Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

By Felipe FloresTechnology

Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science episodes

May 30, 2019#53 Winning Data Science Competitions With Pavel Pleskov - Data Scientist & Kaggle Grandmaster, Top 3 In The World
Pavel Pleskov is a data scientist at Point API (NLP startup) and currently ranks number 3 out of 109,624 on Kaggle, making him a Grandmaster. Pavel has started companies in the past and has worked in many different industries before becoming a data scientist and Kaggle Grandmaster.
In this episode, Pavel explains his background and how he started in the data science space. When Pavel’s girlfriend went to pursue her master’s degree in London, Pavel began interviewing for a quantitative research job nearby. Turns out, the company was a rival of his current employer, causing him to get fired from his job the next day. Former employees of this job contacted Pavel to ask if they would join their new trading firm and be head of their research team. After doing his job for two years, Pavel knew he was capable of doing it on his own. The company works remotely, and after spending time in bitter Russian winters, Pavel looked to work elsewhere. The ideal country turned out to be Vietnam and was Pavel’s first time outside of Russia.
Enjoy the show!

We speak about:
[01:50] How Pavel started in the data space

[09:50] Vietnam is an ideal space for working remotely and teaching English

[21:40] The moment Pavel found Kaggle

[24:20] How Pavel became a data scientist

[28:00] Difference between machine learning engineers and researcher data scientists

[31:50] Why is it essential to be the very best?

[34:20] Machine learning and mathematics

[36:45] The early days of Pavel’s Kaggle journey

[40:00] Pavel’s favorite part of Kaggle

[47:20] The role of automation in Kaggle

[49:40] The steps when approaching a new Kaggle competition

[52:55] Think twice before you commit to data science

Resources:
Pavel’s LinkedIn: https://www.linkedin.com/in/ppleskov/?originalSubdomain=ru
Pavel’s Kaggle: https://www.kaggle.com/ppleskov
Pavel’s Twitter: https://twitter.com/ppleskov

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
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 [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
57 min
May 23, 2019 #52 Transforming Marketing with Data Science with Danielle Timmins – Chief Data Analytics Officer
Danielle Timmins is the Chief Data Analytics Officer for Free Range Creatives. Free Range Creatives is a digital marketing agency that is deeply rooted in data and analytics. They have a different view on agency life and challenge the existing ways of working. They believe that work should be fun (well, at least most days) and that our work must be insightful, inspirational and effective.
In this episode, Danielle tells us how she did not start in the data space but initially wanted to be a doctor. Danielle ended up getting a Master’s in Economic Psychology, during which she concentrated on the digital side of marketing. This is where Danielle got her exposure to data and started to understand it. Danielle got her first start at an NGO in a marketing position. She would shoot mini-documentaries for television and then moved into a more traditional marketing role. Danielle’s first job as a strategist was down in South Africa where she worked with several different clients. This is when she would start to work with data and incorporate it with strategy.
Enjoy the show!
We speak about:
•    [01:45] How Danielle started in the data space
•    [03:20] Background and career
•    [06:20] Deciding what problems to tackle first on the job
•    [08:35] Evolution of marketing
•    [13:35] Favorite failure
•    [16:50] How to communicate data
•    [18:30] Visual presentation style
•    [19:45] How Danielle creates a story
•    [21:30] How do you structure visuals for executives?
•    [23:10] How do you think people can get better at this skill?
•    [24:45] What is a strategist for data?
•    [27:40] What is the role outside of data?
•    [29:00] The main challenges for Danielle’s clients
•    [32:30] Working with clients on case-by-case basis
•    [33:30] Qualities of a great data scientist
•    [35:30] What do you think makes a good data leader?
•    [36:15] Current challenges in the data space
•    [37:40] Future challenges for the data space
•    [42:40] Advice for future data scientists and leaders
Resources:
Sexy Little Numbers
Free Range Creatives: https://www.freerangecreatives.co.za/
Danielle’s LinkedIn: https://www.linkedin.com/in/danielletimmins/
Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
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 [email protected]
Fyrebox - Make Your Own Quiz!
And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
47 min
May 16, 2019#51 AI-as-a-Service with Technology Executive & Serial Entrepreneur Peter Elger, Founder and CEO of fourTheorem
Peter Elger is the founder and CEO of fourTheorem; his focus is on delivering business value to his clients through the application of cutting edge serverless cloud architectures and machine learning technology. His experience covers everything from architecting large-scale distributed software systems, to leading the internationally-based teams that built them.
In this episode, Peter tells us how his first real passion was in physics. After graduating with a BSc in Physics and a master’s degree in Computer Science, he worked for several years at the Joint European Torus (JET), the world's largest operational magnetically confined plasma physics / nuclear fusion experiment. They were doing big data, but at the time they did not refer to it as such; they dealt with around four to five terabytes of scientific data. Peter then transitioned to Indigo Stone as a Senior Technical Architect. Indigo Stone was a software disaster recovery firm which exited in 2007 to EMC.
Peter explains how it is essential to keep your technical skills up-to-date and why some of his favorite days are when he gets to code despite being the CEO of his company. If you can actually be the bridge between the business and the technology, you are an invaluable asset to any company. The freedom to innovate is what led Peter to his entrepreneurial ventures; previously, he had no real experience being his own boss. Peter says it is dangerous to think you can do everything; you have might a broad skill set, but you need to recognize that you have gaps. This is why Peter has always started businesses with co-founders. Currently, his co-founder is a world-class technologist and someone who understands the human dimension. All of his current co-founders and people Peter has worked with previously.

