Championing Diversity and Inclusion with Rochelle Richardson, VP of Data Transformation at TripAdvisor


Rochelle Richardson, VP of Data Transformation at TripAdvisor, is passionate about problem-solving and continuous learning. It is these interests that have led her career to where it is today. Starting her career at Xerox, Rochelle worked her way into process improvement roles, where she focused on utilizing data analytics to support engineering teams, introduce new products and ensure quality outcomes.  

Rochelle’s skills – supporting organizations which have engineers creating quality products to effectively deploy those products while listening to the end customer – have transformed the outcomes for several companies, and she is now employing her expertise at TripAdvisor. 

“I really wanted to work for an organization that was technology-based and provided a really cool mission out to our users. For me, it was great knowing it would be in a transformative state – I knew travel was not going to look the same in 2022 as it did in 2019. It’s been a really awesome opportunity.”  


Improving Diversity and Inclusion Within the Tech World 


Rochelle is passionate about improving representation from marginalized groups within tech, and has worked hard throughout her career to highlight ways that this can be done. Lending her skills to a research project by the Rochester Institute of Technology, Rochelle saw the importance of harnessing potential at a young age. The study uncovered how vital it is for children in the early years of elementary school to see the available opportunities and build the confidence needed to embark on that career. 

“We often say here at TripAdvisor that we want our teams to be representative of the of the community we’re serving. From an overall travel community, we’re serving all genders, all nationalities, all cultures, all you name it from an inclusion and diversity perspective, and specifically we’re trying to get women in to help bridge that gap. It’s super important to us, but it does go back to what is that pipeline we can draw from? And how do you kick that off early?” 

These conversations are vital for improving representation across tech – only through building that pipeline and harnessing the potential of young girls at an early age can we reach a stage where those working in tech reflect the world around us. 


Network, Network, Network 


For those new to the industry, Rochelle’s advice is to never underestimate the importance of networks. These could be networking groups within schools, companies or on social forums like LinkedIn. Finding a group of like-minded people in a similar position to you will help you as you navigate your career. This is true for people of all diverse and underrepresented groups. 

It is also really important to do your diligence when choosing the company you work for, being careful and selective and making sure that the fit is right for you. Looking for a supportive culture is key. 

“For young women coming into the tech space, working for an organization such as TripAdvisor, that is very focused on diversity and inclusion, is going to be a different experience than some of the other tech manufacturing organizations that are out there. So find the one that fits you as an individual.” 

Rochelle advises looking for a company that has that representation in terms of women, people of color, LGBTQ+ not just at the lower levels of the company but at board level too – because that speaks of a diverse culture. 


The Future at TripAdvisor 


It is easy for companies to become passive on the issue of diversity and inclusion – but that has never been the case for TripAdvisor, and they are actively working with colleges and universities to find and attract new talent from diverse groups in order to build that pipeline and continue to have balance as people evolve into higher roles. 

“We try to get that full, representational slate because for us it’s diversity of all things, of color, gender, LGBTQ+, etc. But it’s also diversity of thought. Reaching a little further out, casting a wider net, to try to get that and then have that balanced slate works really well.” 

In terms of data transformation, the opportunities at TripAdvisor are vast. The possibilities around harnessing the available data and using it to improve the business and drive quality improvements are huge – as well as the scope for personalization and monetization in terms of creating a highly individualized targeting program. 

“For those of us not Google, or Amazon, it’s how do you create that? All the data says, if we have a very personalized experience on any respective site, we will go back there. And that will be the place that we keep going. And that translates into the health of the site, the health of the business, but also a happier customer, which is the most important.” 

Over the next few years, TripAdvisor are focusing on global expansion, making this an exciting time to join the company with fantastic, creative tech projects to work on. With the opportunities for growth and development for women, people of color, LGBTQ+, those right across the gender spectrum, etc. Rochelle believes that TripAdvisor is a great choice for those looking for their first – or next – tech challenge. 

“It’s one of the top places in my mind to work from a global diversity perspective. Plus, we’ve got a new CEO, and we’ve got change happening. It’ll definitely be dynamic. There’s a lot happening.” 

