Remote Data Science Analyst Jobs
Remote Data Science Analyst jobs are in active demand across the U.S., with remote-first firms and distributed teams hiring across technology, finance, healthcare, and retail analytics. Employers posting remote roles 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 Analyst 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 analyst jobs.
- Proficiency in Python or R for data analysis and statistical modeling
- Strong SQL skills for querying and manipulating large relational datasets
- Experience with data visualization tools such as Tableau or Power BI
- Bachelor's degree in statistics, computer science, mathematics, or a related field
- Familiarity with machine learning libraries such as scikit-learn or TensorFlow
- Experience communicating analytical findings clearly to non-technical stakeholders
Tips for Your Remote Data Science Analyst Job Search
Build a portfolio that proves async delivery
Remote employers want evidence you can take a vague question and return a clear, documented analysis without back-and-forth. Publish end-to-end projects on GitHub with a written narrative explaining your methodology and findings, not just code.
Signal remote readiness in every application
Remote hiring managers scan for clues that you've worked independently before. Call out async tools you've used, such as Notion, Confluence, or Jira, and mention any distributed team experience in your cover letter or work history, even from freelance or volunteer projects.
Apply early to remote roles that fit
Migrate Mate lists remote data science analyst openings from across the U.S. in one place, so you can find roles that match your skills and apply directly without bouncing between multiple sites.
Sharpen written communication for remote interviews
Remote data science analyst interviews often include a take-home case or async video screen before any live call. Practice writing up your analytical findings as short, executive-style summaries, because your written clarity is part of what remote teams are hiring for.
Remote Data Science Analyst Jobs: Frequently Asked Questions
How do I get a remote data science analyst job?
Remote data science analyst roles go to candidates who can demonstrate independent execution and clear written communication, because you won't have a manager walking the floor. Remote employers screen heavily for proficiency in Python or R, SQL, and data visualization tools like Tableau or Power BI. A portfolio of end-to-end projects, even from personal or open-source work, shows you can move from raw data to insight without hand-holding.
Which companies hire remote data science analysts?
Remote data science analyst roles are posted by CVS Health, ZS Associates, and eBay and others right now, based on current remote listings on Migrate Mate as of September 2026. The hiring mix includes remote-first technology firms, distributed fintech and healthtech companies, and enterprise retailers with centralized analytics teams that operate across time zones.
Can you get a remote data science analyst job with no experience?
Yes, but remote entry-level roles are harder to land than in-office equivalents because you're expected to manage your own workflow from day one. Remote-first startups and smaller analytics consultancies are the most open to entry-level candidates. A portfolio of self-directed projects, a Kaggle competition record, or a published notebook demonstrating real analysis gives hiring managers something concrete to evaluate in the absence of prior job titles.
Do you need a degree for remote data science analyst jobs?
Not always. Many remote employers care more about what you can do with data than where you studied. A degree in statistics, computer science, or a quantitative field is common among hired candidates, but remote hiring managers frequently weigh demonstrated skills, a strong project portfolio, and fluency with tools like Python, SQL, and cloud data platforms as heavily as formal credentials.
Which industries hire the most remote data science analysts?
Remote data science analyst roles concentrate in Technology & Software, Insurance, and Science & Research, based on current remote listings on Migrate Mate as of September 2026. Those sectors favor remote data science analysts because their data infrastructure is cloud-hosted and their analytics teams are built to collaborate asynchronously across distributed locations.
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