Melinda Tellez is a data scientist, strategist and leader with a proven track record of unlocking organizational growth through data-driven insights. As the Director of Data Science at Clarify Health, an enterprise analytics and value-based payments platform company, Melinda collaborates with informatics teams, engineers, and business leaders to deliver impactful solutions that align with strategic goals.
With comprehensive experience across the data and MLOps lifecycle, Melinda specializes in designing, developing, deploying, and maintaining analytics and machine learning models. She has delivered predictive analytics, retrospective modeling, and cutting-edge solutions tailored to various payer and provider use cases. Looking back now, her professional experiences have made her successful in the roles she likes to take on but like anything, it wasn’t always clear how she would break into the data science space when she started out.
Melinda attended UCLA, where she earned her undergraduate degree in Biophysics. It was during a formative internship that her first mentor, Bonnier Feldman, pointed out that she could establish a career in healthcare but not follow the obvious path of her peers by becoming a clinician (blood makes her squeamish and organic chemistry was a nightmare). Bonnie herself had reinvented her career when she retired as a dental surgeon to go on to become a co-founder for a virtual autoimmune practice called rheumission to help those receive customized treatment for their autoimmune disease.
After graduation, Melinda moved to Boston and began her career at Orion Health, a health tech company that helped EMR’s setup health information exchanges when medical records were required to be digitized. She started out in SaaS implementation and development where she learned how to think about product deployment efficiencies but wasn’t able to carve out a place for a transition into analytics and insights.
A few years later, she became a Senior Data Analyst for Brigham and Women’s Hospital while attending Boston University where she was pursuing her Master’s in CS with a concentration in Data Science. Her time at Brigham and Women’s Hospital allowed her to cultivate her expertise in healthcare analytics by supporting clinical departments with data-driven initiatives. Melinda assisted various departments like Quality and Safety, Care Management and Palliative Care by developing reports, visual dashboards and predictive analytics.
At Genpact, Melinda honed her consulting skills from the hospital and technical skills from her degree by delivering machine learning solutions for healthcare clients, including predictive cost estimators and COVID-19 risk models. Her subsequent role at Cogitativo provided hands-on experience with algorithm design and model enhancement, paving the way for her transition to Clarify Health.
At Clarify, she started by building and maintaining ML and predictive analytics pipeline architectures at the company. But the role evolved into also ensuring high post-processing observability and creating robust reporting capabilities for customers to interpret the data. She then began working with other teams within the organizations to help take concepts or new product initiatives through development and experimentation while also assisting with improvement or redesign of existing analytics and predictive frameworks. Her team also developed and patented a novel modeling approach for provider and patient benchmarking which has been successfully deployed and scaled across their pipelines.
In addition to her professional accomplishments, Melinda is a dedicated volunteer with DataKind, leading initiatives that apply data science to humanitarian challenges. She also serves as a Board Member and Data Science Advisor for Aquaya, supporting the development of data-driven solutions to improve access to safe water and sanitation.
Overall, her holistic approach to problem-solving and commitment to impactful, data-driven innovation continue to drive insights improvements not only to those she works with professionally but for those she works with in the nonprofit space as well.
Architecting Solutions: A Problem-Solving Approach
In both her professional and non-profit roles, Melinda approaches data architecture much like a doctor diagnosing patients—she begins by asking her “patients” to share their pain points. After all, everyone faces data challenges, and Melinda excels at guiding stakeholders to then help them uncover and articulate solutions.
Start with the fundamentals:
Before diving into design, it’s crucial to ask:
- What are the specific pain points your organization faces?
- Where are you aiming to create impact?
- What datasets, tools, teams, or infrastructure do you already have to support these concerns?
The answers to these questions establish a foundation of understanding and provide critical insights for mapping an architectural path—from conception to production.
Addressing the common bottlenecks:
Most challenges arise in the middle of the process. Often, people struggle to clearly define their problem or articulate their desired outcome. Without this clarity—or when competing priorities exist—progress stalls, and achieving meaningful impact becomes difficult. Conversely, individuals may rush to a solution without carefully evaluating their options: Should you build, buy, or outsource? Melinda helps organizations overcome these hurdles by aligning problem-solving with their mission. She encourages teams to frame challenges thoughtfully, ensuring workflows and solutions advance their ultimate goals and bridge the gap between where they are and where they want to be.
2 Career Insights for Data Professionals
1. Attention to Detail is Key – Especially in Healthcare
Healthcare data is among the most complex to work with due to changing and complex regulations and the dynamics between payers and providers. If you’re considering a career as a data scientist or analyst in this space, a deep passion for understanding the industry will go a long way. Success in healthcare data requires patience, curiosity, and an appreciation for its unique complexities.
2. Commit to Constant Evolution
The data science field—along with its tools and technologies—is evolving at lightning speed. To thrive, you must embrace lifelong learning and adapt to industry changes. However, it’s equally important to carve out your niche. Focus on building expertise in areas that align with your strengths and career goals, ensuring your evolution is purposeful and impactful.
For more on Melinda, you can find her on LinkedIn!