Journeyman Data Scientist

ISYS Technologies•Omaha, NE

About The Position

A Journeyman Data Scientist is typically a mid-level professional who works independently on data science projects, develops predictive models, analyzes complex datasets, and collaborates with stakeholders to support business decisions.

Requirements

Minimum Clearance Required Secret
Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
Typically 3–7 years of experience in data science, analytics, machine learning, or a related field.
Experience working with large-scale datasets and production environments.
Proven track record of delivering analytical solutions that drive business outcomes.
Programming: Python, R, SQL.
Machine Learning: Scikit-learn, TensorFlow, PyTorch, XGBoost.
Data Visualization: Tableau, Power BI, Matplotlib, Plotly.

Nice To Haves

Master's degree preferred for some organizations.
TS/SCI clearance

Responsibilities

Collect, clean, and prepare structured and unstructured data from multiple sources.
Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies.
Develop, test, and deploy machine learning and statistical models.
Create predictive analytics solutions to support operational and strategic objectives.
Design and maintain data pipelines and analytical workflows.
Build dashboards, reports, and visualizations to communicate findings.
Validate model performance and recommend improvements.
Conduct data quality assessments and ensure data integrity.
Document methodologies, code, and analytical processes.
Support business units by translating complex data into actionable insights.
Apply statistical analysis, machine learning, and data mining techniques.
Develop algorithms and predictive models using programming languages such as Python, R, or SQL.
Work with large datasets using cloud and big data platforms.
Optimize model accuracy, scalability, and performance.
Implement model monitoring and maintenance procedures.
Partner with business stakeholders to understand requirements and objectives.
Present findings and recommendations to technical and non-technical audiences.
Support data-driven decision-making across departments.
Identify opportunities for process improvement and automation.
Collaborate with data engineers, software developers, analysts, and project managers.
Mentor junior data scientists and analysts.
Participate in code reviews and knowledge-sharing activities.
Follow organizational standards, governance, and security requirements.