Data scientist lockheed martin

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Lockheed Martin accelerates data science with Domino Data Lab

Enterprise data scientists are frustrated by the Sisyphean struggle to get the technology assets they require to build data models. But that’s hardly the only hurdle: Because these projects slow-cook in siloes, data science teams often duplicate efforts. It’s a maddening combination of requisitioning hell and redundancies.

No stranger to such challenges, defense contractor Lockheed Martin installed a software platform to make the development of machine learning (ML) and artificial intelligence (AI) models more efficient. The platform centralizes assets required to build data models, reducing the costs of the company’s ML and AI projects by $20 million a year, says Matt Seaman, Lockheed Martin’s chief data and analytics officer of enterprise operations.

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The self-service capabilities are critical for the company’s approach to democratizing access to data, Seaman says. “We’re reducing the barriers to start and run new projects that will help us make better and faster decisions with data.”

Adoption of self-service technology is soaring, representing the next phase of a consumerization phenomenon that put mobile computers and applications into the hands of millions of workers more than a decade ago. But perhaps nowhere is the interest greater than in data science, in which the potential of advanced analytics that helps discover business insights has been constrained by the same clunky processes that have long held back companies from reaching their potential.

Clearing the provisioning hurdle


Pushing the Boundaries of Rocket Science with Data Science

During their talk, they shared how Domino and NVIDIA are helping them create an MLOps framework to bring development, security, and operations best practices to machine learning and achieve a 10 to X efficiency gain for data scientists.

Here are three examples they shared:

  1. Reducing supply chain risks. Lockheed Martin is in the process of building the Orion crew module for human spaceflight that is targeted to go to the moon in A project of this type requires many different unique components and there's significant risk if a part can not be supplied on time. To reduce risk of delays, data scientists are building models with Domino and NVIDIA that analyze open source unstructured data, such as information about a merger between two suppliers; classify and assess potential risks; and provide insight on what might happen next (for example, if a company may experience financial losses). These insights are combined with extensive internal knowledge of the company’s supply chain to give supply chain professionals a better sense of any risks so they can respond to them.

    With Domino and NVIDIA, the company has been able to train these models with effectively no training data—something that required a large amount of compute power--and rapidly deploy interfaces so they could create an active learning loop to obtain feedback from individuals on how the models are performing.

    “The ability to kind of keep track of that data and assign it to these projects really made this project a success.”

    —Mike Johnson, Lead Data Scientist, Lockheed Martin

  2. Building trust in AI systems through its participation in the AlphaDogFight competition, hosted by DARPA. Participants were tasked to develop an AI fighter system that could compete with humans in a Top Gun style aircraft fight. Lockheed was one of many teams, and it secured second place by taking a reinforcement learning approach and using Domino and NVIDIA to spin up multiple training jobs (each of which took on the order of 30 days) and track experiments.

    “Before we had Domino, it took an intern eight weeks to just get access to a GPUNow it's just a push button and it's those savings added up across thousands of employees at scale that really make the difference.”

    —Greg Forrest, Senior Manager of AI and AiMLabs, Lockheed Martin

  3. Predicting equipment failures before they affect operations. In his final example, Mike Johnson shared what it takes to build an F35 aircraft and the number of different operations that have to happen (all in one mile-long building) to turn raw materials into an aircraft. To minimize equipment downtime, the company is prototyping an AI-driven system that can continuously monitor every machine for potential issues and can notify staff when machines aren’t operating properly. So far, using Domino and NVIDIA, around three machine learning engineers were able to develop and train over models and deploy models at the same time.

About the Speakers

Mike Johnson is the technical lead for a team of data scientists, machine learning engineers, and data engineers to deliver AI solutions across Lockheed Martin. He has built machine learning solutions in numerous fields including manufacturing optimization, semiconductor reliability, human resources, radar signal analysis, and time series search. While he has an undying love of natural language processing, he has recently been focused on applying deep learning at scale to the domain of unsupervised anomaly detection.

Greg Forrest is responsible for leading the development of Lockheed Martin’s corporate artificial intelligence strategy, creating an enterprise AI ecosystem and MLOps pipeline, and leading teams that develop transformative AI and machine learning capabilities for Lockheed Martin and its customers.

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Data Scientist (Skill Level 2) *$10K sign on eligible*

Req#: BR

Job Description
This position is participating in our External Referral Program. If you know somebody who may be a fit, click hereto submit a referral. If your referral is hired, you'll receive a $ payment! code-extrefer

This job may be eligible for a $10K sign on bonus for external hires!
At Lockheed Martin Rotary and Mission Systems, Cyber & Intelligence, we are driven by innovation and integrity. We believe that by applying the highest standards of business ethics and forward-thinking thinking, everything is within our reach – and yours as Lockheed Martin employee. Lockheed Martin values your skills, training and education. Come and experience your future!

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 authorities to translate manual data analysis into automated analytics; implement prototype algorithms within production frameworks for integration into analyst workflows.

This vacancy requires a TS/SCI w/Poly clearance

We may not know what's going to change the world next, but chances are we're already working on it, and you can, too. As part of our culture of innovation, you’ll have excellent benefits and amenities, an expansive work environment, ongoing career development and support, rewards and recognition to honor your hard work, and more.

- Medical

- Dental

- k

- Paid time off

- Work/life balance

- Career development

- Mentorship opportunities

- Rewards & recognition

Learn more about Lockheed Martin’s driven and comprehensive benefits package.

This job may be eligible for a $10K sign on bonus for external hires!
Basic Qualifications
- Bachelor's and Master's degree from an accredited college or university in a quantitative subject area (e.g., statistics, mathematics, operations research, engineering or computer science).

- 5 years of validated ability analyzing datasets and developing analytics, consistent record programming with data analysis software such as R, Python, SAS, or MATLAB. An additional two years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Master's degree.

- A PhD from an accredited college or university in a quantitative subject area can be substituted for 3 years of validated ability.
Desired skills
- Produce data visualizations that provide insight into dataset structure and meaning.

- Work with subject matters professionals (SMEs) to identify relevant 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 build descriptive, predictive, and prescriptive analytics.

- Develop experiments to collect data or models to simulate data when required data are unavailable. Develop feature vectors for input into machine learning algorithms.
Lockheed Martin is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.

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A look inside Lockheed Martin's Advanced Technology Center

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The Chief Data Scientist provides the organization with data insights, analytics, and tools to achieve business goals, foster innovation, and promote data-driven decision-making. Develops and executes the data science vision, strategy, and capacity to support organizational long-term, short-term, and mission-critical goals. Being a Chief Data Scientist research and incorporates emerging technologies and methods to continuously improve analytic capabilities and enhance capacity to provide valuable results. Engages stakeholders and champions the design and development of data science initiatives. In addition, Chief Data Scientist builds team capability with mentoring, coaching, and professional development. Possesses a deep and broad knowledge of data science, analytical concepts and methods, and business operations. Typically requires a master's degree in computer science, mathematics, engineering, or related area . Typically reports to top management. The Chief Data Scientist manages a business unit, division, or corporate function with major organizational impact. Establishes overall direction and strategic initiatives for the given major function or line of business. Has acquired the business acumen and leadership experience to become a top function or division head. (Copyright

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Lockheed Martin’s Collaborative Advanced Analytics \u0026 Data Sharing Platform


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