Virginia, Virginia, United States · hoy
Diseñar, desarrollar e implementar modelos de aprendizaje automático escalables y pipelines de datos para analítica operativa y perspectivas predictivas, trabajando de extremo a extremo en un equipo Agile.
Job Summary We are seeking a motivated and experienced Mid-Level Data Engineer and Machine Learning (ML) Developer with Agile methodology experience to support advanced data analytics and machine learning initiatives. This role focuses on designing, developing, and deploying scalable ML models and data pipelines that solve complex, real-world problems. The ideal candidate is a strong problem solver who thrives in a collaborative, fast-paced environment and can work end-to-end across data engineering and machine learning workflows. Responsibilities • Design, develop, and deploy machine learning models and data pipelines to support operational analytics and predictive insights. • Import, process, and analyze large-scale operational and performance datasets. • Develop predictive models, perform root cause analysis, trend analysis, and IoT signal analytics. • Build and optimize end-to-end data analytics solutions, from ingestion to model deployment. • Deliver ML software components in collaboration with Product and Data Science teams. • Write, test, and maintain application code and ML models, including automated testing and deployment. • Collaborate within a cross-functional Agile team to develop next-generation big data and ML applications. • Leverage cloud-based architectures to deploy scalable and optimized ML models. • Construct and maintain optimized data pipelines to feed ML models. • Use programming languages such as Python, Scala, or Java. • Apply CI/CD best practices, including test automation and monitoring. • Advocate for software and ML engineering best practices across the team. • Serve as a technical lead for large-scale initiatives when required. • Perform statistical analysis, model tuning, and performance evaluation. • Transform data science prototypes into production-ready solutions. • Build data visualization, manipulation, and processing tools for team use. • Ensure high-quality data, reproducible experiments, and actionable analytical outcomes. • Work with development teams to automate data logging and repeatable analytics tasks. Qualifications Required • U.S. Citizenship with the ability to pass a background investigation. • Applicable combination of education and experience: • Mid-Level (T3): Bachelors degree in Computer Science, Engineering, Mathematics, or related field, plus 5+ years of experience working with ML frameworks and production data pipelines. • Senior-Level (T4): Bachelors degree in a related field, plus 7+ years of experience, including 5+ years in data preparation and ML model development. • Proven experience as a Machine Learning Engineer, Data Engineer, or similar role. • Experience developing and deploying ML solutions in public cloud environments. • Proficiency with ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, Pandas, and similar tools. • Strong programming skills in Python, with working knowledge of Java or R. • Solid understanding of data structures, data modeling, and software architecture. • Strong background in mathematics, probability, statistics, and algorithms. • Experience developing and evaluating predictive models using large, high-volume datasets. • Knowledge of statistical techniques such as regression, classification, clustering, hypothesis testing, and optimization. • Ability to analyze relational data models and integrate ML model inputs/outputs into transactional systems. • Self-starter with the ability to work independently and collaboratively. • Strong organizational, time management, and communication skills. • Ability to work effectively in a fast-paced, dynamic environment. Desired • Experience with Spark, Kafka, TensorFlow, PyTorch, or similar big data tools. • Experience building real-time or near real-time ML systems. • Prior experience leading or mentoring small technical teams. • Experience automating business processes using RPA technologies. • Familiarity with operational or enterprise-scale systems.
Publicada el 9 nov 2026
