Financial services growth marketing series is where I invite top thought leaders from the financial services industry to share their wisdom on what drives growth.

Mike Barlow shares his insights on:

• Integration Vs. Disruption: What's the right psychological mindset when it comes to growth
• Top challenges and opportunities for Fintech leaders in 2017
• Evolving role of financial services CMO in light of AI and Data Science technologies
• And tons of other stuff: reading rituals, bias in AI, and favorite data anecdotes...

You may follow Mike via Twitter or LinkedIn if you choose to keep up with his exciting adventures. 

Financial services growth marketing series is where I invite top thought leaders from the financial services industry to share their wisdom on what drives growth.

In this episode, Scott Wentworth shares his wisdom on:

• How to avoid the commoditization of financial services products & services
• Five step content creation process that took Scott several years to crystallize from having worked with some of the top financial services thought leaders...
• How to develop a unique point of view to improve resonance with your audience
• Last but not least - the impact of the political shift in financial services industry, and how it specifically affects you as a financial services growth leader.

Scott was kind enough to put together his blueprint for our podcast listeners in a PDF format to make it easy to share with others in your organization: info.wentworthwriting.com/data-leaders

More options to reach Scott are his Linkedin or his website. 

In this episode, David Herman shared his insights on:

• How can financial service companies create value using the emerging integration between behavioral neuroscience and financial data science (the right way and the wrong way)
• How the consumers win when these two fields interact
• How to win consumer trust when building advanced AI products or campaigns, especially catering to finance industry.

David Herman packed some powerful concepts and insights in quick 30 min conversation and it is definitely worth a listen or two :)

Dave's been busy at Payoff, but his youtube channel has some great videos as well. https://www.youtube.com/user/TheScienceguy3000 

For additional questions, you may reach out to David on his linked or his email dh.herman "at" gmail.com

In this podcast, we discuss:

1. Top challenges and opportunities financial services marketing leaders are experiencing
2. Role of data and metrics in consumer journey
3. Specific strategies to address financial services growth challenges
4. Email marketing strategies for Financial Services companies
5. Role of AI in modern marketing

Mika is a growth marketing thought leader in financial services space where she is passionate about helping companies with modern data-informed marketing. Mika does this with significant breadth and depth of strategic management and hands-on experience across marketing communications, channel, digital, social media, mobile, and emerging marketing technologies.

In this podcast episode, Gaurav discusses growth hacking, machine learning, and copywriting. 

Gaurav has been part of mission-critical teams at two San Francisco startups, LinkedIn Slideshare, and Klout. He previously cofounded ThisYaThat, an online book portal for the Indian market, which won the Wharton Entrepreneurship VIP Seed Award and was the first non-US venture to receive an entrepreneurial grant from Wharton’s Innovation Fund.

He was featured in Hindustan Times, Yahoo Finance, YourStory, and TechCircle. He is also the co-inventor of an application-agnostic user search engine. Most recently, he has been building Profillic, a product to fix candidate screening by applying machine learning to the problem of skill validation.

He is finishing up his Master’s, from Columbia University, with a focus on computational linguistics/natural language processing and machine learning, and he collaborated with the data services, machine learning, and business analytics team at Google Nest this summer.

Richard Demsyn-Jones has an extensive experience building predictive models. Currently, Richard is at Google Trust and Safety Analytics, Prior to that Richard helped Capital One with their data science opportunities as a principal data scientist. Richard has an academic background in economics.

I bumped into Richard when he was presenting on sports analytics, and despite zero interest or prior experience in watching in Hockey, I found myself deeply immersed in Richard’s presentation where he discussed data analytics around goalie quality...This goes to show Richard’s compelling storytelling abilities.

Couple of fun facts about Richard: He thinks cereal is good for any meal, also he’s from Canada, but doesn’t want you to hold that against other Canadians.

In this podcast, Richard shares his thoughts on:

1. When it comes to betting, is natural talent more important than quantitative skills
2. Nate Silvers story on why he left the poker world...
3. A key area for a sports analyst to focus on (even if you are a hobbyist, you may want to follow the advice here)
4. How can data be applied when selecting lineups
5. Going beyond sports, how to apply data when building new teams and increase team performance…where you can play off the way different strengths interact with each other.
6. Round of rapid fire questions on data…my favorite part.