Enjoy the show!

We speak about:
[01:45] How Peter started in the data space

[06:50] Transition to disaster recovery

[08:55] Interactive radio and marketing applications

[13:40] Maintaining a grip with technical skills

[16:20] The entrepreneurial bug came organically to Peter

[18:40] Transition to entrepreneurship

[21:45] What to look for in a co-founder

[26:00] Building analytics with machine learning

[29:00] A tale of two technologies

[33:00] Applying AI to existing platforms

[35:10] Knowledge of AI is not necessary to use AI as a service

[37:50] Capable team members are difficult to find

[40:10] Sharing management meetings with all staff members

[44:05] Experiences with handling politics in organizations

[48:50] Removing ego + allowing the team to do their best work

[50:30] Scheduling work to maximize the impact

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology

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 [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
57 min
May 10, 2019 #50 Making CDOs more effective with Prakash Baskar – Founder and President
Prakash Baskar is the Founder and President of Khyanafi. He helps data leaders to rapidly transition and accelerate the success of data, analytics, and digital initiatives. Previously, Prakash was the Chief Data Officer at Santander Consumer USA where he led enterprise data governance, risk infrastructure & information (risk data aggregation), data quality, business data strategy & solutions, and business & reporting analysis functions.
In this episode, Prakash tells us how he started in the data space at his university. His role was to determine how students were performing. If they are not performing well, he needed to identify why. The graduation rates were low at the school, so Prakash was tasked with finding out what was the problem. Then, Prakash discusses starting a new job and having little direction about what to do. With everchanging technology, the description of your job will always be changing too. As a person going into any role, understand that you do not have to ask permission all the time. Have a clear idea of what you can do and what you cannot do, then do what you feel is right for the organization. Look for where the opportunities for expansion are and find a way to get results.
If you ask ten people what the role of a Chief Data Officer is, you will get ten different answers. Whatever the CDO does will ultimately be to enable others to receive real benefits out of the data. Just because something is not broken, does not mean it cannot be improved. There are many different routes a person can take to become a CDO; however, you need someone with knowledge in multiple aspects of business, technology, and people management. A CDO needs to create value for the organization; learn the company you are supporting to anticipate the problems they may run into.
Later, Prakash explains how in business, any change is hard. How you embrace the change after it is made is what will differentiate yourself from others. If the change is too complicated, people will shut off. Start off by telling the client what the change will do for them rather than the steps it will take to get there. Some other tips when presenting a significant change is to be realistic with what it will take and make sure not to overpromise. It is imperative to select things that you can quickly do with minimal engagement from their people. Plus, make sure you have updates for the company each month, so they understand what is being revealed from the data. Finally, Prakash discusses how essential it is to move around the organization in order to understand different departments and he reveals the inspiration behind his latest business venture.
Enjoy the show!

