Senior Data Scientist

Amivero•Washington, DC

1d•Remote

About The Position

The Amivero Team Amivero’s team of IT professionals delivers digital services that elevate the federal government, whether national security or improved government services. Our human-centered, data-driven approach is focused on truly understanding the environment and the challenge, and reimagining with our customer how outcomes can be achieved. Our team of technologists leverage modern, agile methods to design and develop equitable, accessible, and innovative data and software services that impact hundreds of millions of people. As a member of the Amivero team you will use your empathy for a customer’s situation, your passion for service, your energy for solutioning, and your bias towards action to bring modernization to very important, mission-critical, and public service government IT systems. The Gist… Our Senior Data Scientist will lead the formulation and application of mathematical modeling and other optimizing methods to develop and interpret information that assists management with decision making, policy formulation, or other managerial functions. May collect and analyze data and develop decision support software, service, or products. May develop and supply optimal time, cost, or logistics networks for program evaluation, review, or implementation. The Senior Data Scientist is responsible for larger, more complex or technically advanced projects, and may be responsible for tasks among multiple projects. The Senior Data Scientist should have expertise and/or experience in implementing deep and/or machine learning, or artificial intelligence (AI) projects in a High-Performance Computing (HPC) environment; and will be experienced in working with federal government categorical data from flat files, data warehouses, and external/academic sources, and should demonstrate experience as a data scientist and/or machine learning engineer. Senior Data Scientist may be responsible for leading less experienced analysts.

Requirements

U.S. Citizenship required to obtain a DHS clearance
10+ years of relevant demonstrated experience
Strong communication, interpersonal, writing skills
Attention to detail and ability to meet multiple deadlines in a rapidly changing environment
Encourage and build team spirit among staff, bringing about mutual respect and cooperation
Active Secret or Top Secret clearance required

Responsibilities

Conduct research and prepare management, organizational and business analyses and forecasts.
Provide subject matter expertise and active participation in any analytics and reporting requirements meetings with client, or other stakeholders as requested by the Chief or federal staff.
Identify gaps for law enforcement decision support tool development and improvement including opportunities to collect new data, improve data quality, and improve forecasting methodologies, reporting, and scenario planning capabilities.
Develop, maintain, and enhance the suite of Discrete Event Simulation (DES) models to better capture all components of the Immigration Enforcement and Removal Lifecycle.
Evaluate the use of current and other artificial intelligence, data visualization, modelling technology and business intelligence platform types as appropriate, including, but not limited to: Python, GitHub, Qlik, Tableau, ArcGIS, Databricks, Palantir Foundry or other relevant tools.
Maintain and improve law enforcement-related geospatial capabilities.
Maintain and improve Logistics Optimization Decision Support tools.
Streamline reporting on key performance indicators across the data and visualization enterprise promoting information sharing among field and HQ stakeholders.
Promote interoperability of products with other law enforcement DES models as needed to enhance decision-making and forecasting.
Support all ad-hoc activities, requests, model, and tool enhancements, prioritizing and validating requests with law enforcement stakeholders.
Perform recurring model maintenance, support, and governance
Prepare models for official DHS accreditation including supporting model documentation that describes the underlying design and methodology for the model’s concept and its constituent parts, addresses the model’s requirements how the model is used to inform any decision-making processes, analysis of alternatives for methodology chosen, engagement with stakeholders, etc.