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Resp & Qualifications
PURPOSE:Identifies and solves business problems by using various numerical techniques, algorithms, and models in statistical modeling, machine learning, operations research, and data mining. Uses 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.ESSENTIAL FUNCTIONS:- Leads data mining and extraction activities and applies algorithms to derive insights.
- Uses data to understand the data profile and its limitations and applies it to business solutions.
- Collaborates with business units to innovate organizational performance.
- Explores data patterns using existing data science tools.
- Translates business priorities to data science deliverables.
- Implements new data products and builds robust and scalable software.
- Leads/Guides DS on implementing best practices for projects and applies artificial intelligence techniques to achieve concrete business goals while managing limited resources and constraints around data.
- Preferred experience with a statistical package such as R, Pyspark, etc.
- Preferred experience with AWS and Sagemaker Platforms
- 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.
- Intermediate to advanced ability to create data visualizations using Python.
- Leading data science projects or teams (as the most technically advanced team member) or working independently on data science projects.
- Experience with large data sets and distributed computing (Hive/Hadoop) a plus.
- 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 ability to influence cross-functional teams without formal authority.
- Advanced proficiency in Python and Spark/Scala for classical statistical analysis and data modeling, machine learning and ETL processes.
- 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.).
- 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.
- Experience Working with cloud Technologies.