Temporary Machine Learning Scientist Jobs in Arizona

RESPONSIBILITIES:
Kforce's client in Phoenix, AZ is seeking a Data Scientist II to support advanced analytics and machine learning initiatives that drive business outcomes. This role will focus on analyzing large datasets, developing predictive models, and delivering actionable insights to stakeholders across the organization. The ideal candidate has hands-on experience building, evaluating, and deploying machine learning models while working closely with business and technical teams. Exposure to MLOps practices and machine learning lifecycle management is preferred but not required.
Key Responsibilities:
* Develop, test, and optimize machine learning models to solve business challenges and generate actionable insights
* Perform statistical analyses, forecasting, hypothesis testing, and predictive modeling on large and complex datasets
* Partner with business stakeholders to identify opportunities where data science can improve decision-making and operational performance
* Conduct exploratory data analysis to uncover patterns, trends, and opportunities
* Design experiments and evaluate outcomes to support strategic initiatives
* Build and maintain analytical datasets, reports, dashboards, and visualizations
* Present findings and recommendations to both technical and non-technical audiences
* Support model deployment, monitoring, and performance measurement in production environments
* Collaborate with data engineers, analysts, and technology teams throughout the data science lifecycle
* Stay current on emerging machine learning and AI techniques and recommend practical applications
REQUIREMENTS:
* Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related quantitative discipline
* 3-4+ years of experience in data science, machine learning, predictive analytics, or a similar role
* Hands-on experience building and validating machine learning models using Python
* Strong understanding of supervised and unsupervised learning techniques
* Experience with statistical analysis, predictive modeling, and data mining methodologies
* Advanced SQL skills and experience working with large datasets
* Experience using visualization and reporting tools such as Power BI
* Excellent communication skills with the ability to translate technical findings into business recommendations
Preferred:
* Master's degree in a quantitative field
* Experience working in AWS, Azure, or other cloud-based environments
* Exposure to MLOps concepts, including model deployment, monitoring, CI/CD pipelines, and model lifecycle management
* Experience with machine learning frameworks such as Scikit-Learn, TensorFlow, PyTorch, or XGBoost
* Familiarity with predictive analytics, forecasting, optimization, customer analytics, or operational analytics use cases
The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.
We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.
Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce's sole discretion unless and until paid and may be modified in its discretion consistent with the law.
This job is not eligible for bonuses, incentives or commissions.
Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.
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