Alumni Spotlight: Detian Deng, ScM ’13, MSE ’17, PhD ’18
Detian is currently based in Beijing and works as a Senior Data Science Manager for TikTok Ads.
Detian Deng graduated from Johns Hopkins University with his MSE in Computer Science in 2017, and with his ScM and PhD in Biostatistics from the Johns Hopkins Bloomberg School of Public Health in 2013 and 2018.
Currently, Deng works as a Senior Data Science Manager for TikTok Ads in Beijing, China. There, his focus is to develop and optimize the ads measurement methodology and products for TikTok Ads. Ads measurement is the systematic process of quantifying the performance and impact of advertising campaigns.
Describe your current position and responsibilities for current and prospective students about career opportunities in biostatistics.
As a Data Science Manager in ads measurement, I lead a team of data scientists to develop and optimize the methodology and products that quantify the effectiveness of advertising campaigns. The primary goal is to understand the true causal business impact of the ads that advertisers run on TikTok, under substantial observation errors. A manager in ads measurement is typically responsible for: leading and mentoring a team, developing a strategic/execution roadmap, ensuring statistical rigor, cross-functional collaboration, and communicating actionable insights.
Can you describe your day-to-day work life?
The day often begins with a team meeting to align on project priorities and address roadblocks. A significant portion of the day is spent in cross-functional meetings with product, engineering, and sales/ops teams to drive collaborative progress. A key part of my role involves mentoring team members through one-on-ones and providing crucial technical oversight. This means reviewing my teams methodology design and intermediate outcomes to guarantee the quality of deliverable. As manager I also act as a vital bridge between deep technical analysis and high-level business strategy, ensuring the company invests its resources in the right direction.
How did your degree in biostatistics prepare you for your career? What aspects of the Hopkins program did you find most useful?
Hopkins' PhD program has a high reputation even outside of academia and the health care/pharma industry, making it possible to pass the resume screening phase when looking for your first internship. The intern program at the Johns Hopkins Biostatistics Center also prepared me with communication skills and empathies when collaborating with people from non-technical backgrounds. The PhD program prepared me with solid understanding of the statistical methods/theories as well as hands-on skills in data wrangling and programming. These are essential attributes for passing the interviews and delivering trustworthy outcomes in day-to-day work.
What led you to join the Johns Hopkins Department of Biostatistics?
I learned about the Department’s reputation through a web search, reading research papers, and talking to undergrad alumni who also went to Biostatistics graduate programs. I also received advice from my undergraduate faculty and advisor. Transparent information listed on the Department website, including faculty profiles, working groups, curriculum design, alumni introduction, etc. also helped in my decision making.
What advice would you give to prospective students?
- Identify your interests: Start exploring different areas early. Pay attention to what you keep thinking about and what keeps you in the "zone." Use seminars and clubs to narrow it down.
- Get hands-on with data: Get comfortable in R/Python and do full end-to-end mini projects— cleaning, EDA, modeling, validation, and clear plots. Keep things reproducible (Git, R Markdown/Jupyter). Shipping small, complete analyses beats half-finished big ideas.
- Invest in communication: Practice explaining your assumptions and uncertainty in plain language. Write short, clear abstracts; make figures that tell the story. Present early in lab meetings and iterate based on feedback—clarity makes everything else easier.
- Seek early mentorship and feedback: Talk to potential advisors and senior students; ask for reading lists, toy problems, and sanity checks on your ideas. Set a regular check-in cadence and small milestones. Fast feedback loops will help you find your lane and build momentum.
What was your experience navigating the job market after graduation?
After graduation, I started at YouTube Ads (Google) on auto bidding, CVR prediction, and experimentation. I then moved on to Facebook to deepen Bayesian modeling and AI infrastructure; then I transitioned back to digital advertising and landed at TikTok Ads, where I work on multiple fields across measurement (conversion/brand lift, identity/attribution), delivery (ranking, bidding, automation), and vertical solutions (gaming, mini-series, apps).
Academia vs. industry: Academia trained me to value methodological soundness and uncertainty quantification, while industry forced clarity on business levers, speed to production, and measurable outcomes. The biggest shift was embracing “good enough, shipped, and iterated” over perfect-but-late, while still guarding experimental rigor and risk control. I found that academic depth is my unique strength so long as it’s paired with pragmatic engineering and product sense.
Individual Contributor (IC) vs. management: As an IC, I focused on hard technical problems—lift estimation, auction/pacing mechanisms—and built credibility through shipped systems and quantified impact. Moving into management, the job became multiplying impact: setting strategy, building teams, creating SOPs, and aligning across product/engineering/ops. The trade-off is less hands-on modeling, more decision making under ambiguity and talent development. I recommend switching when you’re motivated by org-scale outcomes and enjoy coaching; otherwise, an IC path can be equally high leverage.
What are your favorite memories of your time at Johns Hopkins Biostatistics?
My favorite memories are really about growth and community. I came in as a total newbie and left feeling like an independent researcher—learning how to ask good questions, frame the solution, complete clean analyses, and defend my choices. That arc was powered by great mentorship: my advisor and the faculty pushed me but also gave me room to explore and make mistakes. And my peers—problem sets, late-night whiteboard sessions, conference trips—whose friendships made the hard parts fun. And on a personal note, the best memory of all: I met my wife at JHU. So Hopkins didn’t just shape my career—it changed my life.
