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Remote Science Jobs in Olney, MD (NOW HIRING)

Imagery Scientist (EO)- Expert

Falls Church, VA ยท On-site +1

$180K - $210K/yr

Active TS/SCI Clearance with the ability to obtain a CI/Poly * 4+ years of expert EO imagery exploitation and imagery science experience * Demonstrated expertise in: * EO collection systems * Remote ...

Remote micro1 is engaging Computational Biology Experts to contribute their advanced scientific knowledge to a dynamic customer project. In this role, you'll apply your expertise to help train next ...

GPU Programmer - Remote

Washington, DC ยท Remote

$60 - $85/hr

GPU Programmer - Remote Job Type: Contractor Location: Remote Job Overview We are seeking ... Background in graphics programming, ML acceleration, scientific computing, HPC, or related GPU ...

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Showing results 41-60

Remote Science information

See Olney, MD salary details

$25.1K

$49.5K

$80.9K

How much do remote science jobs pay per year?

As of Aug 21, 2026, the average yearly pay for remote science in Olney, MD is $49,529.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,400.00 and $53,200.00 per year, depending on experience, location, and employer.

What is remote science?

Remote science jobs are positions in scientific fields that can be performed outside of traditional laboratory or office environments, typically from home or any location with internet access. These jobs may include roles in research, data analysis, scientific writing, consulting, or education. Advances in technology and communication tools have made it possible for scientists to collaborate, conduct experiments, and analyze data remotely. Remote science jobs offer flexibility and can help employers and employees reach a broader talent pool. Common areas include biology, chemistry, environmental science, and healthcare research.

What skills and qualifications are needed to thrive as a remote science professional?

To thrive as a Remote Science professional, you need a strong background in your scientific discipline, analytical skills, and typically a relevant degree or higher qualification. Familiarity with data analysis tools, virtual collaboration platforms, and scientific software such as Python, R, or MATLAB is important. Excellent written communication, time management, and self-motivation are standout soft skills in this remote environment. These abilities ensure effective research, collaboration, and productivity while working independently from various locations.

What are common challenges faced by professionals working in remote science roles, and how can they be addressed?

Professionals in remote science roles often face challenges such as effective communication across time zones, limited access to lab equipment, and maintaining collaboration with team members. To address these issues, it is helpful to establish regular virtual check-ins, utilize collaborative digital tools, and set clear expectations for project milestones. Many teams also adopt cloud-based data sharing and remote access to specialized software, ensuring that scientific work continues smoothly despite physical distance.

What is the difference between Remote Science vs Remote Data Analyst?

AspectRemote ScienceRemote Data Analyst
Required CredentialsScience degrees, research experience, technical skillsStatistics, data analysis certifications, technical skills
Work EnvironmentResearch labs, academic institutions, remote research projectsBusiness, finance, tech companies, remote data analysis roles
Employer & Industry UsageUniversities, research institutes, biotech firmsCorporations, consulting firms, tech startups
Search & Comparison IntentUnderstanding research roles, scientific projectsData analysis tasks, business insights

Remote Science and Remote Data Analyst roles share a focus on technical skills and remote work environments. However, Remote Science typically involves research, scientific experiments, and academic or biotech settings, while Remote Data Analysts focus on interpreting data for business insights in corporate environments. Both roles require analytical skills but differ in industry application and specific credentials.

What remote science jobs are there?

Remote science jobs include roles such as research scientists, data analysts, laboratory technicians, and scientific writers. These positions often require specialized knowledge, relevant degrees, and skills in data analysis, laboratory techniques, or scientific software, and may involve collaboration through digital communication tools.

What cities near Olney, MD are hiring for Remote Science jobs?

Cities near Olney, MD with the most Remote Science job openings:

Infographic showing various Remote Science job openings in Olney, MD as of August 2026, with employment types broken down into 4% Internship, 80% Full Time, 13% Part Time, and 3% Contract. Highlights an 100% Remote job distribution, with an average salary of $49,529 per year, or $23.8 per hour.

Principal Scientist AI/ML Specialization - WFH1651 (Remote)

Global InfoTek, Inc.

Reston, VA โ€ข Remote

Full-time

Re-posted 23 days ago


Job description

Clearance Level:

US Citizenship: Required

Job Classification: Full Time

Location: Remote

Years of Experience: 10+ years of relevant experience

Education Level: Advanced degree (MS or PhD) in Electrical Engineering, Computer Science, Applied Mathematics, or a closely related quantitative field. Experience may be considered in place of education requirement.

