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I Became My Own Nutrition Coach
Women in STEM, Voice of Our People, Impact
“It’s about receiving guidance from nutrition coaches on maintaining a healthy, personalized diet in life.”
I Became My Own Nutrition Coach
Dan W.
Dan W. is a Principal Data Scientist who supports sales and marketing initiatives across different businesses. Her team provides data insights, builds predictive models, and turns them into actionable information to support business decisions. This is her seventh year working at ADP! Dan came for the opportunity and stayed for the people. As an immigrant from a foreign country, she feels supported and looks forward to inspiring other women technologists with her story.
My ADP journey began in 2015 when I worked as a business intelligence manager in worldwide sales and marketing. I built predictive models and conducted deep analysis supporting all business units. My team then moved to Global Product & Technology (GPT), where I got promoted to my current position as the principal data scientist. I’m always proud to build impactful models for solving real-life business problems.
I’ve come a long way as a woman technologist who became her own certificated nutrition coach.
I know what it’s like to feel overwhelmed and uncertain about where to start when it comes to making positive changes for your health. Throughout my journey, I experienced rewarding feelings of finding the healthy mindset and approaches that worked for my body. In this blog, I’ll share my story of how I became a certified nutrition coach.
Nine years ago, I was diagnosed with autoimmune disease. I suffered from chronic inflammation and symptoms of metabolic syndrome. Doctors told me I would be on medication for the rest of my life, and the news struck me. I felt defeated and did not want to rely on medicine. After connecting with a friend who teaches pharmacy and a professor who studies nutrition, I received support and learned about what nutrition human bodies need to stay healthy.
It was a long discovery process when I spent time on myself, monitoring both physical and mental health. I am incredibly grateful for my church community and family members who encouraged me to dive deeper into different learning opportunities. I decided to participate in the weight-loss program while enrolling in two nutrition certificate programs.
I first took weekly seminars from NutraMetrix Educational Institute and went through in-person training by health professionals every other year to get recertified. To gain more experience working with different clients’ needs, I completed another two-year Family Wellness Coach program at Whealkon Nutraceutical College and graduated in December 2021.
After hundred hours of training, I became a certified nutrition coach. Not only did this decision change my life, but also it gave me an opportunity to support others in need. As a nutrition coach, I remind myself, my clients, and my family of three best practices to achieve wellness goals:
1) Practice Healthy Eating Habits
Ask yourself: why do you want to achieve these health goals?
The diet changes start in daily behaviors. I help people understand their goals and have conversations beyond exercising and nutrition, including sleep schedules and how they feel about their lifestyles.
2) One Thing at a Time
It’s impossible to see immediate changes overnight. I find asking diet-related questions in systems helpful. As a coach, I switch focuses between the food quality and the quantity of each meal, depending on the client’s health condition.
3) There is No “Best Diet”
Every case is different. My goal is to find what works the best for everyone, making individuals feel strong and healthy based on the diet approach they choose to pursue.
I provided customized wellness coaching and weight management consulting in a family doctor’s clinic before the pandemic. It feels amazing to contribute to the community, including running wellness seminars, hosting 12-week weight loss programs, and providing 1-1 nutritional consultation. I plan to host more in-person events in the future!
Dan and Her Family
In my six years of practice, I encountered patients with common health problems such as diabetes, high blood pressure, and high cholesterol. One can overcome these conditions with weight-loss training while maintaining a healthy diet. Coming up with customized nutritional goals for them always brings me joy as I see their health conditions improve, building friendship and support along the way. If you are interested in learning more about what nutrition coaches can do, you may find this article helpful: How are Health Coaches Trained and Certified? Is Hiring a Health Coach Right for You?
Looking back, working as a data scientist has prepared me with essential skills in pursuing nutritional health. The common ground in both roles is excellent communication, efficient negotiation, and customized analysis.
