Senior Data Scientist - FFPP -8757

A data scientist will develop machine learning, data mining, statistical and graph -based algorithms to analyze and make sense of datasets; prototype or consider several algorithms and decide upon final model based on suitable performance metrics; build models or develop experiments to generate data when training or example datasets are unavailable; generate reports and visualizations that summarize datasets and provide data -driven insights to customers; partner with subject matter experts to translate manual data analysis into automated analytics; implement prototype algorithms within production frameworks for integration into analyst workflows.

• Produce data visualizations that provide insight into dataset structure and meaning
  
• Work with subject matters experts (SMEs) to identify important information in raw data and develop scripts that extract this information from a variety of data formats (e.g., SQL tables,structured metadata, network logs)
  
• Incorporate SME input into feature vectors suitable for analytic development and testing
  
• Translate customer qualitative analysis process and goals into quantitative formulations that are coded into software prototypes
  
• Develop and implement statistical, machine learning, and heuristic techniques to create descriptive, predictive, and prescriptive analytics
  
• Develop statistical tests to make data -driven recommendations and decisions
  
• Develop experiments to collect data or models to simulate data when required data is unavailable
  
• Develop feature vectors for input into machine learning algorithms
  
• Identify the most appropriate algorithm for a given dataset and tune input and model parameters
  
• Evaluate and validate the performance of analytics using standard techniques and metrics(e.g. cross validation, ROC curves, confusion matrices)
  
• Oversee the development of individual analytic efforts and guide team in analytic development process
  
• Guide analytic development toward solutions that can scale to large datasets
  
• Partner with software engineers and cloud developers to develop production analytics
  
• Develop and train machine learning systems based on statistical analysis of data characteristics to support mission automation
  

Requirements

This position requires in -scope poly, within 7 years.

• Programming Languages: Proficiency in programming languages such as Python and R is crucial for data manipulation, analysis, and implementing algorithms. Python is favored for its simplicity and extensive libraries (likeNumPy and pandas), while R is preferred for statistical analysis and data visualization.
  
• Statistical Analysis: A strong foundation in statistics and probability is necessary for analyzing data accurately and making informed decisions. Understanding concepts like regression analysis, hypothesis testing, and statistical distributions is essential.
  
• Machine Learning: Knowledge of machine learning algorithms and frameworks (such as TensorFlow and Scikit -Learn) is vital for building predictive models and automating decision -making processes.
  
• Data Wrangling: The ability to clean and organize complex datasets is critical. Data wrangling involves transforming raw data into a usable format, which is often time -consuming but necessary for effective analysis.
  
• Database Management: Familiarity with SQL and database management systems (like PostgreSQL and MongoDB) is essential for extracting and manipulating data stored in relational databases.
  
• Data Visualization: Skills in data visualization tools (such as Tableau and Matplotlib) help communicate findings effectively. Creating charts, graphs, and dashboards is crucial for making data understandable to stakeholders.
  

Benefits