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
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 [email protected]
Fyrebox - Make Your Own Quiz!
And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1 hr
May 04, 2019#49 Becoming a Kaggle Competition Master with Valeriy Babushkin – Head of Data Science, Kaggle Competition Master (Top 60)
Valeriy Babushkin is the Head of Data Science at X5 Retail Group where he leads a team of 50+ people (4 departments: Machine Learning, Data Analysis, Computer Vision, R&D) and increases profit in a 25+ billion USD company. Also, Valeriy is a Kaggle competition master; ranking globally in the top 60.
In this episode, Valeriy explains his background and how he started in the data science field. At one point, he received an offer for a senior position at a bank; it was the largest privately owned bank at that time in Russia. Valeriy did not find out that he was doing machine learning until working on it for two years. What someone is doing right now could be pretty close to machine learning, and they don't even know. Then, Valeriy speaks on how trust is essential to the job of a data scientist; not only between you and your boss but between you and other departments. Trust will make your job easier when explaining the data, the results, and how reliable they are for the company. However, if there is an existing data science department in the company, you will not have to work as hard to earn the trust of others because it already exists. Sometimes when data scientists join a company, they think their job will just be to code all day. That is not always the case, you will have to talk to many people and often be a business analyst.
Enjoy the show!

We speak about:
[01:45] How Valeriy started in the data space

[06:10] Transiting to working at a bank

[11:30] Understanding the business process

[15:10] Gaining trust from clients

[20:20] Data scientists are business analysts

[24:10] Expectations from the job interview

[25:50] Starting data science teams

[31:40] The type of mindsets to look for in a team member

[37:30] Different teams complement each other

[40:20] Valeriy’s journey with Kaggle

[47:40] Ethical challenges in the industry

[51:20] Persistence is key

Resources:
Valeriy’s LinkedIn: https://www.linkedin.com/in/venheads/
Valeriy’s Kaggle: https://www.kaggle.com/venheads

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
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 [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
55 min
April 26, 2019 #48 Leverage What You Know to Get Your Foot in the Door with Jay Liu - Chief Data Scientist at Digital-Dandelion
Jay Liu is the Chief Data Scientist at Digital-Dandelion specializing in helping insurance, and medical organizations innovate by integrating the latest in Artificial Intelligence (AI), machine learning and big data into their systems. Knowing the best way to learn is by putting your money where your mouth is, Digital-Dandelion launched an online brand and built a customer AI to promote it. There were numerous technical and modeling challenges that were overcome, but in the end, they sold all their stock within three months. They had proven to themselves that customer AI worked. Organizations can have great depth and breadth of customer data from their long-term relationships of selling high-value products and services.
In this episode, Jay explains how he found himself in advertising and started getting fat because of all the Michelin star restaurants his potential clients would treat him to. His data science career began with loyalty cards and being incredibility confident. When someone uses a loyalty card, the company is collecting data. They will know exactly what you purchased and how much you purchased of each item. The customer will be rewarded with monthly coupons. Jay was in charge of coming up with the coupons that were designed to make the customer spend more money in the store. Knowing at least one data programming language will leverage what you have and give you one foot in the door. The best way to get into data science is to know how it will improve the current industry or business you are working for.
Later, Jay explains why QA is a lost skill and the idea that great data scientists have internal discipline. However, there is a race to push the boundaries and become more automated. For example, Facebook collects as much data as possible and thinks about the consequences later. Data is data and people are people. Understanding data is the starting point. Before Jay starts a job, he dives deep and analyzes what every number means to the business with their data collection. Also, Jay considers how to make his bosses job as easy as possible. Overall, the success of his boss will create the most significant impact on his business. If someone has been working at the same job for ten years, they are scared to grow and try something new. Finding a data scientist who has worked at multiple different sizes and types of organizations is the key to finding a well-rounded employee.

Enjoy the show!

