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Data Scientist

Job in Juno Beach, Palm Beach County, Florida, USA
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)
Salary/Wage Range or Industry Benchmark: 110000 - 170000 USD Yearly USD 110000.00 170000.00 YEAR
Job Description & How to Apply Below
Location: Juno Beach

Job Type: Contract

Experience: 6+ Years

Job Summary

We 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
Preferred 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
Responsibilities
  • 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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