Lead Data Scientist- W2- Remote

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Role: Senior Consultant Lead Data Scientist ---Remote

Location: Remote

Exp: 10+

Contract: W2

Visa: USC

Skill Required / Desired Amount Of Experience

• 3+ years of experience working with large and varying data sets, applying qualitative and quantitative analysis to interpret the data.

Required 3 Years

• 1+ year hands-on Data Science experience working from data prep, modeling and feature engineering all the way through to de

Required 1 Years

• Develop algorithms and predictive models to derive insights and business value from data.

Required 1 Years

• Test and validate algorithms and models using Machine Learning, Deep Learning and other modern techniques/methodologies.

Required 1 Years

• Demonstrable proficiency in coding Python, R, JupyterNotebooks, MySQL, PostgreSQL, Microsoft SQL Server, Tableau, Microsoft Power BI, MS Office

Required 3 Years

• Strong knowledge and experience with statistical methods and machine learning algorithms: Hypothesis testing, probability theory, regression, etc.

Required 2 Years

• Hands on experience with Data Science notebooks such as Anaconda, Jupyter, or Zeppelin

Required 2 Years

• Strong understanding of machine learning methods such as classification, feature selection, clustering, neural networks, etc.

Required

1 Years

• Experience and proficiency in utilizing statistical/analytic packages such as SAS, R, SPSS, S-Plus, Matlab to develop statistical models

Nice to have

2

Years

• Understanding of and experience with building canned and ad-hoc reports based on user requirements.

Nice to have

2 Years Master's Degree in Information Technology, Data or Computer Science, quantitatively focused social sciences, or other quantitative fields.

Required 5 Years

Need an individual with analytical & programming background, with applied Data Science experience, curiosity, and passion towards Big Data technology, to serve as a Lead Data Scientist for our Data, Analytics, Research and Standards Unit. Judiciary is working in the areas of validation of algorithmic tools, NLP (Natural Language Processing), Text Mining, Deep Learning, and the creation of a data analytics platform to solve business problems and build predictive analytics products.

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.