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What are the key skills and qualifications needed to thrive as a Data Scientist, and why are they important?

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, typically supported by a degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks, data visualization tools, and big data platforms like TensorFlow, Tableau, and Hadoop, as well as certifications in data science, are highly valued. Excellent problem-solving skills, curiosity, and the ability to communicate complex findings clearly set outstanding data scientists apart. These skills and qualities are crucial for extracting actionable insights from data, driving business decisions, and collaborating effectively with stakeholders.

What are some typical projects Data Scientists work on, and how do they collaborate with other teams?

Data Scientists often work on projects such as building predictive models, analyzing large datasets to uncover trends, and developing data-driven solutions to business problems. They regularly collaborate with cross-functional teams, including software engineers, data engineers, and business analysts, to ensure that their insights are actionable and aligned with business goals. Effective communication and teamwork are essential, as Data Scientists frequently need to present complex findings to non-technical stakeholders and incorporate feedback from various departments.

What are Data Scientists?

Data Scientists are professionals who use statistical, analytical, and programming skills to collect, analyze, and interpret large volumes of data. They extract insights and trends from complex data sets to help organizations make data-driven decisions. Data Scientists often work with machine learning, data mining, and big data technologies to build predictive models and solve business problems. Their work bridges the gap between technical data analysis and actionable business strategy.

What is the job of a data scientist?

A data scientist analyzes large datasets to extract insights and support decision-making using statistical methods, programming, and data visualization tools. They often work with machine learning models and require skills in programming languages like Python or R, as well as knowledge of databases and data analysis techniques.

What is the difference between Data Scientist vs Data Analyst?

AspectData Scientist
Required CredentialsDegree in Computer Science, Statistics, or related field; often requires advanced degrees
Work EnvironmentResearch and development, predictive modeling, machine learning projects
Employer & Industry UsageTech companies, finance, healthcare, consulting firms
Common Search & ComparisonOften compared due to overlapping skills in data analysis and modeling

Data Scientists focus on building predictive models, advanced analytics, and machine learning, often requiring higher-level technical skills and education. Data Analysts primarily interpret existing data, generate reports, and support decision-making with descriptive analytics. While both roles analyze data, Data Scientists handle complex modeling and predictive tasks, whereas Data Analysts focus on data interpretation and reporting.

What other companies are hiring for Data Scientist jobs?
What are the most popular jobs at Shield Ai?
What are the most popular categories at Shield Ai?
Infographic showing various Data Scientist job openings at Shield Ai in the United States as of May 2026, with employment types broken down into 98% Full Time, and 2% Part Time. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution.
Product Manager, AI Platforms (R4991)

Product Manager, AI Platforms (R4991)

Shield AI

San Diego, CA

Full-time

Posted 20 days ago

Job description

Founded in 2015, Shield AI is a venture-backed deep-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include the V-BAT and X-BAT aircraft, Hivemind Enterprise, and the Hivemind Vision product lines. With offices and facilities across the U.S., Europe, the Middle East, and the Asia-Pacific, Shield AI's technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. 

Job Description:
 
The AI Platform Product Manager will drive the strategy and execution of Shield AI's next-generation autonomy intelligence stack-enabling customers and internal teams to train, evaluate, and deploy foundation and domain models that power resilient autonomy at the edge. This PM owns the product vision and roadmap for the Hivemind AI Platform (Forge, training pipelines, data infrastructure, evaluation, and deployment toolchains), ensuring we can manufacture, govern, and field advanced world models, robotics foundation models, and vision-language-action systems safely and at scale. 
 
This role sits at the intersection of AI/ML, autonomy, model lifecycle, infrastructure, and product strategy. The PM partners closely with engineering, AI research, Hivemind Solutions, and field teams to deliver the tooling that enables sovereign autonomy, AI Factories at the edge, and continuous learning-capabilities that are central to Shield AI's strategic direction. 
 
This is a high-impact role for an experienced product leader excited to define how foundation models are trained, validated, governed, and deployed across thousands of autonomous systems in highly contested environments.
What you'll do:
  • AI Model Development & Training Platform
  • Own the roadmap for foundation model training workflows, including dataset ingestion, curation, labeling, synthetic data generation, domain model training, and distillation pipelines.
  • Define requirements for world models, robotics models, and VLA-based training, evaluation, and specialization.
  • Lead the evolution of MLOps capabilities in Forge, including data lineage, experiment tracking, model versioning, and scalable evaluation suites.
  • Data, Simulation & Synthetic Data Factory
  • Define product requirements for synthetic data generation, simulation-integrated data flywheels, and automated scenario generation.
  • Partner with Digital Twin, Simulation, and autonomy teams to convert natural-language mission inputs into data needs, training procedures, and model variants.
  • Safe Deployment & Model Governance
  • Lead the development of model governance and auditability tooling, including model cards, dataset rights, lineage tracking, safety gates, and compliance evidence.
  • Build guardrails and workflows to safely deploy models onto edge hardware in disconnected, GPS- or comms-denied environments.
  • Partner with Safety, Certification, Cyber, and Engineering teams to ensure traceability and evaluation pipelines meet operational and accreditation requirements.
  • Edge Deployment & AI Factory Integration
  • Partner with Pilot, EdgeOS, and hardware teams to integrate foundation-model-based perception and reasoning into autonomy behaviors.
  • Define requirements for distillation, quantization, and inference tooling as part of the "three-computer" development and deployment model.
  • Ensure closed-loop workflows between cloud model training and edge-native execution.
  • Cross-Functional Leadership
  • Collaborate with Engineering, Research, Product, Customer Engagement, and Solutions teams to ensure model outputs meet mission and platform constraints.
  • Translate advanced AI capabilities into intuitive workflows that platform OEMs and partner nations can use to build sovereign AI factories.
  • Sequence foundational capabilities that unblock autonomy, simulation, and customer-facing product teams.
  • User & Customer Impact
  • Develop deep empathy for ML engineers, autonomy developers, and Solutions engineers who rely on the platform.
  • Capture operational data gaps, mission-driven model needs, and domain-specific specialization requirements.
  • Lead demos and onboarding for model-development capabilities across internal and external teams.
Required qualifications:
  • 7+ years of experience in product management or highly technical ML/AI product roles.
  • 2+ years of experience in a hands-on software development role.
  • Strong engineering background (Computer Science, Electrical Engineering, Robotics, or related field).
  • Deep understanding of foundation models, robotics models, multimodal models, MLOps, and training infrastructure.
  • Experience managing complex products spanning data pipelines, cloud training clusters, model governance, and edge deployments.
  • Proven success partnering with research teams to transition ML innovations into stable, production-grade workflows.
  • Familiarity with simulation-based data generation and large-scale data management.
  • Excellent communicator with strong cross-functional leadership skills.
Preferred qualifications:
  • Experience working on autonomy, robotics, embedded AI, or mission-critical systems.
  • Hands-on familiarity with GPU infrastructure, distributed training, or data lakehouse architectures.
  • Experience supporting defense, dual-use, or safety-critical AI systems.
  • Background designing or operating AI Factory-style pipelines (data training evaluation distillation edge deployment).
  • Advanced degree in engineering, ML/AI, robotics, or a related field.
$190,000 - $290,000 a year
#LI-DM2
#LE

Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity

Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)

Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.

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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know. 
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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