Remote Senior Data Science Engineer Jobs
Remote Senior Data Science Engineer jobs are open across the U.S. in technology, finance, healthcare, and enterprise software, at remote-first companies and distributed teams that depend on senior data science engineers to build and own production-grade ML systems. 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 Senior 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 senior data science engineer jobs.
- 5 or more years of experience in data science, machine learning, or a related engineering discipline
- Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Hands-on experience designing and deploying production-grade machine learning pipelines
- Strong command of distributed computing tools including Spark, Databricks, or equivalent platforms
- Experience with cloud platforms such as AWS, Google Cloud, or Azure for data and model infrastructure
- Bachelor's or master's degree in computer science, statistics, mathematics, or a closely related field
Tips for Your Remote Senior Data Science Engineer Job Search
Apply early to remote roles that fit
Migrate Mate lists remote senior data science engineer openings from across the U.S. in one place so you can find roles that match your stack and seniority and apply directly. Remote postings from in-demand employers fill fast, so applying early matters.
Build a portfolio that shows production ML work
Remote employers can't watch you work, so your public portfolio does that job for them. Document end-to-end projects: problem framing, data pipeline choices, model selection rationale, and business outcomes. Code on GitHub with clear READMEs signals the async communication habits remote teams need.
Demonstrate async collaboration skills explicitly
Remote senior data science engineers are expected to drive alignment without synchronous standups. Highlight experience writing design documents, async code reviews, and stakeholder updates in writing. Teams hiring remotely screen for this more carefully than technical skills alone.
Target remote-first companies and distributed teams
Remote-first firms and companies with fully distributed engineering teams have built their workflows around remote collaboration, which makes onboarding, tooling, and expectations clearer from day one. Look for roles where the entire team is remote, not just the position.
Remote Senior Data Science Engineer Jobs: Frequently Asked Questions
How do I get a remote senior data science engineer job?
Target remote-first companies and distributed engineering teams, which hire senior data science engineers without geographic restrictions and expect candidates who own work end-to-end. Remote employers screen heavily for self-direction, strong written communication, and the ability to drive projects asynchronously. A portfolio showing deployed models, documented decisions, and measurable business impact gives you a clear edge over candidates with credentials alone.
Which companies hire remote senior data science engineers?
Companies hiring remote senior data science engineers right now include CVS Health, ZS Associates, and eBay, based on current remote listings on Migrate Mate as of September 2026. Remote-first firms and distributed teams across technology, fintech, and healthcare tend to hire most actively for this role.
Can you get a remote senior data science engineer job with no experience?
Yes, but remote entry roles are harder to land because employers expect you to work independently from day one without in-person support. Remote-first startups and smaller distributed teams are more open to early-career candidates who show strong fundamentals. A public GitHub portfolio, contributions to open-source ML projects, and demonstrated async communication skills can substitute for formal work history in those environments.
Do you need a degree for remote senior data science engineer jobs?
Not always. Many remote employers weight demonstrated skills, production ML experience, and a portfolio of shipped work over formal credentials. A degree in a quantitative field helps, particularly at larger companies with structured hiring, but remote-first firms and early-stage teams routinely hire senior data science engineers who can show results through public projects, professional work, or contract contributions.
Which industries hire the most remote senior data science engineers?
Most remote senior 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 run distributed data and ML teams that operate effectively without centralized offices, making remote hiring a standard practice rather than an exception.
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