During the fall semester's final Stat 300 Seminar (typically focused on Ph.D. student research talks), four Ph.D. alumni joined a special industry-focused career panel via Zoom. Expertly moderated by fifth-year Ph.D. student Louis Cammarata, the panel featured Valeria Espinosa (Ph.D. '13), a staff data scientist at Waymo; Paul Baines (Ph.D. '10), head of engineering at QC Ware; Elaine Zanutto (Ph.D. '98), vice president of methods and advanced analytics at Naxion Research and Consulting; and Samantha Cook (Ph.D. '04), chief scientist at FNA (see their profiles below). The panelists, who are based in California, Pennsylvania, and Michigan, shared their career paths with the students and emphasized the importance of planning, finding a suitable work environment, and developing and marketing the skills needed for their chosen career paths. It's worth noting that while the panelists provided valuable advice, their career tips are based on their own personal experiences, which may differ from those of other alumni.
Importance of Planning a Career Trajectory
While our alumni panelists currently work in industry jobs, they started their careers in academia as faculty members or postdoctoral fellows. As Dr. Zanutto noted, she wasn't sure if she wanted to pursue a tenure-track academic career, but she was curious and decided to try it out. It's completely normal for graduate students to be unsure about whether they want to pursue an academic or industry career. However, our panelists emphasized that it is often easier to transition from academia to industry than the other way around. Therefore, students considering academia may want to start out in a junior faculty or postdoctoral position, where they can gain valuable experience and perspective before taking the next steps in their careers. It is also important for young researchers to speak with mentors and reflect on the pros and cons of academia and industry before making a decision.
Identifying a Suitable Work Environment
Dr. Baines has built expertise in a range of fields, including data science, machine learning, artificial intelligence, and quantum computing, through his work at various companies in different industries. On the other hand, Dr. Zanutto has primarily built her career at Naxion Research and Consulting, where she has overseen the modeling, advanced statistical analysis, and sampling methodology for customer projects. Despite taking different career paths, both Dr. Baines and Dr. Zanutto emphasized during the panel the importance of students finding a work environment that fits their needs. Dr. Baines and Dr. Zanutto noted that while academic research can be intellectually stimulating, they appreciate the more defined timeframes for projects in industry, which allow them to problem-solve, determine a result, and quickly transition to the next project. Both panelists also highlighted the value of a collaborative and team-focused work environment, with Dr. Baines specifically mentioning the benefits of working with and learning from a diverse group of colleagues with complementary skillsets (e.g., CS, engineering, and statistics skills). As Ph.D. students consider their career options in academia and industry, our alumni panelists have demonstrated that it is important to find a workplace environment that aligns with their values and goals.
Developing and Marketing the Requisite Skills for a Career Path
Dr. Espinosa and Dr. Cook were able to effectively translate and market their strong statistical skills to secure their first industry jobs as statisticians at Google. For example, when Dr. Espinosa, a lecturer in causal inference at the time, applied for a position at Google in search ads metrics, she demonstrated her ability to apply her academic expertise to a business environment. In addition to learning specific statistical tools during their Ph.D. programs, both Dr. Espinosa and Dr. Cook developed critical thinking and problem-solving skills through their research and built strong communication skills through writing and presenting. By examining the data through a statistical lens in her work, Dr. Espinosa could create models that were simpler and more interpretable than some of the other simulations. Dr. Cook confided that while she was wary of public speaking as a graduate student, she ultimately realized that the requirement to give talks in Stat 300 and to teach helped her to become a skilled communicator and advocate for her ideas. Dr. Cook and Dr. Espinosa also recommended that students take advantage of computer science (CS) courses, as programming skills have become increasingly important over the course of their careers. Interested Ph.D. students at Harvard can take advantage of a wide range of CS courses, and the department is planning to expand its machine learning course offerings with the introduction of an undergraduate machine learning track. Dr. Espinosa's and Dr. Cook's experiences can serve as a useful guide for students considering careers in industry as they think strategically about their own skill development.
Our alumni panelists provided valuable advice for students looking to find a career path that fits them best, whether in academia or industry. They emphasized the importance of identifying a suitable work environment and developing the skills necessary for the job. Through events like this career panel, our goal is to help students learn about a wide range of career options and encourage them to have proactive conversations with alums in both academia and industry. Students who are looking to take the next step in their career development can consider joining the statistics alumni LinkedIn group, which is open to all Harvard statistics undergraduate and graduate students, alumni, postdocs, and faculty. In addition, students can take advantage of the career coaching and alumni mentoring opportunities available through the Harvard FAS Office of Career Services. Finally, the Department of Statistics would like to express our appreciation for the panelists' thoughtful participation, and we hope to see them at future departmental events.
Career Panel Profiles:
Paul Baines is an engineering and scientific leader that has worked with a variety of emerging technologies including Machine Learning, AI and Quantum Computing. He specializes in building systems that enable those emerging technologies to solve real-world problems. He has worked on applications in domains such as drug discovery, pipeline safety, aircraft and wind turbine maintenance, and customer support. Paul is currently Head of Engineering at QC Ware, a Quantum Computing software company based in Palo Alto, CA. Prior to joining QC Ware, Paul held positions including Head of ML, Data & Infrastructure at Checkr, Head of ML Engineering at Wise.io/GE Digital and Assistant Professor of Statistics at UC Davis. He graduated with his Ph.D. in Statistics under Prof. Xiao-Li Meng in 2010 where his research focused on Astrostatistics and efficient Bayesian computation.
Samantha Cook earned a Ph.D. in statistics at Harvard University in 2004. She is currently the Chief Scientist at FNA, a London- based software company that uses network theory to quantify financial risk. Prior to FNA she worked in the Research Group at Google and has taught statistics at several universities, including Columbia University in New York and Universitat Pompeu Fabra in Barcelona. In addition to network theory and finance, her research interests include Bayesian modeling and statistical computation, and she has published in statistics, economics, finance, psychology, and public health.
Valeria Espinosa graduated from Harvard in 2013. She was the lecturer of the Causal Inference course and a postdoc before joining Google in 2014. She was at Google for 7 years, the first half in Search Ads Metrics and the second half in Google Health. In January 2022 she joined Waymo, where she is a Staff Data Scientist.
Elaine Zanutto is Vice President of Methods and Advanced Analytics at Naxion Research and Consulting, a market research consulting firm guiding clients in a variety of industries including financial services, information technology, health and life sciences, and consumer products. Elaine leads a talented department of statistical programmers and statisticians who advise project teams throughout the firm on modeling, advanced statistical analysis and sampling methodology. Elaine's areas of specialized expertise include Hierarchical Bayesian modeling for estimating individual-level preferences in complex choice situations, segmentation analysis for a broad range of audiences and industry sectors, and the design of highly customized forecasting models that rely on a variety of choice modeling platforms, including dynamic models that account for market evolution in the post-launch environment. She is also an expert on tools for driver analysis in challenging markets where it is difficult to disentangle the impact of specific sources of influence. Before joining Naxion, Elaine was an Assistant Professor of Statistics at The Wharton School of the University of Pennsylvania.