Richard’s Blog:

https://oddacious.github.io/

Richard’s email:

fumble.to.victory [at] gmail.com

Disclaimer: Opinions expressed herein are in my own, and do not represent my employer’s viewpoints on sports analytics or other projects.

Sam Abrahams is a TensorFlow evangelist and a kickass storyteller, programmer, and a statistician! In this podcast episode, Sam discusses his thoughts on TensorFlow coming from a hard-core practitioner point of view. We chat about:

• TensorFlow and how it compares to other machine learning frameworks
• Mastering TensorFlow: How to go from being zero to well-sought out TensorFlow expert
• Journey of a Data Scientist. What does it take to become one...
• And a round of intriguing rapid fire questions guaranteed to give you some golden nuggets!

Show References:

TensorFlow White Paper

Sam's Github With TensorFlow Whitepaper Notes

Sam's Upcoming Book on TensorFlow For Machine Intelligence

Sam's Blog: http://www.memdump.io/

Sean is the director of data science at Space-Time Insight, a leading provider of advanced analytics software for organizations looking to leverage machine learning for their business applications. Having worked across diverse industries, and alongside many talented professionals, Sean has seen the blend of approaches required to successfully convert raw data into real world value.

Sean holds his doctorate in scientific computing, where he used advanced mathematics, parallel computing and optimization to solve challenges in nanotechnology, chemistry and renewable energy. After completing his Ph.D. Sean started his own data science consulting practice, helping companies automate decision-making and uncover patterns in large amounts of data. Sean has since joined a major technology consulting firm working with cross-discipline teams to build the next generation of adaptive, data-driven applications. 

Sean’s upcoming book is focusing on building products with data and will be published by O'Reilly.

To connect with Sean McClure, reach out to him on LinkedIn or Twitter. 

In this conversation, we touch on Return of Investment on Data Science and other things like:

• Real World Value of Data Science
• Pitfalls of Data Science
• Building Data Science Teams and Culture
• Is Data Science a Boy's Club?
• How to Develop Storytelling skills as a Data Scientist?

Be sure to check out the end of the podcast where Sean reveals his favorite data science product that had the most positive impact on his life. 

Ben Spooner has several years of experience as an Army Officer at a battle post in Iraq, both in Intelligence as well as Executive capacities. I sat down with him to discuss his thoughts on activating a data driven work culture using data visualization and dashboards as primary weapons. 

Here's what we discussed:

• A complete dashboard lifecycle - from inception to decision-making
• How to build dashboards that drive decision making
• Up close and personal look at the data scientist who did data research and exploration at the front line trenches of  insurgency in a war zone
• When is raw data the best form of reporting (instead of a fancy-schmancy dashboard)
• Two critical factors that fuels dashboard building process…They are C-words (hint: Curiosity and Collaboration)
• Data Driven culture has two key pursuits: truth and discovery. How to make a dashboard that builds this type of a data driven culture...
• How to deal with stakeholders who don’t appreciate the exploratory process of dashboard building… and how to set their expectations and empower them.
• Bonus: Self-correcting semi-autonomous system and its application to insurance claims processing...

Reach out to Ben's through LinkedIn or Email to learn more about his exciting data science projects.

• How to get unicorn conversion rate for mobile devices...
• What is Adaptive Design and how does it differ from responsive design?
• Mobile specific calls to actions and navigation features...
• How to effectively use sticky header and footer in mobile design?
• How to cut testing time by more than half so you can fit more tests in your testing calendar!

For the past 2 years, Zach has been working as a data scientist at an industry leading data consulting firm.  He works in fraud analytics space where he and his team has saved hundreds of millions of dollars of federal dollars using sophisticated data science techniques. He is also a recent graduate of data science program at UC Berkeley.

When I met him, I was really impressed with your ability to speak “real world” data science and later I found out that he has a professional background in teaching complex topics like physics and calculus, which is what makes you such a good communicator in this field.

I sat down with him on a sunny Saturday afternoon to discuss one of the most exciting projects he has worked on in his data science career. 

Here's a quick recap of what we discussed: