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Data Scientist (Python) (FTL-Hybrid)

  Tiempo completo   Fort Lauderdale
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Tu rol en SIXT

SIXT – US is seeking a talented and motivated Data Scientist to join our team. As a Data Scientist, you will play a crucial role in analyzing and deriving insights from complex datasets to drive data-driven decision-making within our organization. You will leverage your expertise in Python programming and data science techniques to develop predictive models, perform statistical analysis, and generate actionable insights.

Must reside 20 miles from Fort Lauderdale, FL. This role is a hybrid role - (3 days Onsite, 2 days remote)

No relocation assistance is offered for this role.

**PLEASE READ ** No sponsorships available at this time; candidates must be authorized to work in the US and not require any sponsorships now or in the future. **

Key Responsibilities:

1. Data Exploration and Preparation: Gather, clean, and preprocess large datasets from diverse sources to ensure data quality and suitability for analysis.

2. Statistical Analysis and Modeling: Apply advanced statistical methods and machine learning algorithms to identify patterns, trends, and relationships within the data. Develop and validate predictive models to solve business problems and optimize processes.

3. Model Development and Deployment: Design, build, and deploy machine learning models into production environments. Collaborate with software engineers to integrate models into scalable and efficient systems.

4. Data Visualization: Create clear and informative visualizations of analysis results using Python libraries such as Matplotlib, Seaborn, and Plotly. Communicate findings and insights to stakeholders through compelling visual presentations.

5. Continuous Learning and Improvement: Stay updated with the latest developments in data science and machine learning. Continuously enhance your skills and knowledge to drive innovation and improvement in data science practices within the organization.

Lo que ofrecemos

Must reside 20 miles from Fort Lauderdale, FL. This role is Hybrid - (3 days Onsite, 2 days remote)

No relocation assistance is offered for this role.

About us:
We are a leading global mobility service provider with sales of €3.07 billion and around 7,500 employees worldwide. Our mobility platform ONE combines our products SIXT rent (car rental), SIXT share (car sharing), SIXT ride (cab, driver and chauffeur services), SIXT+ (car subscription) and gives our customers access to our fleet of 270,894 vehicles, the services of 1,500 cooperation partners and around 1.5 million drivers worldwide. Together with our franchise partners, we are present in more than 110 countries at 2,098 rental stations. At SIXT, a first-class customer experience and outstanding customer service are our top priorities. We focus on true entrepreneurship and long-term stability and align our corporate strategy with foresight. Want to take off with us and revolutionize the world of mobility? Apply now!

Lo que traes contigo

  • Bachelor's or higher degree in Computer Science, Data Science, Mathematics, Statistics, or a related quantitative field.
  • Proficiency in Python programming and experience with data manipulation libraries such as Pandas and NumPy.
  • Strong analytical and problem-solving skills, with a proven ability to derive actionable insights from complex datasets.
  • Experience in statistical analysis, machine learning, and predictive modeling using libraries like SciPy, Statsmodels, and Scikit-learn.
  • Familiarity with data visualization techniques and tools, including Matplotlib, Seaborn, and Plotly.
  • Experience in deploying machine learning models into production environments is a plus.
  • Excellent communication and collaboration skills, with the ability to effectively convey technical concepts to non-technical stakeholders.

No sponsorships available at this time; candidates must be authorized to work in the US.

Join our team and be part of a dynamic and innovative environment where your contributions will have a significant impact on our business success.

Póster en 2024-04-01 19:20:00