Data Science Engineer Jobs
Data Science Engineer jobs are open across technology, healthcare, finance, and e-commerce, from entry-level to principal and staff levels, with specializations in machine learning infrastructure, MLOps, and real-time data pipelines. Find a role that fits from the openings below and apply directly.
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Join our dynamic team at an innovative insurance software company! We are seeking a Professional Services Consultant with a strong background in analytics within the insurance industry, ideally holding ACAS or near-ACAS designation. In this role, you will be instrumental in supporting our clients’ usage of our software for data extraction and analysis, developing and reviewing statistical models, and implementing effective pricing strategies.
What you’ll do:
Develop and maintain predictive models to assess risk and customer demand, guiding pricing strategies across various lines of business.
Utilize machine learning techniques and programming languages such as Python and R to enhance analytics capabilities.
Communicate complex technical results clearly and effectively, tailoring presentations for diverse audiences.
Demonstrate software functionality and provide training to new users, ensuring a smooth onboarding process.
Requirements:
You’ll do it using:
Extensive experience in the insurance industry, specifically in analytics and predictive modeling.
Proficiency in machine learning methods and programming languages (Python and R).
Exceptional communication skills, with a knack for presenting technical information in an accessible manner.
Strong ability to engage clients and provide valuable insights through data analysis.
You’ll excel by:
Focusing on the art of the possible
Communicating with honesty, candour, and respect
Collaborating seamlessly with colleagues across the company
Being resourceful and creative
Acting with empathy
You’ll love it because:
This is an incredibly impactful role in the organization.
You will have the opportunity to work in a successful, fast-moving, mission-driven company.
You will have growth opportunities.
You will collaborate with other professional and enthusiastic team members.
Earnix has competitive benefits with an all-for-one mentality.
Salary Range:
$78,000-142,000 per year
Position Intro:
Earnix is the premier provider of mission-critical, cloud-based intelligent decisioning across pricing, rating, underwriting, and product personalization. These fully-integrated solutions provide ultra-fast ROI and are designed to transform how global insurers and banks are run by unlocking value across all facets of the business. Earnix has been innovating for insurers and banks since 2001 with customers in over 35 countries across six continents and offices in the Americas, Europe, Asia Pacific, and Israel.
Join our dynamic team at an innovative insurance software company! We are seeking a Professional Services Consultant with a strong background in analytics within the insurance industry, ideally holding ACAS or near-ACAS designation. In this role, you will be instrumental in supporting our clients’ usage of our software for data extraction and analysis, developing and reviewing statistical models, and implementing effective pricing strategies.
What you’ll do:
Develop and maintain predictive models to assess risk and customer demand, guiding pricing strategies across various lines of business.
Utilize machine learning techniques and programming languages such as Python and R to enhance analytics capabilities.
Communicate complex technical results clearly and effectively, tailoring presentations for diverse audiences.
Demonstrate software functionality and provide training to new users, ensuring a smooth onboarding process.
Requirements:
You’ll do it using:
Extensive experience in the insurance industry, specifically in analytics and predictive modeling.
Proficiency in machine learning methods and programming languages (Python and R).
Exceptional communication skills, with a knack for presenting technical information in an accessible manner.
Strong ability to engage clients and provide valuable insights through data analysis.
You’ll excel by:
Focusing on the art of the possible
Communicating with honesty, candour, and respect
Collaborating seamlessly with colleagues across the company
Being resourceful and creative
Acting with empathy
You’ll love it because:
This is an incredibly impactful role in the organization.
You will have the opportunity to work in a successful, fast-moving, mission-driven company.
You will have growth opportunities.
You will collaborate with other professional and enthusiastic team members.
Earnix has competitive benefits with an all-for-one mentality.
Salary Range:
$78,000-142,000 per year
Position Intro:
Earnix is the premier provider of mission-critical, cloud-based intelligent decisioning across pricing, rating, underwriting, and product personalization. These fully-integrated solutions provide ultra-fast ROI and are designed to transform how global insurers and banks are run by unlocking value across all facets of the business. Earnix has been innovating for insurers and banks since 2001 with customers in over 35 countries across six continents and offices in the Americas, Europe, Asia Pacific, and Israel.
