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Remote Data Scientist Deep Learning Jobs in Poughkeepsie, NY

The ideal candidate will have deep expertise in project scheduling, cross-functional coordination ... Lead and mentor a team of remote consultants, supporting their development and optimizing workload

The Product Owner acts as a link between product management and the engineering team using data to ... Technical background with a deep understanding of software development processes * Experience ...

Engineering & Science Job Schedule: Full time Remote: Yes The Opportunity Are you ready to take ... Candidate data is processed in accordance with applicable data protection and employment laws as ...

Remote Data Scientist Deep Learning information

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$37K

$121.3K

$194.1K

How much do remote data scientist deep learning jobs pay per year?

As of Aug 31, 2026, the average yearly pay for remote data scientist deep learning in Poughkeepsie, NY is $121,264.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,300.00 and $134,400.00 per year, depending on experience, location, and employer.

What is a remote data scientist specializing in deep learning?

Remote data scientists specializing in deep learning are professionals who use advanced machine learning techniques, particularly deep neural networks, to analyze large amounts of data and extract meaningful insights. They work from remote locations, leveraging digital tools to build, train, and deploy deep learning models for tasks such as image recognition, natural language processing, and predictive analytics. These experts collaborate with other team members virtually, contributing to projects in industries like healthcare, finance, and technology without needing to be physically present in an office.

What are the key skills and qualifications needed to thrive as a remote data scientist specializing in deep learning?

To thrive as a Remote Data Scientist specializing in Deep Learning, you need a strong background in mathematics, statistics, programming (especially Python), and experience with deep learning frameworks such as TensorFlow or PyTorch, often supported by a relevant degree. Familiarity with cloud platforms (e.g., AWS, GCP), version control systems like Git, and certifications in machine learning are highly beneficial. Strong analytical thinking, problem-solving abilities, and effective remote communication skills help you stand out in this position. These skills and qualities are essential for designing robust models, collaborating with distributed teams, and delivering impactful AI solutions.

How do remote data scientists specializing in deep learning typically collaborate with cross-functional teams?

Remote Data Scientists in Deep Learning often work closely with software engineers, product managers, and domain experts through virtual meetings, shared documentation, and version-controlled code repositories. They collaborate on defining project goals, sharing model insights, and integrating machine learning solutions into products. Effective communication and clear documentation are crucial, as team members may be in different time zones or have varying technical backgrounds. Tools like Slack, JIRA, and GitHub are commonly used to streamline collaboration and track progress.

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For Remote Data Scientist Deep Learning jobs in Poughkeepsie, NY, the most frequently searched job titles are:

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What cities near Poughkeepsie, NY are hiring for Remote Data Scientist Deep Learning jobs?

Cities near Poughkeepsie, NY with the most Remote Data Scientist Deep Learning job openings:

Postdoctoral Associate - Freshwater Nitrous Oxide Modeling

Millbrook, NY • On-site, Remote

Cary Institute of Ecosystem Studies
Scientific Research and Development Services • 51 - 200 employees

$74K/yr

Full-time

Re-posted 22 days ago


Job description

This is a full time, exempt, fully benefitted position. The starting salary is $74,263.

Position summary

The Cary Institute of Ecosystem Studies invites applications for a postdoctoral associate in freshwater biogeochemistry and machine learning. The postdoc will collaborate on an NSF-funded project developing a knowledge-guided machine learning model to quantify global riverine nitrous oxide (N2O) emissions. The project combines process-based modeling with machine learning, and involves compiling and synthesizing global databases of riverine N2O measurements alongside large-scale geocomputation to upscale results across the global river network. The postdoc will have the opportunity to build interdisciplinary expertise spanning freshwater biogeochemistry, geospatial data science, and machine learning, and to contribute to research relevant to international climate assessments. Depending on interest and opportunity, there may be scope for the postdoc to engage in limited field-based research on riverine N2O dynamics, complementary to the core project.

Funding for the position is secured for 2 years. The initial appointment is for one year, with possibility of extension based on performance. The anticipated start date can be as early as September 2026, with flexibility. The position is based at Cary Institute in the beautiful Hudson Valley of New York, a short way north of New York City. The position may have locational flexibility for exceptional candidates. Cary Institute is home to a diverse, vibrant, and supportive community of colleagues.

Questions about the position and project may be directed to Dr. Taylor Maavara at Cary Institute.

Essential responsibilities

Design, plan, and execute scientific research in collaboration with the PI.

Compile, harmonize, and synthesize a global database of riverine N2O measurements and predictor variables from published and contributed sources.

Develop and calibrate a knowledge-guided machine learning framework and apply it via large-scale geocomputation across the global river network.

Present research findings in peer-reviewed papers, at scientific meetings, and in other forums.

Qualifications

Applicants should have expertise in freshwater or riverine biogeochemistry, particularly nitrogen cycling; strong quantitative skills, including experience with process-based modeling and/or machine learning methods; experience handling and synthesizing large, heterogenous environmental datasets; and familiarity with geospatial data science and large-scale geocomputation. A strong record of publishing peer-reviewed research is expected. Applicants should be self-motivated and comfortable working both independently and in close collaboration with the PI. The successful applicant must have completed a PhD in a relevant field before the start date of this position.

Working conditions

The work is primarily office-based. Occasional travel for meetings is required. Cary Institute operates on a 35 hour work week.

Closing date

Review of applications will begin July 27, 2026 and continue until the position is filled.

To apply

Visit our website at https://www.caryinstitute.org/... and complete our online job application. Submit a single file containing a brief cover letter, names and contact information for three professional references, and a curriculum vitae.

The Cary Institute is an Equal Employment Opportunity (EEO) employer. It is the policy of the Institute to provide equal employment opportunities to all qualified applicants without regard to rate, color, religion, sex, sexual orientation, gender identity, national origin, age, familial status, protected veteran or disabled status, or genetic information.

Employment Type: FULL_TIME