Democratizing data, and developing data culture in large enterprise organizations is an incredibly complex process that can seem overwhelming if you don’t know where to start. And today’s guest draws a clear path towards becoming data-driven.

Meenal Iyer, Sr. Director for Data Science and Experimentation at Tailored Brands, Inc., has over 20 years of experience as a Data and Analytics strategist. She has built several data and analytics platforms and drives the enterprises she works with to be insights-driven. Meenal has also led data teams at various retail organizations, and as a wide variety of specialties in Data Science, including data literacy programs, data monetization, machine learning, enterprise data governance, and more.

In this episode, Meenal shares her thorough, effective, and clear strategy for democratizing data successfully and how that helps create a successful data culture in large enterprises, and gives you the tools you need to do the same in your organization.

[Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

When many people talk about leading effective Data Science teams in large organizations, it’s easy for them to forget how much effort, intentionality, vision, and leadership are involved in the process.

Glenn Hofmann, Chief Analytics Officer at New York Life Insurance, is no stranger to that work. With over 20 years of global leadership experience in data, analytics, and AI that spans the US, Germany, and South Africa, Glenn knows firsthand what it takes to build an effective data science function within a large organization.

In this episode, we talk about how he built NeW York Life Insurance’s 50-person data science and AI function, how they utilize skillsets to offer different career paths for data scientists, building relationships across the organization, and so much more.

[Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

The healthcare industry presents a set of unique challenges for data science, including how to manage and work with sensitive patient information and accounting for the real-world impact of AI and machine learning on patient care and experience.

Curren Katz, Senior Director for Data Science & Project Management at Johnson & Johnson, believes that despite challenges like these, there are massive opportunities for data science and machine learning to increase care quality, drive business objectives, diagnose diseases earlier, and ultimately save countless lives around the world.

Curren has over 10 years of leadership experience across both the US and Europe and has led more than 20 successful data science product launches in the payer, provider, and pharmaceutical spaces. She also brings her background as a cognitive neuroscientist to data science, with research in neural networks, connectivity analysis, and more.

[Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Today marks the last episode of our four-part DataFramed Careers Series on breaking into a data career. We’ve heard from Sadie St Lawrence, Nick Singh, and Khuyen Tran on best practices to adopt to help you land a data science interview. But what about the interview itself? Today’s guest, Jay Feng, joins the show to break down all the most important things you need to know about interviewing for data science roles. Jay is the co-founder of Interview Query, which helps data scientists, machine learning engineers, and other data professionals prepare for their dream jobs.

Throughout the episode, we discuss

The anatomy of data science interviews Biggest misconceptions and mistakes candidates make during interviews The importance of showcasing communication ability, business acumen, and technical intuition in the interview How to negotiate for the best salary possible

[Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Today is the start of a four-day careers series covering breaking into data science in 2022. With so so much demand for data jobs today, we wanted to demystify the ins and outs of accelerating a career in data. In this series, we will interview a diverse range of thought leaders and experts on the different aspects of standing out from the crowd in the job hunt.

Our first guest in the DataFramed Careers Series is Sadie St. Lawrence. Sadie St Lawrence is the Founder and CEO of Women in Data, the #1 Community for Women in AI and Tech. Women in Data is a community of over 20,000 individuals and has representation in 17 countries and 50 cities. She has trained over 350,000 people in data science and is the course developer for the Machine Learning Certification for UC Davis. In addition, she serves on multiple start-up boards, and is the host of the Data Bytes podcast.

Sadie joins the show to talk about her career journey in data science and shares the best lessons she has learned in launching data careers.

Throughout the episode, we discuss

The different types of data career paths available How to break into your data science career How to build strong mentor/mentee relationships Best practices to stand out in a competitive industry Building a strong resume and standing out from the crowd 

[Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Data literacy at any organization takes buy-in from all levels of the company, from C-suite leaders all the way to customer-facing team members. But how do you get that buy-in, build a team around data literacy, and transform the way your company works with data?

Today’s guest, Megan Brown, Director of Data Literacy and Knowledge Management at Starbucks, discusses what they have done to forge data culture and data literacy at Starbucks.

Throughout the episode, we discuss

How to increase data literacy in an organization How to secure executive sponsorship for data initiatives The importance of user experience research in building data literacy  Balancing short-term business needs with long-term strategic upskilling Humanizing machine learning and AI within the organization 

Throughout the middle east, efforts are underway to build smart cities from the ground up.

But to create a modern, intelligently-designed city, you first need to lay a solid foundation.

And the strongest foundation you can build a smart city upon is data.

In today’s episode, we speak with Kaveh Vessali, Digital, Data & AI Leader, PwC Middle East, about the intersection between data and public policy and the many exciting insights he’s gained from his role delivering smart cities and data transformation projects within the public sector in the middle east. 

Join us as we discuss:

The important role data plays in shaping public policy  What goes into designing a smart city The change management skills vital for successful digital transformation  Data ethics and the importance of transparency  

Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. 

Listening on a desktop and can’t see the links? Just search for DataFramed in your favorite podcast player. 

In this episode of DataFramed, we speak with Shameek Kundu, former group CDO at Standard Chartered Bank, and Chief Strategy Officer & Head of Financial Services at TruEra Inc about Scaling AI Adoption throughout financial services.

