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 ZS Associates, New York City Department of Health and Mental Hygiene, and Honor. Find a role that fits below and apply directly.
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Rock Solid. Rock Confident.
RockWallet is a financial technology company made up of people who think differently about how digital assets can be managed, accessed, and used.
At RockWallet, our vision is for anyone to be able to access and thrive in the digital economy. It’s our mission to help our customers make the most of these opportunities by building products that empower people to navigate digital asset usage easily, securely, and with confidence. Our self-custodial, multicurrency wallet puts you in charge of your digital assets. RockWallet’s app makes it quick and easy to buy, use, store, and swap top cryptocurrencies, all in one place, on your mobile phone. We are a customer-focused company obsessed with providing the best customer experience and customer support. RockWallet is registered with FinCEN as a Money Service Business. Find out more here at www.rockwallet.com.
Want to join us? We’re expanding our team globally, looking for the right people to help us grow.
Role Overview
We’re looking for a Data Science & Machine Learning Intern to join the RW Data Team and support the development of analytics and predictive models that drive real business decisions. This role offers hands-on exposure to applied data science in a production environment, working with real customer, product, and operational data.
You will contribute to projects such as customer behavior modeling, anomaly detection, and forecasting key metrics, while learning how data science solutions are built, validated, and deployed in practice.
This internship bridges theory and application, combining statistical thinking, experimentation, and practical machine learning to generate actionable insights.
Key Responsibilities
- Assist in developing and evaluating predictive models to understand customer behavior (e.g., engagement, churn, conversion).
- Support anomaly detection analyses to identify unusual patterns in product, marketing, or financial data.
- Help build forecasting models for key metrics such as transaction volume, revenue, and customer activity.
- Work with senior data scientists and data engineers to prepare data, engineer features, and test models.
- Analyze large datasets from multiple sources to identify trends and opportunities for optimization.
- Contribute to dashboards, reports, and internal tools that surface insights to stakeholders.
- Collaborate with product, marketing, and operations teams to understand business questions and define success metrics.
- Document analyses, assumptions, and results clearly for both technical and non-technical audiences.
- Explore and experiment with new data science techniques, tools, and models under guidance.
- Currently pursuing or recently completed a degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- Strong foundation in Python (pandas, numpy, scikit-learn; PySpark is a plus).
- Working knowledge of SQL for querying and aggregating data.
- Understanding of basic statistical concepts, regression, classification, and model evaluation.
- Familiarity with time-series data, forecasting, or anomaly detection concepts (academic or project-based).
- Comfortable working with messy, real-world datasets and learning data cleaning techniques.
- Strong analytical thinking and curiosity about how data translates into business impact.
- Good communication skills and willingness to ask questions and learn.
- Coursework or projects involving machine learning, forecasting, or anomaly detection.
- Exposure to AWS or cloud data tools (S3, Redshift, Glue, SageMaker) through school or projects.
- Experience with data visualization tools (QuickSight, Power BI, Tableau, or similar).
- Interest in fintech, transactional data, fraud analytics, or customer segmentation.
- Familiarity with notebooks, Git, or basic ML pipelines.
- Exposure to NLP or LLM-based analytics (coursework or side projects).
- Hands-on experience working with real production-scale data.
- Exposure to end-to-end data science workflows — from raw data to insights and models.
- Opportunity to contribute to projects that directly impact product and business decisions.
- A strong foundation for future roles in Data Science, Machine Learning, or Analytics.
We thank all interested applicants; however, only those under consideration will be contacted.
RockWallet, LLC is an Equal Employment Opportunity/ Veterans/Disabled/LGBT and Affirmative Action employer. We are committed to diversity and building a team that represents a variety of backgrounds, perspectives, and skills. We do not discriminate and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global diverse team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together.
This job posting may involve the use of artificial intelligence (AI) — such as automated resume screening or candidate assessment — at one or more stages of the recruitment process. If AI tools are used, all decisions are overseen by a human reviewer.
Rock Solid. Rock Confident.
RockWallet is a financial technology company made up of people who think differently about how digital assets can be managed, accessed, and used.