Evo USA #19 – The Importance of BI and Analytics in Business

Host: Aimee Clemson
Guest Speakers:
Ben Huntley – Director of Data Engineering – IDEXX Laboratories
Chad Rose – CEO and Co-Founder – InsightOut
Eric Gonzalez – VP of BI Architecture – Eastern Bank
Chris Majka – Staff VP of BI – FM Global



Evo USA #18 – Leadership How to go from an IC to a leader

Host: Donnie Maclary
Guest Speakers:
Ben Dundee – Lead Director of Data Science – CVS Health
Anthony Renzette – Chief Product Officer – VERITUITY
Vinny Souza – Head of Data Science – Enact Mortgage Insurance
Himanshu Jain – Executive Director of AI and Data Science – CVS Health



The Transition from Data Science into Product Management

In the dynamic world of tech, where the borders between roles often blur, understanding the journey of transitioning from one role to another can be incredibly enlightening. This particularly holds true when the shift involves two highly specialized and critical roles: Data Science and Product Management. One such journey is that of Pushpesh Sharma, currently the Senior Product Manager at Aspen Technology. The intricate challenges he faced while transitioning from Data Science to Product Management offer invaluable insights into these two fascinating fields.


The journey wasn’t a straightforward one for Pushpesh, who was initially compelled to explore the realm of Product Management following an unanticipated shift in his career trajectory. His experiences present an intriguing tale of adapting to change, discovering new intersections between skillsets, and navigating the complexities of divergent yet interconnected roles. In the face of these challenges, Pushpesh not only found a new direction but also helped shape an innovative approach towards Product Management, where his data science expertise played a pivotal role.


Aimee Clemson, Principal Consultant of Data Engineering, had the privilege of sitting down with Pushpesh to delve deeper into his fascinating journey. Engaging and insightful, this conversation provides an exceptional view into the challenges and rewards of transitioning between these two key roles within the tech industry. Get ready to gain a fresh perspective as Aimee draws out Pushpesh’s experiences, learnings, and insights into this multifaceted career transition. Join us in this intriguing dialogue that promises to be rich in wisdom for both data scientists and product managers alike.



Q. Why did you move into product management?

A. Initially, I found myself compelled to transition roles due to circumstances. My career began as a Senior Research Data Scientist with a startup, but when the startup faced difficulties, I was necessitated to explore different dimensions of roles that I could excel in. That’s when I stumbled upon Product Management and realized that it was a compelling intersection of my previous work as a data scientist and my industry experience.

In fact, I believe that having expertise in a specific industry, when combined with data science skills, creates an ideal blend for a product management role. Recognizing this perfect combination led me to pivot and start building my career in that direction. This path allowed me to utilize my strengths while expanding my horizon into the realm of product management.


Q. If we’re considering individuals from the data science realm who are contemplating a shift into product management, what would you say are the key commonalities and differences between the two roles?

A. Indeed, the central element shared between a data scientist and a product manager is data. As a product manager, my primary focus is data — observing trends, understanding product usage, and discerning user preferences. In essence, we’re looking at the same type of data, conducting similar analysis as data scientists, but we apply it differently, connecting it to the various features we aim to develop for our products.

In this capacity, a product manager essentially acts as an interpreter between data scientists and the rest of the team. We are responsible for decoding the data trends, listening to what the customers are explicitly saying, and more importantly, understanding what they aren’t saying

In many ways, this aligns with the data scientist’s role. As a data scientist, one studies various data trends and attempts to create models explaining specific behaviors. This task closely mirrors what a product manager does.

However, from a perspective of differences, a product manager’s focus is predominantly on the customer and usability, contemplating how the product will be used. On the other hand, a data scientist’s focus leans more towards performance – speed, efficiency, and accuracy.

Interestingly, from a customer’s perspective, achieving 99.99% accuracy isn’t as crucial as improved usability and explainability. So, while there are numerous commonalities between the two roles, the final objectives differ, shaping the unique focus of each role.


Q. Given this perspective, how did you determine that this career path was the best fit for you?

A. The decision to transition into product management was a highly personal one for me. My background in the energy industry and self-taught data science skills led me to believe a role that amalgamates these two elements would be a perfect fit. However, this may not hold true for everyone.If you are someone who enjoys customer interactions, who examines any product you use critically, identifying its strengths and weaknesses, a product management role might be a good fit for you. On the other hand, if you have a penchant for hands-on work, or a deep interest in enhancing the workings of algorithms, a data scientist role may better align with your interests.

Ultimately, the path you choose should align with your career goals. I often express my belief that a product manager might have a broader range of opportunities for upward mobility. This is not to undermine the data science career track, which is firmly established and well-regarded.

