Job Title: Principal Data Scientist
Location: Parsippany, NJ
ADP, Inc. seeks a Principal Data Scientist at our Parsippany, NJ location. Responsibilities: Extract, merge, clean and normalize large-scale data from disparate sources in preparation for exploratory data analysis, model building, and validation within a big data environment. Define how analytics solution will be constructed: which existing solutions will be integrated, how they will be integrated, and what gaps in capability need to be filled. Develop and validate machine learning (ML) classifications and regression models using Python or similar tools to answer business needs and positively impact business operations. Serve as design authority at a project level by understanding the business goals and technology constraints and designing appropriate solutions that create measurable impact to business. Prepare data visualizations and presentations to highlight findings and recommendations for both a technical and executive audience. Promote a unified approach leveraging existing data sets and efforts while also ensuring collaboration throughout the organization to ensure adoption of a common standard approach. Implement appropriate mechanisms to track and measure the business impact of work. Mine large-scale call transcripts data to identify patterns and business-relevant content using ML and AI techniques.
Education and Qualifications/Skills and Competencies:
Bachelor’s degree in Data Science, Statistics, Physics, Machine Learning, Computer Science or a related field plus five (5) years of experience. Employer will accept a master’s degree plus three (3) years of related experience in lieu of a bachelor’s degree plus five years of experience.
Two (2) years of experience must include: Conducting data science work within a big data ecosystem, utilizing Hadoop and Hive; Working with structured, semi-structured and time-series data to build models; ML code and model development using Python; Utilizing Python NLP toolkit (NLPTK) to mine “big data” call transcripts for topics, patterns, and sentiment; ML techniques, including logistic and linear regression, decision trees, random forest; AI techniques, including Convolution Neural Network (CNN), Recurrent Neural Network (RNN) and Long / Short-Term Memory (LSTM); Statistical techniques, including ANOVA and Chi-Square; Bayesian inference/probability with direct application to ML feature engineering; Dimension reduction techniques; Monte Carlo simulation; Distributed computing or optimization of ML code for GPU chipsets; R; Tableau; SQL; Linear and Logistic Regression modeling; Decision Tree and Random Forrest Modeling; and Advanced Data Visualization.
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