Data Scientist

A data scientist will have skills sets of both data analysts and data engineers. Data scientists are responsible for designing, implementing, and maintaining a data pipeline. In addition, data scientists shall interpret and analyze complex sets of data, as well as plan, execute, and manage ML projects with cloud-native platforms and advanced ML solutions. They understand some of the most challenging processes, technologies, and can leverage a vast array of methodologies in the field, such as data mining, natural language programming, and machine learning.
Data scientists must have a combination of skills that includes programming, mathematical modeling, statistics, and domain knowledge. They must combine an advanced math and statistics background with programming, domain knowledge, and communication skills to analyze data, create applied mathematical models, and present results in a form useful to the organization. They must also be able to understand and manipulate structured and unstructured large data sets, which requires proficiency in distributed SQL programming, relational and non-relational data queries, general programming languages (such as Python and R,) and machine learning techniques.

The Data Scientist shall perform the following tasks:
• Interpret and analyze data using exploratory mathematic and statistical techniques based on the scientific method.
• Coordinate research and analytic activities utilizing various data points (unstructured and structured) and employ programming to clean, massage, and organize the data
• Experiment against data points, provide information based on experiment results and provide previously undiscovered solutions to command data challenges.
• Coordinate with Data Engineers to build Data environments providing data identified by other data professionals
• Apply and develop scientific methodology, statistics, and algorithms to discover and frame relevant problems, hypotheses, and opportunities.
• Develop predictive and prescriptive modeling, natural language processing (NLP), Robotic Process Automation (RPA), text mining and processing, clustering, forecasting methods, and other advanced statistical techniques.
• Design and automate processes to facilitate the manipulation and analysis of data. Manage and integrate data across dissimilar data sets. Analyze large-scale structured and unstructured data.
• Use frameworks such as Spark and Hadoop to conduct large-scale data processing. Perform statistical modeling and create data visualizations using products like Tableau, Microsoft Power BI and R Shiny.
• Research, design, and implement algorithms to solve complex problems. Program using R, Python (NumPy, SciPy, Pandas) or similar analytical languages.
• Perform data engineering, data processing and modeling techniques using cloud-based data management, data science, and ML platforms such as Databricks, IBM Cloud Pak, Cloudera, and Snowflake.
• Communicate complex concepts and hypothesis to a non-technical audience through digital storytelling.