However, as a product manager, as you start interacting with customers, you may discover opportunities to branch out into sales or marketing, among other avenues. In contrast, a data scientist’s career path tends to follow a more conventional trajectory. The choice largely depends on your personal preferences and career ambitions.

Q. Given your background in data science, do you believe that it provided you with an edge in the realm of product management? Would you say that it gives an advantage over those who may not have had such an experience, or is it simply a different perspective?

A. Absolutely, I believe that having a data science background is indeed an advantage, particularly considering the current trajectory of the industry. As more and more companies incorporate elements of AI or machine learning into their products, understanding how these systems work and how models are constructed can better equip you to address customer needs.

This nuanced understanding allows for more direct connections with the customer, as opposed to a generalized understanding. It is particularly beneficial in this era where businesses across sectors are keen on exploring machine learning and its various facets. The knowledge you gain in data science will be instrumental in understanding how things work, which in turn, aids in making informed decisions.

Additionally, this background enhances your ability to conceive use cases. As a data scientist, you often delve into workflow designs and the thought processes behind them. This understanding allows you to better guide customers or end-users on how to utilize the product or service effectively. Moreover, if you identify a discrepancy, you can also rectify it.

Given these advantages, I’ve noticed that many product managers, including myself, transition from roles like developers or data scientists to product management. This shift often turns out to be a seamless and beneficial transition.


Q. You’ve partially addressed this already, but could you elaborate on how, from a data scientist’s perspective, one can truly excel in the role of a product manager?

A. From a data scientist’s perspective, we’re already adept at analyzing data, comparing various models, and assessing their efficiency and performance. The key is to elevate this perspective to understand that most products aren’t used by experts, but by generalists. In one field, we might be specialists, but we become generalists when it comes to other areas, and that’s where we need to bridge the gap.

The more interaction we have with users and other experts, the better we comprehend how our product or model will be utilized. The crux of the matter lies in a shift of perspective. We need to put ourselves in the shoes of the end-user and contemplate how we would use the product ourselves. However, it’s important to be mindful of and attempt to avoid certain biases that can creep in during this process. For instance, as a product manager, I might have a specific vision of how a product should be used based on my own biases. Yet, in real scenarios, customers or end-users often use products in completely unanticipated ways.

From an engineering standpoint, it might be tempting to resist these unexpected user behaviors and insist on ‘correct’ usage. However, learning to accept and incorporate user feedback into the product development process can enhance the product’s utility and streamline its use. It’s about understanding the real-world usage of the product, not just how we envision it to be used.


Q. Could you elaborate on what a typical day might look like for a data scientist versus a product manager? I understand we’ve discussed this a bit, but some additional details would be appreciated.

A. Indeed, the daily routines of a data scientist and a product manager differ significantly. From a data scientist’s viewpoint, the day often begins with a scrum meeting where we assess our progress on certain projects and define deliverables.

In contrast, as a product manager, my day typically starts by analyzing the usage data of my product. I try to discern if there have been any shifts in usage patterns or if there are any new insights to be gained. A key distinction here is that as a product manager, you don’t typically interact with end users directly. Instead, your conversations are more with product owners or other product managers, who essentially serve as customer proxies.

Despite this, I still find myself using a lot of the tools I used as a data scientist. Python and various data dashboards remain a large part of my work, aiding in data enrichment and analysis. Therefore, while the skills you acquire as a data scientist are still applicable in product management, the latter offers a broader perspective on the product and its future. Conversely, as a data scientist, your focus is more on execution and deliverables based on the defined vision.


Q. We’ve discussed the general idea of product management, but could you elaborate on the distinction between a product manager in this broader sense and the role of a technical product manager?

A. Indeed, the role of a product manager, in general, my VP of Product (Sonali Singh) often refers to as encompassing the ‘5Ds’ – Discover, Design, Develop, Deploy and Deliver. As a product manager, you engage with each of these phases evenly. The Discover phase involves customer interaction and interviews to uncover new improvements. The Design phase entails collaborations with UI/UX teams to determine how the product can be delivered. The Develop phase sees you liaising with your data science team on product or feature development. Finally, the Deploy and deliver phases involves you strategizing go-to-market plans with the sales team.

On the other hand, a technical product manager places a more significant emphasis on the Develop phase. Technical product managers are expected to work more closely with R&D teams, essentially having a more hands-on approach towards product development. So, in the role of a technical product manager, you’re likely to dedicate about 30 to 40% of your time to the Develop phase. The Discover and Design stages remain important, but the emphasis shifts more towards development.