Throughout the episode, Shameek discusses his background, the state of data transformation in financial services, the depth vs breadth of machine learning operationalization in financial services today, the challenges standing in the way of scalable AI adoption in the industry, the importance of data literacy, the trust and responsibility challenge of AI, the future of data science in financial services, and more.

Relevant links from the interview:

We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second surveyCheck out TruEra in actionBank of England Report: The impact of Covid on machine learning and data science in UK BankingMIT Tech Review — Hundreds of AI tools have been built to catch covid. None of them helped

In this episode of DataFramed, we speak with Syafri Bahar, VP of Data Science at Gojek about building high-performing data teams, and how data science is central to Gojek’s success. 

Throughout the episode, Syafri discusses his background, the hallmarks of a high-performance data team, how he measures the ROI on data activities, the skills needed in every successful data team, what is the best organizational model for data mature organizations, how Covid-19 affected Gojek’s data teams, his thoughts on data literacy and governance, future trends in data science and AI, and why data scientists should sharpen their maths and machine learning skills in an age of increasing automation. 

Relevant links from the interview:

We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second surveyGojek’s Data Blog

In this episode of DataFramed, we speak with Noah Gift, founder of Pragmatic AI Labs and prolific author about operationalizing machine learning in organizations and his new book Practical MLOPs. 

Throughout the episode, Noah discusses his background, his philosophy around pragmatic AI, the differences between data science in academia and the real world, how data scientists can become more action-oriented by creating solutions that solve real-world problems, the importance of dev-ops, his most recent book on the practical guide to MLOps, how data science can be compared to Brazilian jiu-jitsu, what data scientists should learn to scale the amount of value they deliver, his thoughts on auto-ml and automation, and more. 

Relevant links from the interview:

We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second surveyUnsettled: What Climate Science Tells Us, What It Doesn't, and Why It MattersCheck out Noah's booksCheck out Noah's course on DataCampConnect with Noah on LinkedInGain access to DataCamp's full course library at a discount!

In this episode of DataFramed, Adel speaks with Maria Luciana Axente, Responsible AI and AI for Good Lead at PwC UK on the state and future of responsible AI.Throughout the episode, Maria talks about her background, the differences & intersections between "AI ethics" and "Responsible AI", the state of responsible AI adoption within organizations, the link between responsible AI and organizational culture, what data scientists can do today to ensure they're part of their organization's responsible AI journey, and more. Relevant links from the interview:

Connect with Maria on LinkedInKate Crawford's Atlas of AI9 Ethical AI Principles for Organizations to FollowPwC's Responsible AI ToolkitRead our Data Literacy for Responsible AI White Paper

In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs,  an AI Observability company on a mission to build the interface between AI and human operators. Throughout the episode, Alessya talks about the unique challenges data teams face when operationalizing machine learning that spurred the need for MLOps, how MLOps intersects and diverges with different terms such as DataOps, ModelOps, and AIOps, how and when organizations should get started on their MLOps journey, the most important components of a successful MLOps practice, and more. 

Relevant links from the interview:

Connect with Alessya on LinkedInAndrew Ng on the important of being data-centricJoe Reis on the data culture and all things datawhylogs: the standard for data logging — please send you feedback, contribute, help us build integrations into your favorite data tools and extend the concept of logging to new data types. Join the effort of building a new open standard for data logging!Try the WhyLabs platform

In this episode of DataFramed, Adel speaks with Dan Becker, CEO of decision.ai and founder of Kaggle Learn on the intersection of decision sciences and AI, and best practices when aligning machine learning to business value.

Throughout the episode, Dan deep-dives into his background, how he reached the top of a Kaggle competition, the difference between machine learning in a Kaggle competition and the real world, the role of empathy when aligning machine learning to business value, the importance of decisions sciences when maximizing the value of machine learning in production, and more. 

Links:

Follow Dan on TwitterFollow Dan on LinkedInWhat 70% of data science learners do wrongCheck out Dan’s course on DataCampdecision.aiDan’s climate dashboard

Before the COVID-19 crisis, we were already acutely aware of the need for a broader conversation around data privacy: look no further than the Snowden revelations, Cambridge Analytica, the New York Times Privacy Project, the General Data Protection Regulation (GDPR) in Europe, and the California Consumer Privacy Act (CCPA). In the age of COVID-19, these issues are far more acute. We also know that governments and businesses exploit crises to consolidate and rearrange power, claiming that citizens need to give up privacy for the sake of security. But is this tradeoff a false dichotomy? And what type of tools are being developed to help us through this crisis? In this episode, Katharine Jarmul, Head of Product at Cape Privacy, a company building systems to leverage secure, privacy-preserving machine learning and collaborative data science, will discuss all this and more, in conversation with Dr. Hugo Bowne-Anderson, data scientist and educator at DataCamp.Links from the show

FROM THE INTERVIEW

Katharine on TwitterKatharine on LinkedInContact Tracing in the Real World (By Ross Anderson)The Price of the Coronavirus Pandemic (By Nick Paumgarten)Do We Need to Give Up Privacy to Fight the Coronavirus? (By Julia Angwin)Introducing the Principles of Equitable Disaster Response (By Greg Bloom)Cybersecurity During COVID-19 ( By Bruce Schneier)