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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Showing 5 of 585+ Data Science Engineer jobs
MINIMUM REQUIREMENTS: Bachelor’s degree or its U.S. equivalent in Computer Science, Information Systems, Computer Engineering, or a related field, plus 5 years of professional experience as a Sr. Data Analyst, Data Modeler, or any occupation, job title, position involving data analysis, data management, and database design.
Must also have experience in the following: 5 years of professional experience writing, optimizing, and troubleshooting advanced SQL queries; including complex joins, aggregations, subqueries, stored procedures, functions, and performance tuning in relational databases; 5 years of professional experience performing data manipulation, automation, and statistical analysis, including building robust data pipelines and ETL processes to integrate disparate railroad data sources; 5 years of professional experience documenting SQL queries, logic, data workflows, and analytics results for technical and non-technical stakeholders; 5 years of professional experience working with cross-functional teams (including business, IT, and finance) to gather requirements, clarify business needs, and deliver solutions strictly via SQL; 4 years of professional experience utilizing statistical analysis, including regression analysis, time-series forecasting, applied to rail operations data to generate insights; 3 years of professional experience developing executive dashboards and interactive reports using tools including Power BI or Qlik, to communicate operational insights and performance metrics effectively; 3 years of professional experience in rail transportation operations, including knowledge of industry terminology and processes, and applying this knowledge to support analysis and decision-making in a railroad environment.
CONTACT: Apply online at cpkcr.com/en/careers or submit resume via email to prabh.sandhu@cpkcr.com. Must specify Ad Code PSPT.
MINIMUM REQUIREMENTS: Bachelor’s degree or its U.S. equivalent in Computer Science, Information Systems, Computer Engineering, or a related field, plus 5 years of professional experience as a Sr. Data Analyst, Data Modeler, or any occupation, job title, position involving data analysis, data management, and database design.
Must also have experience in the following: 5 years of professional experience writing, optimizing, and troubleshooting advanced SQL queries; including complex joins, aggregations, subqueries, stored procedures, functions, and performance tuning in relational databases; 5 years of professional experience performing data manipulation, automation, and statistical analysis, including building robust data pipelines and ETL processes to integrate disparate railroad data sources; 5 years of professional experience documenting SQL queries, logic, data workflows, and analytics results for technical and non-technical stakeholders; 5 years of professional experience working with cross-functional teams (including business, IT, and finance) to gather requirements, clarify business needs, and deliver solutions strictly via SQL; 4 years of professional experience utilizing statistical analysis, including regression analysis, time-series forecasting, applied to rail operations data to generate insights; 3 years of professional experience developing executive dashboards and interactive reports using tools including Power BI or Qlik, to communicate operational insights and performance metrics effectively; 3 years of professional experience in rail transportation operations, including knowledge of industry terminology and processes, and applying this knowledge to support analysis and decision-making in a railroad environment.
CONTACT: Apply online at cpkcr.com/en/careers or submit resume via email to prabh.sandhu@cpkcr.com. Must specify Ad Code PSPT.
Data Science Engineer Jobs by Experience Level
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Find Data Science Engineer JobsData Science Engineer Job Market
Who's Hiring
- Amazon30
- Capital One24
- Meta15
- Deloitte12
- Information Technology Senior Management Forum12
Top Industries Hiring
- Technology & Software69
- Retail27
- Education24
- Banking & Financial Services23
- Consulting & Professional Services19
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 Meta, with the largest share of openings in California, New York, and Virginia, based on current listings on Migrate Mate as of August 2026. Demand is especially concentrated in technology, financial services, and healthcare data platforms.
How many data science engineer jobs are remote?
About 64% of data science engineer openings are fully remote or hybrid as of August 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 585+ Data Science Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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