Data Scientist Poly SR DS

Description

This senior role fosters collaboration with other senior engineers for the development of advanced data analytics solutions and agile development projects in support of a high‑visibility mission. This position involves providing technical leadership and guidance on data analytics and agile development projects, as well as collaborating with cross‑functional teams to drive mission objectives.

At Lockheed Martin, we’re a leading aerospace and defense company that shapes the future of cyber and intelligence. We’re committed to innovating at the Edge:
Harnessing the latest advancements in cyber, artificial intelligence, and machine learning to stay ahead of emerging threats and opportunities.

This Program 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.

Proven past experience as a Data Scientist with programming experience, statistical analysis, machine learning, data wrangling, database management, and data visualization skills.

Providing ongoing training, mentorship, and development opportunities to help our cyber and intelligence professionals stay at the forefront of their field and achieve their career goals.

Competitive and comprehensive benefits package.

Rewards and recognition for your hard work.

Medical and dental coverage.

401k retirement savings plan.

Paid time off for work/life balance.

And more.

• TS/SCI w/Poly
• 5+ years of Data Scientist experience on programs of similar size and scope.
• Bachelor’s degree from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science).
• Five years of experience analyzing datasets and developing analytics, and five years programming with data analysis software such as R, Python, SAS, or MATLAB.
• An additional four 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 years of experience.

• Programming
  
  Languages:
  
  Proficiency in 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.