#32 Carole Wai Hai - Head of Data Science & Analytics

Ep32 Carole Wai Hai - Berlin, Germany.jpg

Carole had an unusual path into data science. She's worked as a content project manager, in strategic planning and in sales before getting into data through Business Intelligence at Fyber where she eventually became their Head of Analytics. Today she is the Head of Data Science & Analytics at Tenjin.

We speak about:

* The strengths of being a generalist

* Upskilling throughout your career

* Focus on self service reporting

* The skills needed in a BI team

* Creating internal user groups to share knowledge

* Convincing people to get training on the tools required to do their job better

* The benefits of gaining a reputation internally

* Setting a strategy for data teams

* The importance of data modelling skills in data teams

* Learning technology on the job when you're background is not technology

* Monthly meeting with key departments to review all dashboards in the department

* Working remotely in global companies

* Metrics about user behaviour

* Offering analytics for many customers with the same problem/need

* How to develop consulting skills

* The platinum rule - book on communication style

* The leadership challenge - book recommendation

* What it's like working in startups

* How to recover from being a workaholic

Carole is based in Berlin Area, Germany

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

Thank you to our sponsors: 

UNSW Master of Data Science Online: studyonline.unsw.edu.au 

Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au 

Fyrebox - Make Your Own Quiz!

#29 Dr. Klaus Ifflander - Chief Analytics Officer

Ep29 Klaus Ifflander - Berlin, Germany.jpg

Klaus started his career doing internships at Yahoo! and the port of Hamburg. He worked as a consultant and completed a PhD in Quantitative Marketing. Today he is the Chief Analytics Officer at YAS.life

We speak about:

* The importance of getting applied experience as early as possible

* Defining KPIs for businesses

* Using data to change organisational behaviour and increase safety

* How to navigate organisations to create data definitions

* Realities of consulting: positives and negatives

* Why large companies require so much custom work

* How to help people and organisations that don't know what they want

* Helping organisations in progressing through their analytics journey

* How to overcome technical challenges with creative solutions in your projects

* Why honesty within yourself and others is imperative in your work

* How to provide customers what they need instead of what they want

* The importance of hard and soft metrics when measuring value

* Applying soft skills in data science

* How to find what will be valuable for your customers

* Expanding your interest with a postgraduate degree

* How your social surroundings affect your purchase decisions

* Using soft skills for data acquisition

* What is eigenvector centrality and what is it used for?

* How product reviews influence your buying decisions

* How to create experiments in business

* Pricing models in the steel business

* Data science in fitness startups

Klaus is based in the Berlin Area, Germany.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

#28 Jennifer Prendki - VP of Machine Learning & Data Strategist

Ep28 Jennifer Prendki - Mountain View, CA.jpg

Jennifer started her career as a particle physicist before becoming a data scientist. After gaining experience in many fields including high frequency algorithmic trading & advertising, she was Atlassian's first Chief Data Scientist. Today she is the VP of Machine Learning at Figure Eight and an Expert and Advisor at the International Institute for Analytics.

We speak about:

* How to see the results of your work sooner and faster

* The importance of choosing your manager

* Making data strategy decisions for companies that are very immature in their approach to data

* Building data science teams from scratch

* Combining impostor syndrome and leaps of faith for your benefit

* The importance of making mistakes to be successful

* What having a great data culture really means

* How to convince peers and supervisors on the benefits and the path of data strategy

* Differences between having a technical and non-technical manager

* Combining technical abilities and business sense

* The importance of customer contact for technical people

* Focus on the impact and outcome of everything that you're building

* How to keep the balance in teams

* Pleasing customers vs product intuition

* How to drive and create a data driven culture

* How to create scale with your data science efforts

* How to build your data science team

* Data engineering vs Machine learning engineer

* How to keep talent

* How can data scientists learn the skills for business leadership

* Active learning and building products for data scientists

Jennifer is based in Mountain View, California

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

#25 Ben Taylor - Chief Al Officer & Cofounder

Ep 25 Ben Taylor - Provo Utah Area.jpg

Ben started his career as a chemical engineer. He developed an interest for computer vision early on. He worked for Intel, then at a hedge fund and then became the Chief Data Scientist at HireVue. A couple of years ago he started his own AI startup called Ziff.ai where he's is building a Deep Learning platform for product visionaries and software engineers.

We speak about:

* How computers amplify us

* What it looks like to start your own AI company

* How to switch programming languages

* Downsides of Google's tensorflow

* What industry expects from data science

* How to deliver value with ML

* How to pick ML projects to tackle

* Eliminating bias in AI applications

* AI powered job interviews of the (near) future

* Topic discovery with DL

* AI warfare in business

* What is a Hive Mind and how it works

* Future health care assessments at home

* AI is cute until it's scary

* The importance of passion and obsession in data science

Articles by Ben on Linkedin:

This is Why Your Data Scientist Sucks:


The Al War Machine: Our Darkest Day


The Al War Machine: The Hive Mind


Getting That Data Science Job


From 0 to $100K+ data science job in 6 months


Ben is based in the Provo, Utah Area

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

#23 Mario Vinasco - Marketing Analytics and Data Science Manager


Mario is an Electrical Engineer from Colombia. He went to Silicon Valley to do his Masters at Stanford University and stayed to build a career in Marketing Analytics. He has incredible experience and has worked at Intuit, Google, HP, Symantec and Facebook. He currently works at Uber as Marketing Analytics and Data Science Manager.

We speak about:

  • Starting in marketing analytics without knowing anything about it

  • The creatives and the quants of marketing analytics

  • How the headaches of tech have changes over the years for data scientists

  • Data dictators and why multiple versions of the truth are necessary

  • How to communicate results of your analysis to executives

  • The importance of data science education in organisations

  • How to pick the best predictive model for your applications

  • Analytics as the art of counting

  • The importance of working with inspiring people

  • Why running experiments is the gold standard of finding what works

  • How to use people analytics - Google style

  • Why your job is to empower your stakeholders - and how to make this a 2 way relationship

  • How to not saturate your channels

  • What he looks for in CVs and applicants

  • How to stand out during interview processes

  • The coolness of network partitions at Facebook

  • Using embeddedings at Facebook - using vectors when one hot encoding gets too cumbersome

  • The importance of rapid prototyping

  • The value of collaboration across your organisation and what that looks like in practice

  • Things to look for and ask during your interview process

  • How to choose where to work

  • Setting up new practice globally at Uber

  • Product manager as a facilitator, protector and enabler

Mario is based in the San Francisco Bay Area

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!