Q. And finally, I’m curious about your personal career trajectory. You’ve experienced both data science and product management. Do you ever envision yourself returning to data science, or do you believe product management is your long-term professional path?

A. In contemplating my professional future, I could see myself gravitating towards the role of a technical product manager, given my deep passion for programming and coding. While I still cherish the broader scope and impact of a product management role, I value the prospect of engaging more hands-on tasks. My academic and professional background in the energy sector aligns well with these roles, fostering confidence and competence in this space. However, I understand this is a subjective choice and may vary for different individuals. Personally, I foresee myself continuing on the path of product management or possibly evolving into a technical product manager role in the future.


Q. We’ve discussed this in our previous interactions, but as we approach the conclusion of our conversation, I would love for you to share the engaging story of how you came to learn Python. Your journey provides such an interesting narrative that I believe our audience would genuinely appreciate.

A. Sure, I’d love to recount that story. It indeed carries an interesting twist. Back in 2017, Houston faced a substantial hurricane, Hurricane Harvey, which is still etched in my memory. At that time, I was engrossed in my PhD journey, which was centered more around chemical engineering and hands-on lab experiments. However, buzzwords like Python and machine learning had been circulating, stirring my curiosity.

Knowing in advance that the school would remain closed for about a week due to the hurricane, and being bound to stay indoors, I decided to use this time effectively. I embarked on a self-learning spree, mainly utilizing YouTube to take Python courses. In seven days, I managed to code a small project. This project eventually found its way into a final semester assignment at school.

The twist in the tale came when the professor for that course recommended me to a startup seeking someone experienced in both the energy sector and data science. In hindsight, it seems like finding a silver lining in a dire situation. As the saying goes, “Every cloud has a silver lining”, and for me, Hurricane Harvey offered the time and opportunity to kick-start my journey in Python and data science.



Aimee — Yeah. Well, you could say you owe your career to the hurricane. It’s definitely finding the Silver Lining.

Pushpesh — Yeah, absolutley.

Aimee — Brilliant. Alright. Well, thank you so much for running through that Pushpesh. That was that was really interesting. Thank you so much!

Pushpesh — Thank you.


Pushpesh’s story is a testament to adaptability and passion for lifelong learning. It paints a vivid picture of how fields intertwine, where expertise in one domain can significantly benefit roles in another. His unusual path, kick-started during the days of Hurricane Harvey, showcases the blend of determination and opportunity. We hope his narrative will inspire those exploring a similar path and shed light on the intriguing complexities and rewards that come with such a transition.


If you feel inspired by Pushpesh’s journey and would like to delve deeper into this topic, please don’t hesitate to reach out to Aimee. She would be more than happy to discuss and offer further insights.

The Importance of Data Engineering and Analytics to Sports Teams

Sports in America have always been a popular and integral part of society. With millions of fans following various sports and teams, there is a vast amount of data generated from each game, practice, and player. However, it is not enough to have just raw data; it needs to be collected, analyzed, and turned into insights that can be used to improve teams’ performance and even shape future games.

This is where data engineering comes in, which is the process of collecting, transforming, and storing data in a way that allows for effective analysis and use. By leveraging data engineering techniques, sports teams and organizations can gain a competitive edge and advance American sports.

One way that data engineering can be used in sports is through player performance analysis. By collecting data on player movements, ball trajectories, and other game statistics, teams can identify strengths and weaknesses in individual players’ performances. This data can then be used to develop personalized training plans for each player, leading to improved performance on the field or court.

Another area where data engineering can be applied is in the analysis of game strategy. By analyzing past games and identifying patterns in gameplay, teams can develop effective strategies that take into account their strengths and weaknesses. This can lead to more efficient use of resources and increased success on the field.

Data engineering can also be used to improve fan engagement. By collecting and analyzing data on fan behavior, teams can create personalized experiences for fans and provide them with more relevant and engaging content. This could include personalized merchandise suggestions, targeted marketing campaigns, and even interactive experiences at the stadium.

Overall, the application of data engineering in American sports has the potential to revolutionize the way teams operate and interact with fans. By using data to make informed decisions, teams can optimize player performance, develop more effective game strategies, and provide fans with personalized experiences that keep them engaged and coming back for more.

As the world becomes increasingly data-driven, the role of data engineering in sports will only continue to grow. It will be interesting to see how sports teams and organizations leverage this technology to push the boundaries of what is possible on the field and court.

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