In our current world, we see artificial intelligence (AI) everywhere as people adapt to their digital footprints. It makes our lives easier by speeding up communication across nations and time zones. As a data scientist supporting sales and marketing, I encourage associates to learn teams’ needs and strategically develop a plan to fit those needs. My advice for those new to the field is to focus on gaining experience in analytical and communication skills. They are also essential as I switch roles, working with different groups of people.
I used to be “shy” in starting conversations, but my experience as a data scientist has allowed me to take in stories through a new lens. I practice the same mindset in working as a nutrition coach, stepping out of my comfort zone to conduct health seminars at the clinic. Working with patients has also improved my presentation skills, which I can apply to the tech workforce.
It’s about receiving guidance from nutrition coaches on maintaining a healthy, personalized diet in life.
Regarding my health condition, I am happy to say I no longer need medication. I focus on making intelligent decisions in healthy eating behaviors and taking responsibility for my health goals. I am proud of my journey and will continue to help others in need, especially women who suffered from fatigue during the pandemic.
When giving nutrition advice, I am mindful of people’s financial situations. The same thought process applies to working as a data scientist, analyzing circumstances for different clients. Both roles build my confidence in identifying the needs and proposing customized plans after assessing them.
As a data scientist, I compare solutions and propose the best ways to reach business goals in given timelines. Mentoring new data scientists and identifying their needs has been a wonderful experience. I see myself continuing practicing analytical and interpersonal skills in tech and in nutrition coaching, achieving both health and career goals.
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AI and Data Ethics: 5 Principles to Consider
As organizations develop their own internal ethical practices and countries continue to develop legal requirements, we are at the beginning of determining standards for ethical use of data and artificial intelligence (AI).
In the past 20 years, our ability to collect, store, and process data has dramatically increased. There are exciting new tools that can help us automate processes, learn things we couldn’t see before, recognize patterns, and predict what is likely to happen. Since our capacity to do new things has developed quickly, the focus in tech has been primarily on what we can do. Today, organizations are starting to ask what’s the right thing to do.
This is partly a global legal question as countries implement new requirements for the use and protection of data, especially information directly or indirectly connected to individuals. It’s also an ethical question as we address concerns about bias and discrimination, and explore concerns about privacy and a person’s rights to understand how data about them is being used.
What is AI and Data Ethics?
Ethical use of data and algorithms means working to do the right thing in the design, functionality, and use of data in Artificial Intelligence (AI).
It’s evaluating how data is used and what it’s used for, considering who does and should have access, and anticipating how data could be misused. It means thinking through what data should and should not be connected with other data and how to securely store, move, and use it. Ethical use considerations include privacy, bias, access, personally identifiable information, encryption, legal requirements and restrictions, and what might go wrong.
Data Ethics also means asking hard questions about the possible risks and consequences to people whom the data is about and the organizations who use that data. These considerations include how to be more transparent about what data organizations have and what they do with it. It also means being able to explain how the technology works, so people can make informed choices on how data about them is used and shared.
Why is Ethics Important in HR Technology?
Technology is evolving fast. We can create algorithms that connect and compare information, see patterns and correlations, and offer predictions. Tools based on data and AI are changing organizations, the way we work, and what we work on. But we also need to be careful about arriving at incorrect conclusions from data, amplifying bias, or relying on AI opinions or predictions without thoroughly understanding what they are based on.
We want to think through what data goes into workplace decisions, how AI and technology affect those decisions, and then come up with fair principles for how we use data and AI.
What Are Data Ethics Principles?
Ethics is about acknowledging competing interests and considering what is fair. Ethics asks questions like: What matters? What is required? What is just? What could possibly go wrong? Should we do this?
In trying to answer these questions, there are some common principles for using data and AI ethically.
- Transparency – This includes disclosing what data is being collected, what decisions are made with the assistance of AI, and whether a user is dealing with bots or humans. It also means being able to explain how algorithms work and what their outputs are based on. That way, we can evaluate the information they give us against the problems we’re trying to solve. Transparency also includes how we let people know what data an organization has about them and how it is used. Sometimes, this includes giving people an opportunity to have information corrected or deleted.