Resources:
Jay’s LinkedIn: https://uk.linkedin.com/in/jay-liu-76ab2b8a
Digital-Dandelion: https://www.digital-dandelion.com

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
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 [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

44 min
April 19, 2019 #47 Transforming Government Organisations with Data Science with Marek Rucinski – Deputy Commissioner, Smarter Data Program
Marek Rucinski is the Deputy Commisioner leading the Smarter Data Program at the Australian Taxation Office (ATO). Marek has taken part and driven the evolution and transformation of Marketing, Analytics, Data and Digital capabilities for over 20 years. This has been done in both industry roles and consulting services capacity, across Australian, Asian and Global clients, across Retail, Telco, Consumer Goods, Financial Services, Mining & Utilities sectors. His passion centers on helping clients change the role of Marketing & Analytics capabilities in Digital and Data age, from activating the capability through acting on insights, to transforming customer experience and the whole business via delivering value across business functions. Prior to ATO & Accenture, Marek lead and created analytics functions and teams in a Retail industry, and developed global corporate strategy frameworks and analytics in a multinational organizations.
In this episode, Marek tells us about how he was always interested in the science behind marketing. Marketing as a discipline has been completely transformed due to the emergence of data as a driver for engagement with the customer. Marek is not a classically trained data scientist; he is a data strategist and can dive deep into the organization’s needs in order to drive value to the customer. Marek tells us how some businesses can struggle with how to handle the findings of research from data scientists. It is essential to translate the potential into targets to create the prize. Leave the ego at the door and find the ability to be critiqued.
Later, Marek tells us how educating businesses on analytics as a mechanical process is essential for them to perceive how the whole thing works. He then explains his transition from consulting to government and how his excitement lies in the play with analytics at an enormous scale. Then, Marek describes how to have each section of the value chain working with purpose and precision. Data has to be trusted, organized, and accessible for the company. A data strategist must consider how the data is being delivered to their client. You want to create products and interactive experiences for the business as opposed to simple spreadsheets. Finally, Marek answers the audience’s questions including what makes a good data scientist and current challenges in the data science industry.

Resources:
Marek’s LinkedIn: https://www.linkedin.com/in/rucinskimarek/

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
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 [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

39 min
April 12, 2019#46 Today is the Best Time to be a Data Scientist with Jonny Bentwood – Global Head of Data & Analytics
Jonny Bentwood is the Global Head of Data & Analytics at Golin. Jonny is an innovative leader with 15+ years of experience in communications - winning, retaining and working for Fortune 100 clients such as Facebook, Unilever, Heineken, Barclays, HP and Microsoft. He has a proven record as a creator of pioneering solutions with ability to transform business to radically impact bottom line. Jonny presents complex information in an engaging and informative style and is a strategic consultant to executives using data to provide guidance on reputational and crisis issues and maximising marketing campaigns.
In this episode, Jonny tells a story about how MTV got in touch with him to apply data in figuring out who would most likely win The Apprentice. After being in the industry for over twenty years, he believes this is the best time to be in data. CMOS are spending more of their money than ever before on analytics. How do data scientist prove their value? People use data purely in a descriptive way. To succeed and bring value to clients, one needs to switch from describing the data to telling the customer what they need to do with the data. Set the goals of who, what, and why to figure out which message will be most useful before you even start. Take it a step further by using prescriptive data and make it predictive. This is where you study what will happen in the future. We are continually absorbing and understanding what things could happen and will happen. This opportunity is essential to identify issues before they occur and fix them.
We speak about:
[01:30]      How Jonny started in the data space

[04:50]      Public relations

[06:00]      Descriptive, prescriptive, and predictive

[08:15]      Difference between interesting and useful

[10:00]      Understanding the customer

[15:25]      Cultural shift of data in organizations

[19:10]      Challenging the status quo

[22:40]      Shiny object syndrome

[26:45]      The twenty percent time

[30:00]      Bringing data application to the masses

[34:30]      Each stage of the customer journey

[39:30]      Getting value for money

[42:45]      Return on investment

[44:15]      Data + creativity
Resources:
Jonny’s LinkedIn: https://uk.linkedin.com/in/jonnybentwood
Jonny’s Twitter https://twitter.com/jonnybentwood?lang=en

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
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 [email protected]
Fyrebox - Make Your Own Quiz!
And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
56 min
April 05, 2019 #45 Mastering the Domain of Your Work Before Becoming a Data Scientist with Warwick Graco - Senior Director Data Science
Warwick Graco is the Senior Director of Data Science at the Australian Taxation Office (ATO). He has worked in defence, health, and taxation and has been involved in analytics for 25 years. He is a practicing analytics professional and is currently convenor of the Whole of Government Data Analytics Centre of Excellence and is a senior data scientist in Data Science and Special Acquisition Group of the Smarter Data Program of the ATO. He has a BSc from the University of New South Wales and a Ph.D. from the University of New England Australia. His professional interests include organisational innovation and learning, organisational decision making and analytics.

