioning from Data Scientist to Technical PM Books to read for the transition Transitioning for non-technical people Doing user research Quality assurance in ML Advice for supporting an ML team as a Scrum master Links: Geo's LinkedIn: https://www.linkedin.com/in/geojolly/ Product School community: https://productschool.com/ http://theleanstartup.com/  Netflix CPO Medium blog: https://gibsonbiddle.medium.com/ Glovo is hiring: https://jobs.glovoapp.com/en/?d=4040726002 Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: CJ’s background Evolutionary biology Learning machine learning Learning on the job and being honest with what you don’t know Convincing that you will be useful CJ’s first interview Transitioning to industry Tailoring your CV Data science courses Moving to Berlin Being selective vs ‘spray and pray’ Moving on to new jobs Plan for transitioning to industry Requirements for getting hired Publications, portfolios and pet projects Adjusting to industry Bad habits from academia Topics with long-term value CJ’s textbook Links: CJ's LinkedIn: https://www.linkedin.com/in/christina-jenkins/ Positions for master students: one two Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Eleni’s background Spatial data analytics Responsibilities of a postdoc Publishing papers Best places for data management papers Differences between postdoc and PhD Helping students become successful Research at the DIMA group Identifying important research directions Reviewing papers Underrated topics in data management Research in data cleaning Collaborating with others Choosing the field for Master’s students Choosing the topic for a Master thesis Should I do a PhD? Promoting computer science to female students Links: https://www.user.tu-berlin.de/tzirita/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Sara’s background Product designer’s responsibilities Data product manager’s responsibilities Planning with the team Design thinking and product design Data PMs vs regular PMs Skill requirements for Data PMs Going from a product designer to a data product manager Case studies Resources for learning about product management Data PM’s biggest challenge Multitasking and context switching Insights from user interviews Using new, unfamiliar tools Documentation Idea generation Do Data PMs need to know ML? Links: Product Management Courses: https://www.lennyrachitsky.com/course and https://www.reforge.com/mastering-product-management Product Management Reading: https://svpg.com/inspired-how-to-create-products-customers-love/ and https://steveblank.com/category/customer-development/ Data Engineering for Noobs: https://www.datacamp.com/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Barbara’s background Do you need a manager or an expert? Technical and non-technical requirements for managers Importance of technical skills for managers Responsibilities and skills of a manager Importance of technical background for managers Getting involved in business development and sales Developing the team Checking team’s work Data science expert Hiring experts Who should we hire first? Can an expert build a team? Data science managers in startups Project management Ensuring that projects provide value Questions before starting a project Women in data science Finding Barbara online General advice Link: Barbara's LinkedIn: https://www.linkedin.com/in/barbara-sobkowiak-1a4a9568 Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Nick’s background Being a career coach Overview of the hiring process Behavioral interviews for data scientists Preparing for behavioral interviews Handling "tricky" questions Project deep dive Business context Pacing, rambling, and honesty “What’s your favorite model?” What if I haven’t worked on a project that brought $1 mln? Different questions for different levels Product-sense interviews Identifying key metrics in unfamiliar domains Tech blogs Cold emailing Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Noah’s background Solopreneurship A day of a solopreneur Exponential vs linear work Escaping the office work - digging the tunnel Structuring goals Staying motivated Publishing books Planning out books Writing a book is like preparing to run a marathon Distributed income Getting started as a solopreneur Lowering expenses and adding time The right time to quit full-time Building a network Teaching at universities Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Links: https://join.slack.com/t/integratedmlai/shared_invite/zt-r3hpj44k-gfhf1pzIt3jixrATyXCWnQ https://www.linkedin.com/in/thomives/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Caitlin’s background The last mile in data The Pareto Principle Failing to use data Making sure data is used Communicating with decision-makers Working backwards from the last mile Understanding how data drives decisions Sketching and prototyping Showing the benefits of power data Measurability Driving change in data Asking high-leverage questions Resistance from users Understanding domain experts Linear projects vs circular projects Recommendations for data analyst students Finding Caitlin online Links: Emelie's talk https://locallyoptimistic.com/post/linear-and-circular-projects-part-1/ https://locallyoptimistic.com/post/linear-and-circular-projects-part-2/ Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

We talked about: Rishabh's background Rishabh’s experience  as a sales engineer Prescriptive analytics vs predictive analytics The problem with the term ‘data science’ Is machine learning a part of analytics? Day-to-day of people that work with ML Rule-based systems to machine learning The role of analysts in rule-based systems and in data teams Do data analysts know data better than data scientists? Data analysts’ documentation and recommendations Iterative work - data scientists/ML vs data analysts Analyzing results of experiments Overlaps between machine learning and analytics Using tools to bridge the gap between ML and analytics Do companies overinvest in ML and underinvest in analystics? Do companies hire data scientists while forgetting to hire data analysts? The difficulty of finding senior data analysts Is data science sexier than data analytics? Should ML and data analytics teams work together or independently? Building data teams Rishabh’s newsletter – MLOpsRoundup Links: https://mlopsroundup.substack.com/ https://twitter.com/rish_bhargava Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html