- Fairness – AI doesn’t just offer information. Sometimes it offers opinions. This means we have to think through how these tools and the information they give us are used. Since data comes from and concerns humans, it’s essential to look for biases in what data is collected, what rules are applied, and what questions are asked of the data. For example, if you want to increase diversity in hiring, you don’t want to only rely on tools that tell you who has been successful in your organization in the past. This information alone would likely give you more of the same rather than more diversity. While there is no way to completely eliminate bias in tools created by and about people, we need to understand how the tools are biased so we can reduce and manage the bias and correct for it in our decision making.
- Accuracy – The data used in AI should be up to date and accurate. And there needs to be ways to correct it. Data should also be handled, cleaned, sorted, connected, and shared with care to retain its accuracy. Sometimes taking data out of context can make it appear misleading or untrue. So accuracy depends partly on whether the data is true, and partly on whether it makes sense and is useful based on what we are trying to do or learn.
- Privacy – Some cultures believe that privacy is part of fundamental human rights and dignity. An increasing number of privacy laws around the globe recognize privacy rights in our names and likeness, financial and medical records, personal relationships, homes, and property. We are still working out how to balance privacy and the need to use so much personal data. Law makers have been more comfortable allowing broader uses of anonymized data than data where you know, or can easily discover, who it’s about. But as more data is collected and connected, questions arise about how to maintain that anonymity. Other privacy issues include security of the information and what people should know about who has data about them and how its used.
- Accountability – This is not just compliance with global laws and regulations. Accountability is also about the accuracy and integrity of data sources, understanding and evaluating risks and potential consequences of developing and using data and AI, and implementing processes to make sure that new tools and technologies are created ethically.
As organizations develop their own internal ethical practices and countries continue to develop legal requirements, we are at the beginning of determining standards for ethical use of data and AI.
ADP is already working on its AI and data ethics, through establishing an AI and Data Ethics Board and developing ethical principles that are customized to ADP’s data, products and services. Next in our series on AI and Ethics, we will be talking to each of ADP’s AI and Data Ethics Board members about ADP’s guiding ethical principles and how ADP applies those principles to its design, processes, and products.
Read our position paper, “ADP: Ethics in Artificial Intelligence,” found in the first blade underneath the intro on the Privacy at ADP page.
Tech & Innovation Blog
Why I think ADP is one of the best places for ML engineers and Data Scientists
Why ADP, ML and Data Science, Careers
When first asked to write an article for ADP’s tech blog, I had flashbacks to working on my dissertation, and it was, to put it delicately, one of my worst nightmares.
I mean, don’t get me wrong, I am proud of my work and forever thankful to my advisors for pushing me, but writing is not one of my natural abilities. Nevertheless, the request came at a rather interesting time for me, so I said yes. But let me take a step back.
“One day, the AIs are going to look back on us the same way we look at fossil skeletons on the plains of Africa. An upright ape living in dust with crude language and tools, all set for extinction.”
From one of my all-time favorite movies, that quote has been stuck in my head for more than half a decade. The first time I heard it, the quote resonated with the young geek in me and triggered my curiosity and desire to understand Artificial Intelligence (AI). That, in turn, pushed me to pursue a master’s degree and kickstart a career as an ML engineer. My years of research taught me that we are far from AI overlords, but the quote changed the lens with which I view the world.
So, I mentioned above that the request to write this article came at an interesting time for me. Why? I’m currently building a language model that can write meaningful phrases and sentences—as if written by a human being (where was this when I was writing my dissertation?!)
Natural Language Generation captured my interest at ADP when I discovered all the time and effort our client service associates put into crafting documents for our clients. I asked myself, “If we’re building machines to converse with us, why can’t we have them write for us, too?” Not only would that yield consistency in the quality and tone of our client responses, but for people like me, it may reduce an associate’s angst over a potentially time-consuming task and improve job satisfaction. That sounded like a win-win.
