Data Scientist
Job in
Juno Beach, Palm Beach County, Florida, USA
Listed on 2026-08-30
Listing for:
Arbor TekSystems
Full Time
position Listed on 2026-08-30
Job specializations:
-
IT/Tech
Machine Learning/ ML Engineer, Data Scientist, Data Analyst, AI Engineer (Applied/Software)
Job Description & How to Apply Below
Job Type: Contract
Experience: 6+ Years
Job SummaryWe are looking for an experienced Data Scientist with 6+ years of hands-on experience in developing machine learning models, performing advanced data analysis, and delivering data-driven solutions. The ideal candidate should have strong expertise in Python, SQL, Machine Learning, Statistics, and data visualization, along with experience working with large and complex datasets.
Mandatory Skills- 6+ years of experience in Data Science / Machine Learning
- Strong proficiency in Python
- Hands-on experience with Machine Learning algorithms
- Strong understanding of Statistics and Probability
- Experience with Pandas, Num Py, Scikit-learn
- Experience in data preprocessing, feature engineering, and model development
- Experience with model validation, tuning, and performance evaluation
- Experience with data visualization using Tableau, Power BI, Matplotlib, or Seaborn
- Strong experience working with large datasets
- Ability to translate business problems into data science solutions
- Strong analytical and problem-solving skills
- Experience with Deep Learning
- Experience with AWS, Azure, or GCP
- MLflow or similar ML lifecycle tools
- Experience deploying ML models into production
- Experience with MLOps and CI/CD
- Analyze large datasets to identify trends, patterns, and business insights.
- Develop, train, validate, and deploy machine learning models.
- Perform data cleaning, preprocessing, feature engineering, and exploratory data analysis.
- Collaborate with business, engineering, and product teams to understand requirements.
- Evaluate model performance and continuously improve model accuracy.
- Build dashboards and visualizations to communicate analytical findings.
- Present complex analytical results to technical and non-technical stakeholders.
- Develop scalable and production-ready data science solutions.
- Document methodologies, models, findings, and recommendations.
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