Building data science functions has become tables takes for many organizations today. However, before data science functions were needed, the finance function acted as the insights layer for many organizations over the past. This means that working in finance has become an effective entry point into data science function for professionals across all spectrums.

Brian Richardi is the Head of Finance Data Science and Analytics at Stryker, a medical equipment manufacturing company based in Michigan, US. Brian brings over 14 years of global experience to the table. At Stryker, Brian leads a team of data scientists that use business data and machine learning to make predictions for optimization and automation.

In this episode, Brian talks about his experience as a data science leader transitioning from Finance, how he utilizes collaboration and effective communication to drive value, how leads the data science finance function at Stryker, and what the future of data science looks like in the finance space, and more.

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 third episode of this four-part DataFramed Careers series being published every day this week on building a career in data. We’ve heard from Nick Singh on the importance of portfolio projects, as well as the distinction between content-based and coding-based portfolio projects. When looking to get started with content-based projects, how do you move forward with getting yourself out there and sharing the work despite being a relative beginner in the field?Today’s guest tackles exactly this subject.

Khuyen Tran is a developer advocate at prefect and a prolific data science writer. She is the author of the book “Efficient Python Tricks and Tools for Data Scientists” and has written 100s of blog-articles and tutorials on key data science topics, amassing thousands of followers across platforms. Her writing has been key to accelerating here data career opportunities. Throughout the episode, we discuss:

How content creation accelerates the careers of aspiring practitioners The content creation process How to combat imposter syndrome What makes content useful Advice and feedback for aspiring data science writers  

Resources mentioned in the episode:

Analyze and Visualize URLs with Network Graph Show Your Work by Austin Cloud Mastery by Robert Greene Deep Questions with Cal Newport Podcast  

[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 second episode in our DataFramed Careers Series. In this series, we will interview a diverse range of thought leaders and experts on the different aspects of landing a data role in 2022.

In the first episode of the series, Sadie discussed at great length the importance of having a solid data science portfolio to land a role in data. But what makes a great data science portfolio?

Nick Singh, co-author of Acing the Data Science Interview, joins the show to share everything you need to know to create high-quality, thorough portfolio projects.

Throughout the episode, we discuss

How portfolio projects build experience Who should be focusing on portfolio projects The different types of portfolio projects Biggest pitfalls when creating portfolio projects How to get noticed with your portfolio projects Concrete examples of great portfolio projects 

[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/

Introducing the DataFramed Careers Series. Over the past year hosting the DataFramed podcast, we've had the incredible privilege of having biweekly conversations with data leaders at the forefront of the data revolution. This has led to fascinating conversations on the future of the modern data stack, the future of data skills, and how to build organizational data literacy. 

However, as the DataFramed podcast grows, we want to be able to provide the data science community across the spectrum from practitioners to leaders, with distilled insights that will help them manoeuvre their careers effectively. And we want to do that more often. 

This is why we’re excited to announce the launch of a four-day DataFramed Careers Series. Throughout next week, we will interview four different thought leaders and experts about what it takes to break into data science in 2022, best practices to stand out from the crowd, building a brand in data science, and more. Moreover, this episode series will mark DataFramed’s transition from biweekly to weekly.

Starting Monday the 30th of May, DataFramed will become a weekly podcast.

For next week’s DataFramed Careers Series, we’ll be covering the ins and outs of building a career in data, and the different aspects of standing out from the crowd during the job hunt. We’ll be hearing from Sadie St Lawrence, CEO and Founder of Women in Data on what it takes to launch a data career in 2022. Nick Singh, Co-author of Ace the Data Science Interview and 2nd time guest of DataFramed will join us to discuss what makes a great data science portfolio project. Khuyen Tran, Developer Advocate at Prefect on will outline how writing can accelerate a data career, and Jay Feng, CEO of Interview Query will join us to provide tips and frameworks on acing the data science interview.

For future DataFramed episodes, we’ll definitely still cover the different aspects of building a data-driven organization, cover the latest advancements in data science, building data careers, and more. So expect more varied guests, topics, and more specials series like this one in the future.

The data journey is a slow painstaking process. But knowing where to start and the areas to focus on can help any organization reach its goals faster.

Today’s guest, Vijay Yadav, Director of Quantitative Sciences & Head of Data Science at the Center for Mathematical Sciences at Merck, explains the 6 key elements of data strategy, complete with advice on how to navigate each.

Join us as we discuss:

The different components of a data strategy Shifting mindset within the C-Suite Structuring the operating model Enabling people to work with data at scale Most effective tactics to kickstart a community around data science

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.

It’s no secret that data science jobs are on the rise; but data skills across the board are rising — leading to what today’s guest calls “hybrid jobs.”

This will require a paradigm shift in how we think about jobs and skills.  

Today’s guest, Matt Sigelman, President of The Burning Glass Institute & Chairman of Emsi Burning Glass, talks about the difficulties of connecting companies with top talent, the hybridization of many positions, and how to position yourself in the ever-changing market. 

Join us as we discuss:

The methodology of using data science on the labor market The demand for data skills & how they’re evolving Blending skills to get ahead in the job market & the rise of subskills How educational institutions can prepare students for hybridization  Advice to the audience on how to structure their approach to skill acquisition 

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.

When most people hear digital transformation, it’s almost always the technology that first springs to mind.

That’s a mistake.

You can have the most sophisticated tech stack in the world, but if you don't build your organization’s data culture, your digital transformation efforts will be for naught.