As I worked on the model, a friend joked that I was probably wasting my time on a project that my organization may never adopt. I disagreed. I’m blessed to work for wonderful, supportive leaders. Since I started at ADP, both my director and vice president have always encouraged me to challenge the status quo. Did I always succeed? Nope, but they created a safe space where I could take risks. Sometimes I fail, and that’s OK. It’s worth it to try.
I started working for ADP’s Retirement Services organization almost two years ago, thanks to a fantastic director who believed in me and gave me an opportunity despite my minimal experience. It was at a time when ADP ambitiously sought to build AI-centric products to make our client experience better. As a budding ML engineer, this was my happy place.
Although ADP has been around for over seven decades, a few years ago, we refocused on incorporating AI into our core strategy. This shift presented engineers with Machine Learning and Data Science backgrounds a unique opportunity. Sadly, for my peers at other companies, things they tell me they often face are a lack of opportunity, lack of problems to solve, and a limited scope due to the maturity of their company systems. You won’t find those things here.
We are still in an evolving space and actively innovating, which creates a ton of opportunity. I may be biased, but I think ADP is one of the best places for ML engineers and data scientists that love to innovate to grow their careers. Why? Besides a strong support system from senior leadership, we have a corporate focus to infuse AI into our products along with an unending stream of potential products and solutions to create.
Some parts of our company are still in the nascent stages of leveraging machine learning to improve our products. You may not find a lot of opportunities to build products from the ground up (although we are working on several!) inside a Fortune 500 company like ADP, but many also don’t have what we uniquely offer. ADP pays over 20% of the working population in the United States, giving ML engineers and data scientists a rare chance to work with some of the industry’s biggest datasets.
As an ADP ML engineer, I get the best of all worlds. I get to research and implement solutions for relevant problems and issues that impact the working world. For example, my team is currently tackling one of the biggest financial challenges in the country: retirement preparedness. We’re using comprehensive datasets from different organizations to enable us to teach people better financial planning habits and demonstrate the impact of those lessons on their financial future. I love to say we are, “Helping America Retire Better.” Every extra year of planned retirement that we deliver to people makes me happy. Impacting people’s lives through my work is what motivates me to come to work every day.
But it’s not all rainbows and unicorns. This article wouldn’t be complete and would be slightly disingenuous if I didn’t talk about the challenges. Let’s be realistic. Everyone faces challenges at work.
One problem I see is that people love the hyper buzzwords: AI, Machine Learning, Deep Learning, Data Science, oh my! But often, people don’t always see the value in the ideation phase. One of the great things about ADP is our culture of encouraging innovation that helps engineers move forward. Yes, maybe there were times people were wary of an idea, but no one ever discouraged me from working on a proof of concept.
Another challenge has to do with our scale, which is sometimes a blessing and a curse for ADP. Because of it, we need to work with teams across the organization and deal with conflicting opinions and priorities. Leaning into our core value of working as “One ADP,” many times, this helps us to resolve these issues, but it might take a few less-than-fun meetings or calls. These challenges can sometimes be annoying, and they take resilience to navigate through, but thanks to my amazing team and leadership support, I’ve never felt helpless or demotivated.
So, what do you say? Does this sound like a place for you? I’ll end by simply saying: give us a try. Apply and interview. I promise, once you meet us, you’ll understand why people stick around for a long time. I mean a really long time. Some of the smart and awesome engineers I work with had the pleasure of seeing the original Star Wars…in the movie theatre (no, I mean the first time!). Our multigenerational workforce is one of the things that makes this place culturally rich and diverse, but no less fun.
Ciao!
PS: The natural language model I’ve been working on wrote this article, so I hope you enjoyed it!
PPS: Just kidding. The model did generate some of the sentences I used in this piece, and hopefully, someday, it will be able to write an entire blog post for me!
Sanjay Varma Rudraraju is an Application Developer at ADP based in New Jersey.
