Remote Data Science Engineer Jobs
Remote Data Science Engineer jobs are open across the U.S. in sectors like technology, financial services, and healthcare, at remote-first companies and distributed engineering teams that hire from anywhere. Employers hiring remotely right now include CVS Health, ZS Associates, and eBay. Find a role that fits below and apply directly.
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Lead Data Scientist, Data Science
National Grid is hiring an Lead Data Scientist, for our Syracuse, Brooklyn, Hicksville NY or Waltham MA office. This is a Hybrid Role with work from home availability.
Job Purpose
The Customer Performance Lab is seeking a highly motivated Lead Data Scientist to lead the next generation of AI-enabled analytics solutions across the Customer Organization.
This role will focus on applying Generative AI, machine learning, semantic modeling, and enterprise data platforms to transform how business users consume insights, interact with data, and make decisions. The successful candidate will develop AI-powered analytics solutions that leverage customer operational data, call transcripts, knowledge stores, and semantic layers to deliver scalable business value.
This position will partner closely with Customer Operations, IT, and Business Transformation teams to drive AI adoption and modernize the analytics experience
Key Accountabilities
- Design, develop, and optimize Retrieval-Augmented Generation (RAG) solutions that combine AI models with enterprise knowledge stores, semantic layers, and operational data to improve response accuracy and business relevance.
- Build and maintain AI knowledge architectures, including metadata frameworks, vector stores, business glossaries, semantic models, and contextual data repositories that enable AI systems to understand Customer Operations data and processes.
- Develop reusable AI skills, agents, copilots, and prompt frameworks that automate analytical workflows, KPI interpretation, root cause analysis, dashboard generation, and insight discovery.
- Lead AI cost optimization initiatives by leveraging knowledge stores, retrieval patterns, caching strategies, model selection, and token management techniques to reduce operational expenses while maintaining performance.
- Partner with engineering teams to deploy AI capabilities into business applications, dashboards, and web-based solutions.
- Hands-on experience building and deploying RAG (Retrieval-Augmented Generation) solutions.
- Experience with vector databases, embeddings, semantic search, and document retrieval techniques.
- Experience integrating LLMs through APIs such as OpenAI, Snowflake Cortex, Databricks AI, Anthropic, or similar platforms.
- Experience with prompt engineering, grounding, hallucination mitigation, and AI evaluation frameworks.
#LI-SA1
Qualifications
- Bachelor's, Master’s, PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field.
- Strong Python development skills with demonstrated experience building production-ready analytics, AI, automation, API integration, or data engineering solutions.
- Experience working with enterprise AI technologies, including AI/LLM APIs, prompt engineering, retrieval frameworks, AI assistants, copilots, or workflow automation solutions.
- Experience with Retrieval-Augmented Generation (RAG), vector search, knowledge stores, machine learning, statistical analysis, or predictive modeling is highly desirable.
- Experience deploying solutions through APIs, web applications, dashboards, or enterprise reporting platforms is preferred.
- Strong SQL and data platform experience, including data modeling, transformation, and optimization within modern cloud environments such as Snowflake, Databricks, Microsoft Fabric, or equivalent platforms.
Salary
MA: $153,000 - $180,000 a year DNY: $164,000 - $192,000 a year UNY: $136,000 - $160,000 a year.
National Grid utilizes an assessment that evaluates the job qualifications/characteristics using AI or statistically based scoring.
This position has a career path which provides for advancement opportunities within and across bands as you develop and evolve in the position; gaining experience, expertise and acquiring and applying technical skills. Candidates will be assessed and provided offers against the minimum qualifications of this role and their individual experience.
National Grid is committed to providing equal employment opportunities to all employees and applicants for employment regardless of protected class. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, genetic information, or any other protected status in accordance with applicable federal, state, and local laws. National Grid maintains affirmative action programs for individuals with disabilities and protected veterans.
Our employment practices are designed to ensure that all individuals are treated fairly and with respect throughout the hiring process and during employment. National Grid complies with all applicable federal, state, and local anti-discrimination laws. We are dedicated to fostering a workplace that is free from unlawful discrimination and harassment, and we encourage a culture of respect for all.
Lead Data Scientist, Data Science
National Grid is hiring an Lead Data Scientist, for our Syracuse, Brooklyn, Hicksville NY or Waltham MA office. This is a Hybrid Role with work from home availability.
Job Purpose
The Customer Performance Lab is seeking a highly motivated Lead Data Scientist to lead the next generation of AI-enabled analytics solutions across the Customer Organization.
This role will focus on applying Generative AI, machine learning, semantic modeling, and enterprise data platforms to transform how business users consume insights, interact with data, and make decisions. The successful candidate will develop AI-powered analytics solutions that leverage customer operational data, call transcripts, knowledge stores, and semantic layers to deliver scalable business value.
This position will partner closely with Customer Operations, IT, and Business Transformation teams to drive AI adoption and modernize the analytics experience
Key Accountabilities
- Design, develop, and optimize Retrieval-Augmented Generation (RAG) solutions that combine AI models with enterprise knowledge stores, semantic layers, and operational data to improve response accuracy and business relevance.
- Build and maintain AI knowledge architectures, including metadata frameworks, vector stores, business glossaries, semantic models, and contextual data repositories that enable AI systems to understand Customer Operations data and processes.
- Develop reusable AI skills, agents, copilots, and prompt frameworks that automate analytical workflows, KPI interpretation, root cause analysis, dashboard generation, and insight discovery.
- Lead AI cost optimization initiatives by leveraging knowledge stores, retrieval patterns, caching strategies, model selection, and token management techniques to reduce operational expenses while maintaining performance.
- Partner with engineering teams to deploy AI capabilities into business applications, dashboards, and web-based solutions.