Briefly Describe the Work:

GITI is seeking a Principal Scientist to serve as the senior technical authority on an R&D program focused on passive RF emitter identification and network analysis from real-time sensor data streams. The Principal Scientist leads independent, hands-on analysis of NDF (Network Description File) sensor datasets, provides technical direction across parallel research threads, and serves as the primary technical advisor to the government sponsor. The role spans the full research lifecycle: formulating hypotheses, writing and executing analytical code in Python and Jupyter notebooks, interpreting and validating results, and communicating findings to both technical peers and non-specialist stakeholders. This is a deeply technical, hands-on position the Principal Scientist conducts analysis directly and does not delegate technical work as a substitute for personal proficiency. The candidate will work within a small, distributed team operating in air-gapped Linux environments on resource-constrained tactical edge hardware, with no cloud computing.

Responsibilities:

  • Conduct independent, hands-on data analysis on RF sensor datasets using Python and Jupyter notebooks formulating hypotheses, writing and running analytical code, interpreting results, and producing findings that directly advance program research objectives
  • Provide technical advice and research direction across a multidisciplinary team; define analytical objectives, review and validate technical outputs from AI/ML engineers and software developers, and ensure coherence across parallel research threads
  • Serve as primary technical advisor to the government sponsor: translate operational requirements into research objectives, communicate findings clearly to non-specialist stakeholders, and maintain program alignment with sponsor priorities through written reports and technical presentations
  • Design and execute analytical investigations into RF sensor data quality, emitter behavior, and attribution reliability including characterizing error sources, identifying systematic artifacts, and developing methods to distinguish real physical signatures from sensor or processing artifacts
  • Produce technical documentation working notes, research findings, monthly status reports, and briefing materials that accurately represent the scope and confidence level of analytical results

Expert-level career professional recognized as a technical authority in RF systems, signals intelligence, or a closely related applied domain. Exercises broad independent judgment in defining research approach, evaluating methods, and interpreting results. Operates with minimal supervision; accountable for the scientific integrity and practical relevance of program research outputs. Advanced degree (MS or PhD) with 10+ years of hands-on applied R&D experience.

Required Skills:

  • 10+ years of hands-on applied R&D experience in RF systems, signals intelligence, electronic warfare, or related domains.
  • Proven ability to quickly acquire domain knowledge; specifically in the areas of wireless digital communications and military techniques, tactics, and procedures
  • Demonstrated ability to independently develop and execute data analyses in Python or equivalent tools on real sensor datasets; must be capable of writing production-quality analytical code, not merely directing others to do so
  • Experience addressing common problems with large quantities of real-world data, such as imputation, noise, bias, and errors
  • Track record of working effectively on constrained-hardware edge systems no cloud, no discrete GPU with attention to computational efficiency and multi-core, multi-thread performance on x86 platforms

Desired Skills:

  • Deep familiarity with RF signal characteristics, sensor phenomenology, and the interpretation of passive receiver data including recognition of processing artifacts, attribution ambiguities, and the limits of sensor-derived measurements
  • Hands-on experience applying machine learning particularly metric learning, deep learning networks, or similarity-learning architectures to RF or time-series signal data, including feature engineering, training pipeline development, and model validation
  • Familiarity with TDMA network protocols, emitter identification techniques (CID/PID), and the signal processing challenges of dense, contested electromagnetic environments
  • Experience with interferometric direction-finding, TDOA geolocation, or related passive geolocation methods, including practical knowledge of their failure modes and accuracy limitations
  • Experience with binary serialization formats (FlatBuffers, Protocol Buffers) and high-throughput sensor data pipelines operating in near-real-time on resource-constrained hardware
  • Background in statistical signal processing error ellipses, bearing estimation uncertainty, feature reliability under noise with the ability to distinguish statistically significant findings from artifacts of small sample size or improper normalization

Relevant Certifications:

  • Professional certifications in data science, signal processing, or related technical fields. Advanced academic credentials (PhD, MS) in a relevant quantitative discipline are strongly preferred and may substitute for certifications.

Global InfoTek, Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability.

About Global InfoTek, Inc. Global InfoTek Inc. has an award-winning track record of designing, developing, and deploying best-of-breed technologies that address the nation's pressing cyber and advanced technology needs. GITI has rapidly merged pioneering technologies, operational effectiveness, and best business practices for over two decades.