Immediate need for a Data Scientist to support our customer, a Federal Healthcare Insurance organization. The selected candidate will be responsible for identifying and solving business problems by using various numerical techniques, algorithms, and models in statistical modeling, machine learning, operations research, and data mining. The selected candidate will use advanced analytical capabilities to support data science initiatives. Communicates across product teams and with customers and educates on artificial intelligence, machine learning, and statistical models. Acting as a liaison and interface between analytics, business units and other departments.
Each of our clients data scientists owns their own high-impact projects, whether it is a new modeling technique delivering better predictions, a new analysis and visualization highlighting weak spots in our pipelines, or a new tool that can help the team-s workflow. We-re looking for a Data Scientist who can leverage their prior experience and take on and lead large data science projects, from inception to completion. In addition to contributing technically throughout the project, you will also responsible for: working with product to refine product requirements, managing/coordinating the efforts of other data scientists contributing to the project, and interfacing with stakeholders throughout the company.
This position is a remote opportunity with rare onsite requests. Want to learn more about this long term contract opportunity? Then you should contact Elite Technical right away for consideration!
- 5+ years as a Data Scientist
- Python and PySpark are required. Advanced proficiency in Python and Spark/Scala for classical statistical analysis and data modeling, machine learning and ETL processes. Intermediate to advanced ability to create data visualizations using Python.
- AWS Cloud knowledge (AWS migration considered big plus)
- Ability to write production-ready code including documentation and unit tests.
- Experience with machine learning methods like k-nearest neighbors, random forests, ensemble methods and more.
- Proficiency in data science modeling - AI, Machine Learning, Deep Learning, Decision Trees, Random Forest, Neural Networks, Supervised/Unsupervised Learning, Forecasting, Predictive Modeling and Clustering.
- Strong background in machine learning using unsupervised and supervised methods.
- Deep knowledge of fundamentals of machine learning, data mining and statistical predictive modeling, and extensive experience applying these methods to real world problems
- Fluency in SQL and other programming languages. Some development experience in at least one scripting language (PHP, Python, Perl, etc.)
- Strong skills in software prototyping and engineering with expertise in applicable programming and analytics languages (Python, R, Spark/Scala) and various open source machine learning and analytics packages to generate deliverable modules and prototype demonstrations of their work.
- Proven experience of using Python Machine Learning & Data Pre-processing Libraries. (Scikit Learn, Numpy, Pandas)
- Ability to initiate and drive projects to completion with minimal guidance
- The ability to communicate the results of analyses in a clear and effective manner
- Ability to transform concepts into practical solutions. Experience with data science/ML techniques
- Machine Learning experience -- Successful implementation of ML solutions
- Communication Ability -- Experience/comfort in presenting abstract concepts
Preferred, not required:
- Preferred experience with a statistical package such as R, MATLAB, SPSS, SAS, Stata, etc.
- Proficiency with healthcare analytics and data structures is preferred.
- Desired interdisciplinary skills include big data technologies, ETL, statistics and causal inference, Deep Learning, modeling and simulation. - Experience with large data sets and distributed computing (Hive/Hadoop) a plus.
- Leading data science projects or teams (as the most technically advanced team member) or working independently on data science projects.
Monday, December 18, 2023
Wednesday, November 29, 2023
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