Data Science Engineer Jobs by Experience Level
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Find Data Science Engineer JobsData Science Engineer Job Market
Who's Hiring
- Amazon27
- Capital One27
- Information Technology Senior Management Forum18
- Deloitte15
- Meta14
Top Industries Hiring
- Technology & Software29
- Education21
- Retail8
- Insurance7
- Banking & Financial Services7
What Employers Look For
The qualifications that appear most often in 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 Data Science Engineer Job Search
Separate your ML and engineering work
Hiring managers want to see both sides of your work. Structure your resume into distinct sections for model development and for infrastructure, pipelines, or deployment engineering. Conflating the two makes it harder for reviewers to assess either.
Target openings that match your stack
Data science engineer job descriptions vary widely in tooling. Filter for roles that name the exact frameworks you know well, whether that is PyTorch, Spark, or Kubeflow. Applying to a strong stack match beats applying broadly to a dozen misaligned roles.
Apply early to roles that fit
Migrate Mate lists data science engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Showcase end-to-end project ownership
Interviewers routinely ask how you took a model from prototype to production. Prepare a clear narrative for at least one project covering data ingestion, model training, deployment, and monitoring. Vague answers about 'building models' without the deployment story consistently hurt candidates at this level.
Quantify pipeline performance, not just accuracy
Accuracy metrics impress data scientists, but data science engineers are expected to own system performance too. Lead with latency improvements, throughput gains, or cost reductions your pipelines achieved. Those numbers stand out to engineering managers who are evaluating your systems thinking.
Negotiate on scope, not just compensation
After an offer, ask specifically which part of the ML lifecycle you will own at hire versus six months in. Scope ambiguity is the top source of dissatisfaction in this role. Clarifying it upfront also signals the kind of systems-level thinking that makes hiring managers more confident in their offer.
Data Science Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most data science engineers?
The companies hiring the most data science engineers right now include Amazon, Capital One, and Information Technology Senior Management Forum, with the largest share of openings in California, New York, and Virginia, based on current listings on Migrate Mate as of November 2026. Demand is especially concentrated in technology, financial services, and healthcare data platforms.
How many data science engineer jobs are remote?
About 74% of data science engineer openings are fully remote or hybrid as of November 2026, making it one of the more flexible engineering roles to search. Sub-areas like MLOps, feature engineering, and model serving infrastructure tend to have the highest share of remote-eligible positions, since those workflows are well-suited to asynchronous, distributed team structures.
How do you become a data science engineer?
Start by building strong fundamentals in Python and SQL, then layer in distributed data processing frameworks like Spark. Work on end-to-end projects that take a model from raw data through training, deployment, and monitoring. Develop familiarity with cloud platforms and containerization. Contributing to open-source data infrastructure projects or publishing documented pipelines on a public portfolio accelerates your path into the role.
Can you break into data science engineering without much experience?
Yes, but you need to substitute depth for breadth early on. Build one well-documented end-to-end ML pipeline project that demonstrates data ingestion, training, deployment, and observability. Roles titled junior data engineer or ML engineer are common entry points where companies expect less production experience. Certifications in a major cloud platform can strengthen applications when your professional history is thin.
What does the data science engineer interview process look like?
Most processes run three to five rounds. An initial recruiter screen is followed by a technical phone interview covering Python, SQL, or system design basics. The core rounds typically include a take-home or live coding assessment on pipeline design, a machine learning systems design interview, and a final round with engineering leadership. Some employers add a presentation of a past project as a fifth stage.
Where can I find and apply to data science engineer jobs?
You can find and apply to data science engineer jobs on Migrate Mate, which lists current openings from employers across the United States. Find the roles that fit your experience and stack, then apply directly to each listing from the results on this page.
See All 743+ Data Science Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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