At RockWallet, our vision is for anyone to be able to access and thrive in the digital economy. It’s our mission to help our customers make the most of these opportunities by building products that empower people to navigate digital asset usage easily, securely, and with confidence. Our self-custodial, multicurrency wallet puts you in charge of your digital assets. RockWallet’s app makes it quick and easy to buy, use, store, and swap top cryptocurrencies, all in one place, on your mobile phone. We are a customer-focused company obsessed with providing the best customer experience and customer support. RockWallet is registered with FinCEN as a Money Service Business. Find out more here at www.rockwallet.com.
Want to join us? We’re expanding our team globally, looking for the right people to help us grow.
Role Overview
We’re looking for a Data Science & Machine Learning Intern to join the RW Data Team and support the development of analytics and predictive models that drive real business decisions. This role offers hands-on exposure to applied data science in a production environment, working with real customer, product, and operational data.
You will contribute to projects such as customer behavior modeling, anomaly detection, and forecasting key metrics, while learning how data science solutions are built, validated, and deployed in practice.
This internship bridges theory and application, combining statistical thinking, experimentation, and practical machine learning to generate actionable insights.
Key Responsibilities
- Assist in developing and evaluating predictive models to understand customer behavior (e.g., engagement, churn, conversion).
- Support anomaly detection analyses to identify unusual patterns in product, marketing, or financial data.
- Help build forecasting models for key metrics such as transaction volume, revenue, and customer activity.
- Work with senior data scientists and data engineers to prepare data, engineer features, and test models.
- Analyze large datasets from multiple sources to identify trends and opportunities for optimization.
- Contribute to dashboards, reports, and internal tools that surface insights to stakeholders.
- Collaborate with product, marketing, and operations teams to understand business questions and define success metrics.
- Document analyses, assumptions, and results clearly for both technical and non-technical audiences.
- Explore and experiment with new data science techniques, tools, and models under guidance.
- Currently pursuing or recently completed a degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- Strong foundation in Python (pandas, numpy, scikit-learn; PySpark is a plus).
- Working knowledge of SQL for querying and aggregating data.
- Understanding of basic statistical concepts, regression, classification, and model evaluation.
- Familiarity with time-series data, forecasting, or anomaly detection concepts (academic or project-based).
- Comfortable working with messy, real-world datasets and learning data cleaning techniques.
- Strong analytical thinking and curiosity about how data translates into business impact.
- Good communication skills and willingness to ask questions and learn.
- Coursework or projects involving machine learning, forecasting, or anomaly detection.
- Exposure to AWS or cloud data tools (S3, Redshift, Glue, SageMaker) through school or projects.
- Experience with data visualization tools (QuickSight, Power BI, Tableau, or similar).
- Interest in fintech, transactional data, fraud analytics, or customer segmentation.
- Familiarity with notebooks, Git, or basic ML pipelines.
- Exposure to NLP or LLM-based analytics (coursework or side projects).
- Hands-on experience working with real production-scale data.
- Exposure to end-to-end data science workflows — from raw data to insights and models.
- Opportunity to contribute to projects that directly impact product and business decisions.
- A strong foundation for future roles in Data Science, Machine Learning, or Analytics.
We thank all interested applicants; however, only those under consideration will be contacted.
RockWallet, LLC is an Equal Employment Opportunity/ Veterans/Disabled/LGBT and Affirmative Action employer. We are committed to diversity and building a team that represents a variety of backgrounds, perspectives, and skills. We do not discriminate and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global diverse team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together.
This job posting may involve the use of artificial intelligence (AI) — such as automated resume screening or candidate assessment — at one or more stages of the recruitment process. If AI tools are used, all decisions are overseen by a human reviewer.
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Find JobsRemote Senior Data Science Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software
- Consulting & Professional Services
- Retail
- Insurance
- Science & Research
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 ZS Associates, New York City Department of Health and Mental Hygiene, and Honor, based on current remote listings on Migrate Mate as of August 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, Consulting & Professional Services, and Retail, per current remote listings on Migrate Mate as of August 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.
See All 72 Remote Senior Data Science Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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