Data Scientist

Job Description

Base-2 Solutions is seeking a Data Scientist who 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.

• 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 (like Num Py 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 Tensor Flow 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 Postgre
    
    SQL and Mongo
    
    DB) 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.

• Bachelor's degree from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science).
• Five (5) years of experience analyzing datasets and developing analytics, five (5) years of experience programming with data analysis software such as R, Python, SAS, or MATLAB.
  • An additional four (4) years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Bachelor's degree.
• A PhD from an accredited college or university in a quantitative discipline can be substituted for four (4) years of experience.

• Produce data visualizations that provide insight into dataset structure and meaning.
• Work with subject matter 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 are 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.