Data Scientist

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Location:

Posted on Feb 6, 2025
Tampa, Florida, United States

AREA:

Accounting & Finance

Empowering people who build the future.

BASIC FUNCTION:

Build and implement scalable machine learning solutions across the entire ML lifecycle, from data processing to model deployment. Work with cross-functional teams to deliver impactful solutions while designing and implementing end-to-end machine learning pipelines and optimizing data processing workflows.

Primary Duties & Responsibilities:

%	Description of duty/responsibility
70%	Design and implement end-to-end machine learning pipelines Build and optimize data processing workflows Deploy and monitor ML models in production environments Create proof-of-concept applications to validate ML solutions Define model monitoring criteria Ensure model quality assurance Perform exploratory data analysis to provide insights to be used in decision making
30%	Collaborate with engineering teams to develop scalable solutions Document technical processes and maintain code quality standards Participate in stakeholder management and cross-functional collaboration Monitor analytics project pipeline and track analytics impact on business

SCOPE:

Work across the entire machine learning lifecycle while collaborating with cross-functional teams. Design, implement, and maintain scalable ML solutions, from data processing to model deployment. Responsible for optimizing performance, ensuring code quality, and delivering impactful solutions that meet business requirements.

Qualifications and Educational requirement(s):

Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or related field
3+ years of professional experience in data science or machine learning

Technical Requirements:

1. Programming Languages and Tools:

Python (Advanced): pandas, numpy, scikit-learn, matplotlib, seaborn, TensorFlow/PyTorch
SQL: Advanced query optimization and database design
Shell Scripting: Bash/Shell for automation
R, Java/Scala, JavaScript (Nice to have)

2. Tools and Platforms:

Version Control: Git
Containers: Docker, Kubernetes
Cloud Platforms: AWS, GCP, or Azure
ML Ops: MLflow, DVC, or similar
Big Data: Spark, Hadoop ecosystem
CI/CD: Jenkins, GitLab CI, or similar

Required Skills:

Data processing and feature engineering
Model development and optimization
ML pipeline design and implementation
Production deployment and monitoring
Performance optimization
Code versioning and documentation
Strong problem-solving abilities
Excellent written and verbal communication
Project management capabilities
Attention to detail

Preferred Qualifications:

Experience with ML ops tools
Knowledge of distributed computing frameworks
Experience with real-time prediction systems
Familiarity with A/B testing and experimental design
Relevant certifications (AWS ML Specialty, Google Professional ML Engineer)
Open-source contributions or research publications
Experience with agile development methodologies

Gerdau believes in equal employment opportunity related to all employees and applicants for employment. It is the policy of Gerdau that there will be no discrimination against any employee or applicant for employment on the basis of race, religion, color, national origin, citizenship, marital status, sex, age, sexual orientation, genetic information, gender identity, veteran status, disability, or other legally protected status. All external applicants must be legally eligible to immediately work in the country of hire without current or future sponsorship.

If you are vision-impaired or have some other disability under the Americans with Disabilities Act / Accessible Canada Act or a similar law, and you wish to discuss potential accommodations related to applying for employment at Gerdau, please contact our Talent Acquisition Team at (866)-788-2798 or recruiting@gerdau.com

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