Senior Data Scientist Offline
• Master’s degree (preferred) with emphasis on coursework of a quantitative nature (e.g., Statistics, Computer Science, Engineering, Mathematics, Physics, Data Science, Industrial/Organizational Psychology and Econometrics, etc.) and 5 years of experience working in a data analytical or computer programming function
• Working knowledge of relational databases and standard SQL query methods
• Proficiency in at least one general programming language such as r, Python, Java or C/C++
• Experience working with big data within a Hadoop environment
• Experience working with R, SAS or other statistical packages
• Advanced experience with statistical, econometric or data-mining tools and methods
• Experience with supervised /unsupervised learning , linear regression, generalized linear regression, logistic regression , decision trees , random forests and ensemble methods
• Experience with high-performance Deep Learning frameworks such as TensorFlow, PyTorch, Theano
• Strong oral, written, and interpersonal communication skills and an ability to work in a collaborative team setting
Responsibilities
• Leverage R, Python, SQL, & Excel to dig into data to extract meaning from information. Interface closely with business teams to understand/define requirements, domain knowledge/models, and data needs.
• Supports analytics projects and collaborates with cross-functional stakeholders to complete end-to-end analyses that includes business requirements, data gathering, analysis, scale-able solutions deliverables, including visualizations and presentations
• Advanced-level knowledge and skills in data modeling, data structure, and the application of complex SQL queries with data from multiple sources, including a Big Data platform (e.g., Hadoop, AWS, Azure)
• Conceptualize problems, apply appropriate theory, explore approaches, simulate, and implement AI /ML models
• Define data needs, evaluate data quality, and extract/transform data for analytic projects and research
• Demonstrate a strong understanding of project scope, data extraction methodology, design of dependent and profile variables, logic and design of data cleaning, exploratory data analysis and statistical methods
• Produce timely and error-free deliverables in an efficient manner, managing multiple tasks and varying project scopes
• Explore and analyze source data and data flows, working with both structured and unstructured data; Manipulate high-volume, high-dimensional data from a variety of sources to identify value-generating patterns, anomalies, relationships, and trends
• Utilize analytical applications like Python/R and recent Big Data technologies (Hadoop/Spark) to identify trends and relationships between different pieces of data, draw insightful conclusions and translate those analytical findings into next generation control algorithms
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