Ecological Data Scientist, IS-1560-12

Application Deadline: 18 May 2026

Department: National Zoo and Conservation Biology Institute

Employment Type: Full Time

Location: Front Royal, VA

Reporting To: Supervisory Ecologist

Compensation: $102,415 - $133,142 / year

Description

OPEN DATE: April 20, 2026CLOSING DATE: May 18, 2026
POSITION TYPE: Trust FundAPPOINTMENT TYPE: Temporary Not to Exceed nine (9) Months (may be extended based on funds availability)SCHEDULE: Full Time DUTY LOCATION:  Front Royal, VA
Non-sensitive (NS)/Low Risk

Who May Apply: Open to all Qualified Applicants

What are Trust Fund Positions? Trust Fund positions are unique to the Smithsonian. They are paid for from a variety of sources, including the Smithsonian endowment, revenue from our business activities, donations, grants and contracts. Trust employees are not part of the civil service, nor does trust fund employment lead to Federal status. The salary ranges for trust positions are generally the same as for federal positions and in many cases trust and federal employees work side by side. Trust employees have their own benefit program and may include Health, Dental & Vision Insurance, Life Insurance, Transit/Commuter Benefits, Accidental Death and Dismemberment Insurance, Annual and Sick Leave, Family Friendly Leave, 403b Retirement Plan, Discounts for Smithsonian Memberships, Museum Stores and Restaurants, Credit Union, Smithsonian Early Enrichment Center (Child Care), Flexible Spending Account (Health & Dependent Care).  Conditions of Employment

• Pass Pre-employment Background Check and Subsequent Background Investigation for position designated as low risk.
• Complete a Probationary Period
• Maintain a Bank Account for Direct Deposit/Electronic Transfer.
• The position is open to all candidates eligible to work in the United States.  Proof of eligibility to work in U.S. is not required to apply.
• Applicants must meet all qualification and eligibility requirements within 30 days of the closing date of this announcement. 

OVERVIEW

INTRODUCTIONThe Smithsonian Institution is the world’s largest museum, education, and research complex, with 21 museums and the National Zoo. This position is located in the Smithsonian's National Zoo and Conservation Biology Institute (NZCBI), Front Royal, VA. NZCBI plays a leading role in the Smithsonian's global efforts to save wildlife species from extinction and train future generations of conservationists.
SUMMARY
We are seeking a motivated Ecological Data Scientist to join our interdisciplinary research team focused on advancing conservation and ecological understanding through innovative data analysis. The successful candidate will work at the interface of ecology, statistics, and data science with large ecological datasets—including wildlife monitoring (e.g., animal movements, camera trapping, acoustics, eDNA), remote sensing, and field survey data—to support research, management, and decision-making in conservation and natural resource management. The incumbent will perform analytical and evaluative work associated with research activities involving the ecological study of organisms and the environment, summarizing findings for scientific and lay audiences. The incumbent must have demonstrated expertise in computer programming (e.g., R, Python) and fluency in managing large, complex datasets. The incumbent supervises interns, students, and collaborates with other ecologists, conservation practitioners, and data scientists to develop analytical tools, integrate diverse datasets, and generate actionable insights for biodiversity conservation. The position provides opportunities for independent research, collaborative grant development, and peer-review publication, and will contribute to strengthening quantitative capacity across the Institute.
  LOCATION
Conservation Ecology Center, Smithsonian’s National Zoo and Conservation Biology Institute, Front Royal, VA

DUTIES AND RESPONSIBILITIES

• Integrate diverse data streams, including wildlife monitoring data, long-term ecological datasets, movement data, and remotely sensed environmental variables; 
• Develop and apply statistical, computational, and machine learning methods to understand ecological patterns, species distributions, population dynamics, and ecosystem processes; 
• Contribute to the development of dashboards, decision-support tools, and repr