Data Scientist

AVER•Washington, DC

5d•Hybrid

About The Position

AVER is a fast-growing SDVOSB that has achieved significant revenue growth over the past 5 plus years, with much of our work focused on Data Analytics, Mission Support, IT Modernization, and Biometrics. AVER is seeking a Data Scientist with strong experience in advanced analytics, automation, and Artificial Intelligence/Machine Learning (AI/ML) to develop innovative solutions that enhance data-driven insights, pattern discovery, and operational efficiency. The ideal candidate will have experience evaluating, integrating, and optimizing diverse and complex data sources, including images, metadata, geospatial information, and other structured and unstructured datasets. This individual will help design and develop centralized, searchable, and secure data environments while applying AI/ML, machine learning, computer vision, pattern recognition, and predictive analytics to uncover meaningful trends and relationships within large datasets. The Data Scientist will collaborate with technical teams, data engineers, leadership, and other stakeholders to modernize data handling, automate analytical processes, improve data quality, and develop scalable analytical solutions. We are looking for someone who is highly hands-on with technologies such as Databricks, Python, AI/ML, data engineering, and advanced analytics and can translate complex data into meaningful, actionable insights. Be part of a collaborative team that values AVER's mission, focus, and culture to deliver exceptional client service with great people and transformative technology solutions to enable Government mission success.

Requirements

Bachelor's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Information Systems, or a related field from an accredited institution
8+ years of experience in data science, advanced analytics, or applied machine learning
Demonstrated experience designing, building, or integrating centralized data environments, data lakes, or enterprise analytical platforms
Strong proficiency with Python, R, or similar programming languages used for statistical analysis, automation, data processing, and machine learning model development
Hands-on experience with Databricks for data engineering, advanced analytics, and machine learning within a cloud environment
Experience using Databricks for large-scale data processing, feature engineering, model development, training, validation, and production deployment
Experience applying machine learning, computer vision, image analysis, and/or pattern recognition techniques to real-world datasets
Experience developing, training, validating, and deploying machine learning models
Familiarity with geospatial analytics tools and methodologies, including ArcGIS and/or Python-based geospatial libraries
Experience integrating device, event, location, image, and other heterogeneous datasets for advanced analysis
Experience cleaning, standardizing, transforming, and validating large and diverse datasets
Ability to design and implement automated workflows for image tagging, classification, indexing, and metadata extraction
Strong understanding of API integrations, data pipelines, ETL/ELT processes, and modern data engineering practices
Experience developing automated analytical workflows and scalable data-processing solutions
Demonstrated ability to translate complex analytical findings and technical concepts into clear, actionable insights for non-technical stakeholders and leadership
Strong analytical, problem-solving, and critical-thinking skills
Strong interpersonal communication skills with a collaborative, customer-focused mindset
Demonstrated ability to work autonomously, take ownership of complex technical challenges, and deliver solutions within a mission-driven environment
Ability to obtain and maintain the government clearance required for the position.
This role supports a government agency that requires U.S. citizenship.

Nice To Haves

Master's degree preferred
Experience supporting federal law enforcement, forensic analysis, criminal investigations, or other investigative missions
Familiarity with digital evidence workflows, device metadata, image-based forensic analysis, and investigative data
Experience developing, deploying, or integrating AI/ML solutions within secure or restricted environments.
Experience applying computer vision to image classification, similarity detection, object recognition, or related analytical use cases
Proficient with data visualization and geospatial visualization tools, including Power BI and ArcGIS dashboards
Experience working with cloud-based data environments and technologies, including Azure, SQL, and Oracle
Knowledge of secure data architecture, data governance, access controls, and data protection requirements
Experience integrating APIs, data platforms, analytical applications, and AI/ML capabilities into centralized data environments

Responsibilities

Assess existing data sources, including digital evidence, images, spreadsheets, device metadata, location data, and other investigative information, and define requirements for a unified, searchable, and secure data environment
Recommend software tools, platforms, and architectural approaches to organize, store, integrate, and connect disparate data types to enhance analytical capabilities and pattern discovery
Identify technical and operational requirements, evaluate available solutions, and support the development and implementation of AI/ML models that integrate seamlessly into the centralized data environment
Evaluate and recommend software solutions and API integrations that support pattern recognition, data standardization, automated ingestion, and analytical workflows
Collaborate with federal leadership and technical stakeholders to ensure the data environment meets operational, security, investigative, and forensic requirements
Leverage Databricks to develop, train, validate, and deploy machine learning models
Collaborate with data engineering and business teams to develop scalable analytics solutions capable of supporting large and complex datasets
Support the development of modern data pipelines and workflows that improve the accessibility, consistency, security, and usability of investigative data
Analyze evidence images and associated metadata to identify similarities, relationships, repeated methods, and potential connections across investigative cases
Apply advanced analytics, machine learning, computer vision, and pattern recognition techniques to identify trends, behavioral patterns, and recurring techniques associated with criminal activity
Combine device, event, location, and geospatial data to develop visual summaries, maps, hotspot analyses, and other investigative analytical products
Develop predictive assessments that help identify emerging trends, recognize patterns, and anticipate potential future activity
Standardize, clean, validate, and enrich location information associated with device collection and criminal events to ensure accuracy and consistency
Develop automated methods to sort, label, classify, and categorize large collections of skimmer device images, enabling efficient tagging, indexing, searching, and retrieval
Develop approaches for automated image tagging, classification, metadata extraction, and pattern identification
Ensure images and associated investigative data are searchable, structured, standardized, and optimized for analytical and investigative use
Develop automated workflows that reduce manual processing and improve the speed, consistency, and accuracy of forensic data analysis
Evaluate analytical and machine learning outputs for accuracy, reliability, scalability, and operational usefulness
Provide training, technical documentation, and hands-on guidance to federal personnel on newly implemented tools, analytical methodologies, AI/ML capabilities, and data workflows
Provide ongoing technical and analytical support to improve data handling, analytical rigor, and operational efficiency across customer mission
Collaborate directly with investigators and forensic analysts to understand mission requirements, refine analytical tools, and ensure solutions remain aligned with operational needs.
Translate complex technical and analytical concepts into clear, actionable information for technical and non-technical stakeholders.
Recommend opportunities to improve existing analytical processes through automation, advanced analytics, AI/ML, and emerging technologies.