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Remote Deep Learning Jobs in Lancaster, TX (NOW HIRING)

Director, Medical Economics

Dallas, TX · Remote

$178K - $234K/yr

This is a remote position, open to candidates who reside in: Dallas, TX. You will be fully remote ... Promote operational efficiency and shared learning by actively sharing successful tactics ...

Director, Medical Economics

Dallas, TX · Remote

$178K - $234K/yr

This is a remote position, open to candidates who reside in: Dallas, TX. You will be fully remote ... Promote operational efficiency and shared learning by actively sharing successful tactics ...

This role requires deep technical ownership across DFT architecture, scan insertion, ATPG, MBIST ... Familiarity with yield learning, diagnosis, and manufacturing test optimization.

In this role, you will balance deep hands-on technical expertise with strategic leadership ... Act as a technical mentor, fostering a culture of continuous learning, psychological safety, and ...

Remote but must be located in the Central US Region Travel: 80% Reports to: Manager, Product ... Deep understanding of public safety workflows, including PSAP and law enforcement operations ...

Remote, (Onsite) Duration: Contract Year of Exp: 8+ yrs to 15 yrs. We are seeking a highly ... This role requires deep technical ownership across DFT architecture, scan insertion, ATPG, MBIST ...

Remote, (Onsite) Duration: Contract Year of Exp: 8+ yrs to 15 yrs. We are seeking a highly ... This role requires deep technical ownership across DFT architecture, scan insertion, ATPG, MBIST ...

Showing results 41-60

Remote Deep Learning information

See Lancaster, TX salary details

$10.4K

$79.5K

$132.7K

How much do remote deep learning jobs pay per year?

As of Aug 7, 2026, the average yearly pay for remote deep learning in Lancaster, TX is $79,524.00, according to ZipRecruiter salary data. Most workers in this role earn between $68,300.00 and $131,800.00 per year, depending on experience, location, and employer.

What is a remote deep learning engineer?

A Remote Deep Learning job involves working with artificial intelligence and machine learning models, particularly using deep neural networks, from a location outside a traditional office, often from home. Professionals in this field design, build, and optimize algorithms that enable computers to learn from large amounts of data. They often work on projects such as image and speech recognition, natural language processing, or autonomous systems. The remote aspect allows flexibility and access to global opportunities, but requires strong communication skills and the ability to collaborate virtually with teams.

What are common challenges faced by remote deep learning engineers, and how can they be addressed?

Remote deep learning engineers often encounter challenges such as limited access to high-performance computing resources, communication barriers with distributed teams, and difficulties in collaborating on large codebases or datasets. These issues can be mitigated by leveraging cloud-based platforms for scalable computing, using clear communication tools like Slack or Zoom for regular check-ins, and employing version control systems like Git for collaborative code management. Proactively setting up workflows and documentation helps ensure smooth collaboration and project continuity within a remote environment.

What is the difference between Remote Deep Learning vs Remote Machine Learning Engineer?

AspectRemote Deep LearningRemote Machine Learning Engineer
Required CredentialsBachelor's/Master's in CS, AI, or related; experience with neural networksBachelor's/Master's in CS, Data Science, or related; experience with algorithms and data modeling
Work EnvironmentCollaborative teams, research-focused, often in tech or AI companiesDevelopment teams, data-driven projects, across various industries
Employer & Industry UsageTech firms, AI startups, research institutionsTech companies, finance, healthcare, e-commerce

Remote Deep Learning specialists focus on designing and training neural networks for AI applications, often requiring advanced knowledge of deep neural architectures. Remote Machine Learning Engineers work on developing algorithms and models for broader data analysis and predictive tasks. While both roles involve machine learning, deep learning emphasizes neural networks, whereas machine learning engineers may work with a variety of algorithms across industries.

What skills and qualifications are needed to thrive as a remote deep learning engineer?

