Senior Data Scientist

New

JobgetherData science

USContractSenior

Salary43.06 - 71.76 USD per hour

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Job Details

Languages: Excellent written and verbal English communication skills
Experience: At least five years of relevant professional experience; a combination of higher education and work experience totaling at least nine years must include a minimum of five years of related experience.
Required Skills: PythonSQLETLMicrosoft Power BITableauData scienceNosql

Have a bachelor's degree and at least five years of relevant professional experience, or a combination of higher education and work experience totaling at least nine years, including at least five years of related experience.
Have at least five years of experience in data science, statistics, econometrics, quantitative analysis, or a related discipline.
Demonstrate experience working with large, complex, and diverse datasets.
Understand A/B testing, sample selection, hypothesis testing, model validation, and bias analysis.
Have proficiency with statistical software, programming languages, and analytical tools used for data exploration, modeling, and reporting.
Have intermediate knowledge of SQL and NoSQL databases and practical experience performing ETL using SQL and Python.
Have experience with hybrid database environments spanning on-premises infrastructure and cloud platforms.
Be familiar with Bayesian modeling, classification, clustering, neural networks, non-parametric techniques, and multivariate statistics.
Have experience creating data visualizations and communicating analytical insights using Tableau and Power BI.
Have hands-on experience with Alteryx.
Have excellent written and verbal English communication skills and be able to explain technical findings clearly.
Preferred: a master's degree or doctorate in a quantitative discipline, advanced econometric methods, and experience deploying machine learning models in enterprise production environments.

Analyze large, complex datasets to solve business problems using statistical analysis, econometrics, and machine learning.
Source, ingest, clean, transform, and prepare data for analysis and reliable data pipelines.
Design, develop, validate, and implement analytical models, including classification, clustering, simulations, and predictive modeling.
Build champion/challenger models and refine performance based on accuracy, reliability, stability, and business feedback.
Develop self-healing model frameworks and support machine learning deployment in enterprise environments.
Use SQL, Python, Alteryx, Tableau, and Power BI for statistical analysis, data engineering, visualization, and reporting.
Create model outputs, visualizations, reports, and educational materials to communicate findings to stakeholders.
Collaborate with data scientists, business partners, and technical teams to establish research approaches and analytical methods.
Review code for accuracy, efficiency, maintainability, quality, and compliance with development best practices.
Mentor team members and monitor data quality, model stability, and scalability across cloud and on-premises environments.

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