At Memorial Sloan Kettering (MSK), we're not only changing the way we treat cancer, but also the way the world thinks about it. By working together and pushing forward with innovation and discovery, we're driving excellence and improving outcomes.
For the 28th year, MSK has been named a top hospital for cancer by U.S. News & World Report. We are proud to be on Becker's Healthcare list as one of the 150 Great Places to Work in Healthcare in 2018, as well as one of Glassdoor's Employees' Choice Best Place to Work for 2018. We're treating cancer, one patient at a time. Join us and make a difference every day.
By joining the Strategy and Innovation team, you will have an exciting group of pioneers at your side. The department is comprised of data scientists, operations researchers, strategic analysts, software engineers, behavioral psychologist, and designers. Our well-blended team allows for a creative environment where everyone can freely explore ideas and concepts that benefit the entire department.
Along with increasing access to world renowned surgeons and rare treatment options, our focus is to enhance the patient experience and assess and take the lead on the system-wide impact on resources stemming from surgical demand.
The Role and Projects
We are seeking an Operations Research Engineer who will perform mathematical modeling and act as a modeling enthusiast on projects that use newfangled approaches to improve how care is delivered. As part of the Surgery Analytics team you will empower decisions motivated by data and process changes to enhance patients' outcomes and experience. This will involve defining and scoping analyses, understanding processes and identifying decision points, collecting and running data, building and validating statistical and mathematical models, transforming models into impactful products, working with multiple departments and delivering results.
Sample projects include:
Scheduling accuracy and optimization project : Improve existing, and develop new models to predict a patient's time and resource requirements during and after surgery. Transform proof of concept models and ideas to production level
Nurse staffing optimization : Explore and use patient demand data to predict the anticipated staffing needs in the Operating Suite. Assess the staffing constraints and preferences to the best match capacity and demand through heuristics or optimization. Deliver insight through data regularly along interactive display of potential decisions
Analytical decision support for leadership : Questions that require appropriate timing for impactful decisions. What is the true surgical robotic needs for the next five years. Have we increased case duration for surgical cases and is this an effect of increased complexity in the types of cases we see?
Are you able to answer yes to all of these bullets?
Successful at finding, gathering, and structuring data by talking to and learning from partners in the organization
Build and validate analysis around a high-level question from a non-technical perspective
Excited to serve as a technical liaison and self-assured at adding or removing complexity to modeling approaches to arrive at the best approach to support decisions consistently
Outline assumptions behind analysis and communicate the scope of the analysis
Skilled in using quantitative techniques (from descriptive statistics, predictive modeling & forecasting, to prescriptive analytics including decision analysis, math programming, simulation, machine learning) to resolve business problems
Hungry to make processes and systems more effective and efficient in order to improve health care delivery for our patients
Eager and curious to undertake a wide variety of complex problems, and able to learn and work independently
Linear and mixed-integer programming (Gurobi/Python, GAMS, AMPL, LINDO)
Simulation: discrete-event, agent-based, system dynamics, Monte Carlo (Simio, Flexsim, Anylogic, Arena)
Statistical software (R, SAS, or SPSS) or programming (Python, Java, C++, or VBA)
Knowledge in advanced and applied analytics (academic or industry): machine learning, predictive modeling, forecasting, simulation, optimization
Data visualization (Tableau, R, python)
Excellent verbal and written communication
Delivering a presentation
Bonus/nice to have:
Masters degree or PhD in a related field
Robust and Stochastic Optimization
Linear regression, random forest, GBM, SVM, ANN
When applying, please include both a resume and a cover letter (as a single PDF).
MSK is an equal opportunity and affirmative action employer committed to diversity and inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration without regard to race, color, gender, gender identity or expression, sexual orientation, national origin, age, religion, creed, disability, veteran status or any other factor which cannot lawfully be used as a basis for an employment decision.
Federal law requires employers to provide reasonable accommodation to qualified individuals with disabilities. Please tell us if you require a reasonable accommodation to apply for a job or to perform your job. Examples of reasonable accommodation include making a change to the application process or work procedures, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.
Internal Number: 8507532
About Memorial Sloan-Kettering Cancer Center
As one of the world's premier cancer centers, Memorial Sloan-Kettering Cancer Center is committed to exceptional patient care, leading-edge research, and superb educational programs. The close collaboration between our physicians and scientists is one of our unique strengths, enabling us to provide patients with the best care available today as we work to discover more effective strategies to prevent, control, and ultimately cure cancer in the future. Our education programs train future physicians and scientists, and the knowledge and experience they gain at Memorial Sloan-Kettering has an impact on cancer treatment and the biomedical research agenda around the world.