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Job Title: Data Scientist (Must have AWS Service Cloud, Sagemaker and Formation Exp)
Job Location: 100% Remote
Duration: Long-Term Contract
Rate: Open
Required Skills/Experience
Statistical modeling and data analytics experience including exploratory data cleansing, data analysis, feature engineering, hypothesis testing, multivariate linear and logistic regression, Analysis of Variance (ANOVA), design of experiments, factorial analysis, post-hoc analysis, dimensionality reduction, computer vision, emotion and sentimental analysis.
Prediction modeling techniques including multivariate linear/logistic regression, decision trees, random forests, support vector machines, and Bayesian networks. Considerable experience with Natural Language Processing and information retrieval.
Considerable experience in R programming, Python, SQL, JMP, SPM, FSL, MATLAB, Octave, Tableau, PowerBI, Teradata. Proficient with ggplot2, shiny, dplyr, plotly, numpy, matplotlib, nltk, scikit-learn, scikit-image, opencv, tensorflow, spacy.
Experience
Engaged in a wide variety of pre-sale architecting phase with customers and partners, helping them architect scalable, highly available solutions leveraging AWS
Research, recommend, plan, prototype, evaluate, deploy and optimize Data Science models and Serverless products, identify new opportunities and drive efforts that improved IT processes and Customer Experience
Provide Machine Learning solutions for enterprise customers
Experience deploying models in customer aws cloud computing environments
Designed, developed and deployed ML models into production environments
Designed and implemented aws cloud based microservice architectures using CI/CD Pipelines and Docker
Experience with Machine Learning, Recommendation Systems, and Natural Language Processing
Architected machine learning deployment solutions for production environments on AWS, utilizing CloudFormation, Amazon SageMaker, EMR, S3, DynamoDB, Lambda, and other AWS services
Fluency in Python, experienced with Scala
Create presentations, visualizations, storytelling dashboards and reports for C level, board of directors and pre-sales technical customer meetings.
Implement full development lifecycle process for Machine Learning/Deep Learning projects from EDA, processing, training, evaluation, retraining, cloud/on-premises deployment and monitoring.