To thrive as a Remote Deep Learning Engineer, you need strong programming skills in Python, a deep understanding of machine learning algorithms, and typically a degree in computer science, engineering, or a related field. Proficiency with frameworks like TensorFlow or PyTorch, as well as cloud computing platforms such as AWS or Google Cloud, is essential, and certifications in these technologies can be advantageous. Excellent problem-solving abilities, self-motivation, and clear communication are crucial soft skills for remote collaboration and project delivery. These skills ensure effective development, deployment, and maintenance of deep learning models while working independently in distributed teams.
What job categories do people searching Remote Deep Learning jobs in Lancaster, TX look for? The top searched job categories for Remote Deep Learning jobs in Lancaster, TX are:
What cities near Lancaster, TX are hiring for Remote Deep Learning jobs? Cities near Lancaster, TX with the most Remote Deep Learning job openings:

Director, Medical Economics

Oscar Health

Dallas, TX • Remote

$178K - $234K/yr

Full-time

Medical, PTO

Re-posted 12 days ago


Oscar Health rating

6.9

Company rating: 6.9 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

251st of 303 rated insurance


Job description

Hi, we're Oscar. We're hiring a Director, Medical Economics to join our Actuarial team.

Oscar is the first health insurance company built around a full stack technology platform and a relentless focus on serving our members. We started Oscar in 2012 to create the kind of health insurance company we would want for ourselves-one that behaves like a doctor in the family.

About the role:

The Director, Medical Economics, plays an instrumental role in Oscar's medical economics operating model, serving as a dedicated, proactive financial and analytic partner to a Market Vice President. You will be the point person accountable for supporting trend management and achieving market affordability targets. Rather than just tracking data, you will identify, size, and diagnose medical cost and utilization drivers, translating data into action. You will work as a strategic "quarterback," to triage deep-dive analytics to centralized analytic teams such as network performance, forecasting, and data science when appropriate, while maintaining deep understanding and ownership over your markets' context, goals, and results. You will manage your team to contribute analyses, reports, and dashboards to the medical economics tooling suite, building to meet market level needs in a way that is standardized, repeatable and re-usable across markets.

You will report to the Senior Director, Medical Cost Analytics.

Work Location: This is a remote position, open to candidates who reside in: Dallas, TX. You will be fully remote; however, our approach to work may adapt over time. Future models could potentially involve a hybrid presence at the hub office associated with your metro area. #LI-Remote

Pay Transparency: The base pay for this role is: $178,848 - $234,738 per year. You are also eligible for employee benefits, participation in Oscar's unlimited vacation program, company equity grants and annual performance bonuses.

Responsibilities:

  • Trend Management Accountability & Partnership: Partner with regional Market teams, regional actuaries, and market medical officers to co-lead regional trend management and drive total cost of care reduction strategies.
  • Proactive Opportunity Identification: Lead the proactive identification, sizing, and root-cause analysis of medical cost and utilization anomalies ("flares") and identification of affordability opportunities within assigned regions.
  • Executive Communication: Present comprehensive, executive-ready analytics and materials
  • Team Leadership: Mentor analysts in developing both analytic expertise and "soft skills," specifically regarding business writing, data visualization, and partner influence.
  • Analytic Quarterbacking: Act as a primary gateway and triage point for your market team's medical economics requests, effectively routing complex requests to centralized analytic teams such as network performance, forecasting, and data science when appropriate, while managing end-to-end follow-up with market leadership.
  • Affordability Integration: Connect local market programs and emerging cost flares into Oscar's centralized affordability framework and governance programs.
  • Playbook Development: Collaborate with central affordability and local market teams to develop localized trend management strategies.
  • Shared Tooling & Innovation: Contribute to the department's core tooling strategy by building analytics tailored to specific market needs with an eye toward scaling them into national solutions via our internal tooling program. Develop best practices in analytics, automation, and documentation, contributing to department programs around innovation, and tooling improvements. Improve adoption of generative AI tools to improve team effectiveness.
  • Enablement & Self-Service: Provide support and training to market leadership teams to ensure self-service utilization of medical economics reports & tooling.
  • Cross-Pollination: Promote operational efficiency and shared learning by actively sharing successful tactics, playbooks, and localized analytic tools across different regional markets.
  • Compliance with all applicable laws and regulations
  • Other duties as assigned

Requirements:

  • Bachelor's degree in a STEM field, or 4 years commensurate experience.
  • 10+ years of quantitative analysis in the healthcare industry.
  • Experience with medical economics, corporate strategy, or a related analytics-driven leadership role.
  • Experience with health insurance / payer analytics, with an understanding of medical claims data (e.g., CPT/HCPCS, ICD-10, DRGs) and standard healthcare industry data sources.

Bonus points:

  • Business writing and storytelling skills; ability to simplify complex actuarial concepts for executive audiences.
  • Familiarity with ACA-specific healthcare dynamics and how they impact external financial reporting.
  • Fellow of the Society of Actuaries (SOA), or on the track to become one.

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