- Hands-on experience building and deploying RAG (Retrieval-Augmented Generation) solutions.
- Experience with vector databases, embeddings, semantic search, and document retrieval techniques.
- Experience integrating LLMs through APIs such as OpenAI, Snowflake Cortex, Databricks AI, Anthropic, or similar platforms.
- Experience with prompt engineering, grounding, hallucination mitigation, and AI evaluation frameworks.
#LI-SA1
Qualifications
- Bachelor's, Master’s, PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field.
- Strong Python development skills with demonstrated experience building production-ready analytics, AI, automation, API integration, or data engineering solutions.
- Experience working with enterprise AI technologies, including AI/LLM APIs, prompt engineering, retrieval frameworks, AI assistants, copilots, or workflow automation solutions.
- Experience with Retrieval-Augmented Generation (RAG), vector search, knowledge stores, machine learning, statistical analysis, or predictive modeling is highly desirable.
- Experience deploying solutions through APIs, web applications, dashboards, or enterprise reporting platforms is preferred.
- Strong SQL and data platform experience, including data modeling, transformation, and optimization within modern cloud environments such as Snowflake, Databricks, Microsoft Fabric, or equivalent platforms.
Salary
MA: $153,000 - $180,000 a year DNY: $164,000 - $192,000 a year UNY: $136,000 - $160,000 a year.
National Grid utilizes an assessment that evaluates the job qualifications/characteristics using AI or statistically based scoring.
This position has a career path which provides for advancement opportunities within and across bands as you develop and evolve in the position; gaining experience, expertise and acquiring and applying technical skills. Candidates will be assessed and provided offers against the minimum qualifications of this role and their individual experience.
National Grid is committed to providing equal employment opportunities to all employees and applicants for employment regardless of protected class. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, genetic information, or any other protected status in accordance with applicable federal, state, and local laws. National Grid maintains affirmative action programs for individuals with disabilities and protected veterans.
Our employment practices are designed to ensure that all individuals are treated fairly and with respect throughout the hiring process and during employment. National Grid complies with all applicable federal, state, and local anti-discrimination laws. We are dedicated to fostering a workplace that is free from unlawful discrimination and harassment, and we encourage a culture of respect for all.
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Find JobsRemote Data Science Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software
- Insurance
- Science & Research
- Consulting & Professional Services
- Energy
What Employers Look For
The qualifications that appear most often in remote data science engineer jobs.
- Proficiency in Python with experience building production-grade data and ML pipelines
- Hands-on experience with distributed computing frameworks such as Apache Spark or Flink
- Experience deploying and monitoring machine learning models in cloud environments like AWS, GCP, or Azure
- Familiarity with MLOps tooling including feature stores, model registries, and workflow orchestrators
- Bachelor's or master's degree in computer science, data engineering, statistics, or a related field
- Experience with containerization and orchestration tools such as Docker and Kubernetes
Tips for Your Remote Data Science Engineer Job Search
Apply early to remote roles that fit
Migrate Mate lists remote data science engineer openings from across the U.S. in one place, so you can find roles that match your skills and apply directly without sorting through listings mixed with on-site or hybrid work.
Build a portfolio that shows end-to-end ownership
Remote employers can't watch you work, so your portfolio does the talking. Publish projects that go from raw data to a deployed model or production pipeline, with clear documentation explaining your decisions, tradeoffs, and results.
Prove async communication in your application
Distributed teams run on written communication. Write detailed, structured cover notes and include a README-style project summary in your application materials that shows you can explain technical work clearly to engineers and non-technical stakeholders alike.
Target remote-first companies with distributed engineering teams
Remote-first technology companies and distributed product teams hire data science engineers remotely as a default, not an exception. Look for companies whose engineering blogs, job postings, and team pages describe fully distributed or async-first cultures rather than hybrid arrangements.
Remote Data Science Engineer Jobs: Frequently Asked Questions
How do I get a remote data science engineer job?
Remote data science engineer roles go to candidates who can demonstrate technical depth and independent execution without in-person oversight. Remote-first companies and distributed product teams screen heavily for async written communication, self-directed project management, and hands-on fluency with tools like Python, SQL, Spark, and cloud ML platforms. A portfolio of end-to-end projects, clear documentation, and evidence you've shipped models that drove measurable outcomes gives you a concrete edge over candidates who lack remote work examples.
Which companies hire remote data science engineers?
Employers currently hiring remote data science engineers include CVS Health, ZS Associates, and eBay, per current remote listings on Migrate Mate as of September 2026. Remote data science engineer roles concentrate at remote-first technology companies, distributed fintech and healthtech teams, and enterprise software firms that run globally distributed engineering organizations.
Can you get a remote data science engineer job with no experience?
Yes, but remote entry-level data science engineer roles are harder to land because employers expect you to contribute independently from day one without on-site mentorship. Early-career candidates who break in typically target remote-first startups, build a public portfolio of end-to-end ML or data pipeline projects, and demonstrate strong async communication through thorough documentation and clear write-ups. Open-source contributions and a completed capstone that shows production-ready thinking open more doors than a degree alone.
Do you need a degree for remote data science engineer jobs?
Not always. Many remote employers weigh a strong project portfolio, demonstrated proficiency in Python, SQL, and ML frameworks, and evidence of shipped work more heavily than a specific degree. A relevant bachelor's or master's in computer science, statistics, or a related field still appears in most job postings, but candidates without one regularly compete successfully by showing real results, published work, and measurable contributions to data or ML systems.
Which industries hire the most remote data science engineers?
Most remote data science engineer openings sit in Technology & Software, Insurance, and Science & Research, per current remote listings on Migrate Mate as of September 2026. These sectors hire data science engineers remotely because their core products, research, and analytics infrastructure are fully digital and can be built and maintained by distributed teams working across time zones.
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