Data Scientist Skill Level 2

Onyx Point•Haniver, MD

4d•$78,000 - $275,000

About The Position

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. 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 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 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

Requirements

TO BE CONSIDERED FOR THIS POSITION YOU MUST CURRENTLY HAVE AN ACTIVE TS/SCI WITH POLYGRAPH SECURITY CLEARANCE WITH THE FEDERAL GOVERNMENT. (U.S. CITIZENSHIP REQUIRED).
This position requires a 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 (like NumPy 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.
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.

Responsibilities

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 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

Benefits

We offer a comprehensive benefits package, including:
Health Coverage: Medical, dental, and vision insurance
Additional Insurance: Basic Life/AD&D, Voluntary Life/AD&D, Short and Long-Term Disability, Accident, Critical Illness, Hospitalization Indemnity, and Pet Insurance
Retirement Plan: 401(k) plan with company match
Paid Time Off: Generous PTO, paid holidays, parental leave, and more
Wellness: Access to wellness programs and mental health support
Professional Development: Opportunities for growth, including tuition reimbursement
Additional Perks: Flexible work arrangements, including remote work options
Flexible Spending Accounts (FSAs)
Employee referral programs
Bonus opportunities
Technology allowance
A diverse, inclusive, and supportive workplace culture