In this episode, Warwick tells us how he got started in data research the skills gained that led him to his successes today. Warwick explains why transparency is a business requirement for software and tools in the data science field. People with more analytical backgrounds will be more willing to accept an opaque solution over a transparent solution. When analytics was in the early stages, some organisations pushed back from data science; feeling they were on top of their portfolio and did not need any outside resources. No matter what results Warwick would come up with for these organisations, they would continue to have the same attitudes. Since 2010, there has been a shift in attitudes because data science has shifted from the background to the foreground.

Then, Warwick tells us the difference between good support and lousy support in the workplace. While Warwick was working with organisations, instead of providing results, he did the reverse. Ask the organisation what they want rather than telling them the findings. Providing the outputs clients wish to see led to incremental improvements built into their business intelligence reports. Warwick also explains why you can no longer be a data scientist; you will need to learn and master the domain of your work. For instance, Warwick learned everything about ophthalmology while working on data science with an ophthalmologist. Later, Warwick explains his process of publishing research, improving privacy concerns, and automated supports.

Enjoy the show!

Show Notes:
• [02:20] How Warwick started in data science
• [05:55] Aptitude for research
• [08:40] Purpose-built software + decision trees
• [12:20] Accepting opaque solutions vs. transparent solutions
• [16:45] Pushback of data analytics
• [21:15] Difference between good support and bad support on the job
• [25:25] Necessity to learn the domain first
• [29:00] How to learn on the job
• [32:20] Process of publishing research
• [41:50] Improving legal and privacy concerns
• [44:25] Automated support + decision-making operations
• [52:40] Developing an analytical + practical mindset
• [58:10] Hyperspecialized
• [64:30] Moving toward data + analytics as a service
• [66:25] Advice from Warwick

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
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 [email protected]
Fyrebox - Make Your Own Quiz!
And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

1 hr 10 min
March 29, 2019#44 Using Data Science to Actually Solve Problems with Caroline Worboys - Data Expert, Investor, Advisor, COO & Vice Chair
Caroline Worboys is a data expert, investor, advisor, COO at Outra & Vice Chair at DMA Group. She has been working in the data industry for over 30 years. In this time, she’s had a fascinating journey. She has worked, created, mentored and consulted through many data driven organisations. She’s played all the different roles: technical lead, a business lead, a founder and investor.
While Caroline doesn’t describe herself as a data scientist and didn’t go to university, she has always worked with data and has a wealth of experience. She started in the field by working with consumer data for direct marketing and progressed to the point where she founded and sold several successful data related start-ups. Currently, she is the founder and COO of Outra.
In this episode, we talk about what it was like being a woman in technology in the 80’s, how the use of data has progressed over the years and how she keeps her team focused on the goal of doing things faster than other companies.

Summary
How Caroline got started in data (03:02)

What she learnt from observing senior colleagues and what it was like being a woman in technology in the 80s (05:38)

Using customer data in order to target people at the right time (07:46)

The principles of working with consumer data hasn’t changed (10:04)

How the care and attention required for direct mail has now been lost with email and digital marketing (11:09)

The importance of being curious and learning (12:31)

Starting her own business and finding a different way to charge customers (13:46)

Advice for young people and why it’s important to seek people for advice (21:34)

Personal drivers to start her business (23:35)

How her business innovated as technology changed (25:10)

The challenge of using data to actually solve problems (30:29)

Considerations when choosing her team (35:48)

The recruitment process is like for Caroline’s company (39:00)

How Caroline keeps her team focused on the goal of doing things faster than other companies (41:40)

The difficulties of work/ life balance (44:16)

Considerations for being a leader in the data space (47:03)

The importance of thinking about the type of data you want to work with (51:43)

Quotes
“Seek out people who have really, honestly read the book and seen the movie and been there. Because they can stop you from going down a whole bunch of dead ends.”

“You can’t scale and have thousands of relationships with thousands of people. But you can create a culture, and processes below that culture, that are scalable.”

Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology

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 [email protected]
Fyrebox - Make Your Own Quiz!

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
58 min

About Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

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Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science episodes

About Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science