Today’s guest, Mai AlOwaish, Chief Data Officer at Gulf Bank, knows this better than anyone. As the first female CDO in Kuwait, she’s on a mission to ensure everyone at Gulf Bank becomes an expert in the data they use every day.

Join us as we discuss:

Why data and people are more important than technology for digital transformationThe pioneering Data Ambassador program Mai spearheaded at Gulf BankThe importance of diversity in data science and technology overall

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. 

As we enter the new year—it seems like we’re telescoping into the future of work. Companies embracing remote work, the great resignation putting pressure on teams to create more fulfilling roles—signals an expanding opportunity for applicants to find their dream roles in data science, but also for hiring managers to create awesome candidate experiences. 

Today’s guests, Nick Singh, and Kevin Huo, authors of Ace The Data Science Interview, discuss how aspiring data scientists and data scientists can stand out from their crowd—and what hiring managers need to change to win over talent today. 

Join us as we discuss:

How to wow recruiters and hiring managers with your resumeThe type of skills aspiring data scientists need to show on the job huntThe value of direct email over job listingsWhat recruiters and hiring managers need to change in an evolving job market

Relevant links from the interview:

Ace the Data Science InterviewFollow Nick Singh on LinkedInFollow Kevin Huo on LinkedInNoah Gift’s Appearance on DataFramedSign up to gain early access to gain DataCamp Talent—DataCamp’s portal for data science jobs

In this episode of DataFramed, we speak with Vishnu V Ram, VP of Data Science and Engineering at Credit Karma about how data science is being leveraged to increase financial inclusion.

Throughout the episode, Vishnu discusses his background, Credit Karma’s mission, how data science is being used at Credit Karma to lower the barrier to entry for financial products, how he managed a data team through rapid growth, transitioning to Google Cloud, exciting trends in data science, and more. 

Relevant links from the interview:

You can now learn data science with your team for free—try out DataCamp Professional with our 14-day free trial. Data roles at Credit KarmaCredit Karma’s mission

In this episode of DataFramed, we speak with Brian Campbell, Engineering Manager at Lucid Software about managing data science projects effectively and harnessing the power of collaboration. Throughout the episode, Brian discusses his background, how data leaders can become better collaborators, data science project management best practices, the type of collaborators data teams should seek out, the latest innovations in the data engineering tooling space, 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 surveyLucid’s Tech Blog

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, we speak with Rick Scavetta and Boyan Angelov about their new book, Python and R for the Modern Data Scientist: The Best of Both Worlds, and how it dawns the start of a new bilingual data science community.  

Throughout the episode, Rick and Boyan discuss the history of Python and R, what led them to write the book, how Python and R can be interoperable, the advantages of each language and where to use it, how beginner data scientists should think about learning programming languages, how experienced data scientists can take it to the next level by learning a language they’re not necessarily comfortable with, 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 Rick and Boyan’s bookCheck out Rick’s courses on DataCampCheck out Boyan's other booksConnect with Rick on LinkedInConnect with Boyan on LinkedIn

In this episode of DataFramed, Adel speaks with Elad Cohen, VP of Data Science and Research at Riskified on how data science is being used to combat fraud in eCommerce.Throughout the episode, Elad talks about his background, the plethora of data science use-cases in eCommerce, how Riskified builds state-of-the-art fraud detection models, common pitfalls data teams face, his best practices gaining organizational buy-in for data projects, how data scientists should focus on value, whether they should have engineering skills, and more.

Relevant links from the interview:

Connect with Elad on LinkedInRegister for our upcoming webinarsHow Riskified chooses what to research

In this episode of DataFramed, Adel speaks with Sergey Fogelson, Vice President of Data Science and Modeling at Viacom on how data science has evolved over the past decade, and the remaining large-scale challenges facing data teams today.

Throughout the episode, Sergey deep-dives into his background, the various projects he’s been involved with throughout his career, the most exciting advances he’s seen in the data science space, the largest challenges facing data teams today, best practices democratizing data, the importance of learning SQL, and more. 

Relevant links from the interview:

Connect with Sergey on LinkedInCheck out Sergey’s course on DataCampLearn more about AirflowLearn more about PySparkLearn more about SQL

More resources from DataCamp

Upskill your team with DataCampOur Guide on Open Source Software in Data ScienceYour Organization’s Guide to Data Maturity

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

In this episode of DataFramed, Adel speaks with Amen Ra Mashariki, principal scientist at Nvidia and the former Chief Analytics Officer of the City of New York on how data science is done in government agencies, and how it's driving smarter cities all around us. 

Throughout the episode, Amen deep-dives into the use-cases he worked on to make the city of New York smarter, how data science allows cities to become more reactive and proactive, the unique challenges of scaling data science in a government setting, the friction between providing value and data privacy and ethics, the state of data literacy in government, and more. 

Links from the interview:

Follow Amen on LinkedInFollow Amen on TwitterThe New York City Business AtlasHurricane Sandy FEMA After-Action ReportData Drills

We are super excited to be relaunching the DataFramed podcast. In this iteration of DataFramed, Adel Nehme, a data science educator at DataCamp, will uncover the latest thinking on all things data and how it’s impacting organizations through biweekly (once every two weeks) interviews and conversations with data experts from across the world. 

Check out this snippet for a preview of what’s to come and for a short chat with DataCamp’s CEO Jonathan Cornelissen on where he thinks data science is headed and the major challenges facing data teams today. 

Links:

For the rest of April, get free access to DataCamp.Get involved with DataCamp Donates

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)