Career Center

The Georgetown University Graduate Data Science and Analytics Program invites applicants for a full-time, non-tenure line position as an assistant teaching professor.

The successful candidate will teach three graduate-level courses per semester (Fall and Spring) and will participate in administrative tasks, such as admissions and advising.

This is a non-tenured teaching position, where the core focus will be on course development (traditional and online), teaching, student support, and some administrative tasks such as student support, admissions, and committee participation. The teaching component (85-90%) includes 6 class sections (30 – 40 students each) per year with summers as a potential option. The administrative component varies with need and ranges from 10 – 15%.

All new hires will be signed for a one-year initial contract to assure a good match. The renewal will be for a three-year term pending a committee review.  This academic appointment is August 1, 2023 to May 31, 2024 and the renewal will be from June 1, 2024 to May 31, 2027. Contracts are renewable after each contract term and depend on yearly reviews, performance, and committee review and approval.

The Data Science and Analytics Program offers a Master of Science degree in Data Science and Analytics and a BS(BA) – MS 5-year accelerated program in Data Science and Analytics. Ideal candidates will possess excellent teaching skills, experience, and interest, as this is a teaching position. Candidates should be comfortable with course development, teaching online (including the use of Canvas and Zoom), classroom teaching, and student support. Georgetown University values student success, achievement, equality, diversity, and support. An ideal candidate will encourage and actively support all of these elements. Candidates must be self-directed and independent, but also committed to being an active and valuable member of our Data Science team.  

With its location in Washington, D.C., Georgetown University provides opportunities to connect with government agencies as well as high-tech industries. The Data Science and Analytics Program maintains considerable and continuous relationships with local and non-local companies, offers regular seminars and career fairs, and promotes a strong sense of community among faculty and students. 

Georgetown attracts some of the very best students in the country and the world. The Georgetown University Graduate Data Science and Analytics Program provides students with rigorous training in computational, mathematical, and statistical methods to prepare them for careers in data science and analytics.

Please note that this is not a research position. While research is highly regarded and encouraged, it is completed on the faculty member’s own time and is not contained within the requirements of this position. This position is also not connected to the option of transitioning into a tenure-line or research position. There is no avenue to translate from a non-tenure teaching position into a tenured position. If you are interested in a tenure-line and research-focused position, please do not apply to this advertisement.

Candidates must possess a PhD in Computer Science, Mathematics, Statistics, Data Science, or a related field.

Candidates must have expertise in most of the following skill sets:

• Programming in R and Python: With an emphasis on standard Data Science tool sets (pandas, seaborn, numpy, matplotlib, Tidyverse, etc.) 

• General Mathematics: Single and multivariable calculus, linear algebra, algorithm implementations and numerical methods, basic and advanced statistics 

• General Data Science Skills: Data munging (i.e data wrangling), gathering (including APIs), data cleaning and preparation, EDA, all components of the data science life cycle

• Big data and Cloud Computing: AWS, Azure, linux, bash, ssh, Hadoop, Spark, MapReduce, etc.

• Advanced Data Visualization: Tableau, Gephi, R/Shiny, R/leaflet, R/NetworkD3, R/ggplot2, seaborn, matplotlib, D3, Javascript, Bokeh, Plotly, etc.

• Data Storytelling: Expertise with communicating information from data, narratives, storytelling, visual narratives, decision science, etc. 

• Machine Learning: Theory, algorithms, and applications. Classification and regression, Naive Bayes, decision trees & random forests, PCA, clustering, association rule mining, linear and logistic regression, etc.

• Deep Learning: Experience with deep learning as applied to NLP and computer vision. Familiarity with standard neural network paradigms (MLP, CNN, RNN, LSTM, VAE, Transformers) as well as experience with Keras, Tensorflow, and PyTorch. Added bonus would be familiarity with both traditional and deep reinforcement learning.

Candidates must possess both teaching experience and a clear interest in working with, supporting, and advising students.

Preference may be given to candidates with specialized strengths in statistics, applied mathematics, NLP, deep learning, and machine learning.