Lead Data Scientist

About the Job Job Title: Senior Consultant Lead Data Scientist Location: Remote Duration: 6 Months Big Data experience is mandatory Responsibilities: • Develop and apply quantitative and qualitative analytic methods to identify, collect, process and analyze large data sets for specified purposes. • Develop and / or validate predictive models that are scalable, repeatable, effective, and meet the expectations of the decision-makers and stakeholders. • Serve as an expert resource in the design and creation of a data analytics platform and the various data transformations and coding needed to create and maintain the data pipeline. • Serve as a cross-product expert, providing technical guidance in Machine Learning, Natural Language Processing, Data Mining and Information Retrieval experiments and projects. • Analyze use cases, understand user behaviors, identify repetitive and/or error prone manual human processes that can be augmented or automated. • Develop polished, high-impact persuasive reports and presentations that enable strategic decision-making supporting the project’s mission. Requirements: • Master’s Degree in Information Technology, Data or Computer Science, quantitatively focused social sciences, or other quantitative fields. • 3+ years of experience working with large and varying data sets, applying qualitative and quantitative analysis to interpret the data. • 1+ year hands-on Data Science experience working from data prep, modeling and feature engineering all the way through to deployment. • Develop algorithms and predictive models to derive insights and business value from data. • Test and validate algorithms and models using Machine Learning, Deep Learning and other modern techniques/methodologies. • Demonstrable proficiency in coding Python, R, Jupyter Notebooks, MySQL, PostgreSQL, Microsoft SQL Server, Tableau, Microsoft Power BI, MS Office • Strong knowledge and experience with statistical methods and machine learning algorithms: Hypothesis testing, probability theory, regression, etc. • Hands on experience with Data Science notebooks such as Anaconda, Jupyter, and Zeppelin • Strong understanding of machine learning methods such as classification, feature selection, clustering, neural networks, etc. • Experience and proficiency in utilizing statistical/analytic packages such as SAS, R, SPSS, S-Plus, Matlab to develop statistical models • Understanding of and experience with building canned and ad-hoc reports based on user requirements. Strong analytical skills with the ability to analyze data sets to determine trends, establish strategies, and make decisions.