Predoctoral Appointee - Engineer Research Associate
- Argonne National Laboratory
- Location: Lemont, USA
- Job Number: 7322686 (Ref #: 421596)
- Posting Date: Newly posted
Job Description
Argonne Vehicle and Mobility Systems department (VMS) at Argonne National Laboratory is seeking a Predoctoral Appointee to support research and development efforts through data engineering, machine learning and applied software development. The position focuses on transforming complex, large-scale datasets into actionable insights and deployable analytical capabilities that support research, modeling, and decision-making. The Predoctoral Appointee will participate in the design, development, and evaluation of data-driven systems and analytical workflows that support large-scale data modeling and analytics. The role requires a strong foundation in data science and data engineering, with the ability to build efficient, reusable, and scalable data workflows from data collection, data integration, and data management through analysis, machine learning model development, deployment, and visualization.
The Predoctoral Appointee will collaborate closely with multidisciplinary research teams to support research and analytical data needs.
Job Responsibilities:
Designing and implementing end-to-end ETL and data processing pipelines to collect, ingest, clean, transform, store, and query large-scale datasets from diverse sources.
Developing and maintaining data engineering workflows using Python.
Building, training, tuning, and evaluating machine learning models for regression, classification, clustering, and anomaly detection tasks.
Integrating structured, semi-structured, and unstructured data into unified analytical datasets.
Collaborating with researchers to deploy data products.
Creating visualizations, dashboards, or analytical reports to communicate insights and model results to technical and non-technical stakeholders.
Supporting data quality assessment, exploratory data analysis, and model interpretability efforts.
Position Requirements
Recent or soon-to-be completed Master’s degree in Computer Science, Data Science, Data Analytics, Software Engineering, Data Engineering, Machine Learning, Applied Statistics.
Background in data science, computer science, engineering, statistics, applied mathematics, or a related field.
Demonstrated experience applying data science and machine learning techniques to real-world or research-driven problems.
Analytical and problem-solving skills, with the ability to translate research questions into data-driven solutions.
Ability to work collaboratively in a research environment and communicate technical concepts effectively.
Proficiency in Python for data analysis, data engineering, and machine learning.
Experience developing and maintaining data pipelines using workflow orchestration or scheduling tools.
SQL skills and experience working with relational databases, familiarity with NoSQL or document-based databases.
Experience with large-scale data processing frameworks.
Experience integrating data from multiple and heterogeneous sources.
Experience applying machine learning techniques, including supervised and unsupervised learning, model evaluation, and hyperparameter tuning.
Experience with software development best practices, including version control, testing, documentation, and code review.
Experience with data visualization tools or libraries.
A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
This position requires an on-site presence at the Argonne campus in Lemont, Illinois.
Job Family
TemporaryJob Profile
Predoctoral AppointeeWorker Type
Long-Term (Fixed Term)Time Type
Full timeThe expected hiring range for this position is $58,297.00-$97,161.00.Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.
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