Data Scientist Jobs
Data Scientist jobs are open from entry-level to staff and principal across technology, healthcare, finance, and retail, with specializations in machine learning engineering, natural language processing, and applied analytics. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 2,294+ Data Scientist jobs
Work You'll Do
As an Analytics and Insights Engineer III on the Cyber and Strategic Risk team, you will be responsible for:
You will support operational technology (OT) cybersecurity missions by developing and sustaining data products that improve cyber threat detection and response. This role focuses on OT cyber analytics, machine learning model development, and data engineering across on-premises and cloud-connected environments. You will serve as the project's primary data science resource and collaborate with delivery, security operations, cloud, and program stakeholders to advance analytic capabilities. The role is differentiated by its ownership of machine learning development and its contribution to OT cyber analytics innovation.
- Develop and sustain OT cyber data ontologies to support analytics, detection, and reporting use cases
- Build, train, test, and maintain machine learning models for OT cyber threat and attack detection
- Design and maintain data pipelines that move sensor and telemetry data from on-premises environments to cloud platforms
- Analyze cyber data to identify patterns, anomalies, and indicators that support security operations and mission objectives
- Collaborate with technical leads, delivery teams, security operations center stakeholders, cloud/data teams, and program management counterparts to implement scalable analytics solutions
A successful candidate would possess these skills:
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to provide clear guidance to others
The Team
Deloitte's Government & Public Services (GPS) practice - our people, ideas, technology and outcomes - is designed for impact. Serving federal, state, & local government clients as well as public higher education institutions, our team of professionals brings fresh perspective to help clients anticipate disruption, reimagine the possible, and fulfill their mission promise.
Our Enterprise Security offering embeds security in all aspects of digital transformation by securing a client's technical backbone while enabling secure digital transformation. Includes security architecture, secure development and deployment, end-to-end cyber cloud capabilities, application security, and security for emerging technologies and connected products.
Qualifications
Required:
- Bachelor's degree
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
- Active Secret security clearance required
- Ability to travel 25%, on average, based on the work you do and the clients and industries/sectors you serve
- 5+ years of experience with:
- Data Modeling
- JIRA
- Machine Learning
- Python
- SQL
Work You'll Do
As an Analytics and Insights Engineer III on the Cyber and Strategic Risk team, you will be responsible for:
You will support operational technology (OT) cybersecurity missions by developing and sustaining data products that improve cyber threat detection and response. This role focuses on OT cyber analytics, machine learning model development, and data engineering across on-premises and cloud-connected environments. You will serve as the project's primary data science resource and collaborate with delivery, security operations, cloud, and program stakeholders to advance analytic capabilities. The role is differentiated by its ownership of machine learning development and its contribution to OT cyber analytics innovation.
- Develop and sustain OT cyber data ontologies to support analytics, detection, and reporting use cases
- Build, train, test, and maintain machine learning models for OT cyber threat and attack detection
- Design and maintain data pipelines that move sensor and telemetry data from on-premises environments to cloud platforms
- Analyze cyber data to identify patterns, anomalies, and indicators that support security operations and mission objectives
- Collaborate with technical leads, delivery teams, security operations center stakeholders, cloud/data teams, and program management counterparts to implement scalable analytics solutions
A successful candidate would possess these skills:
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to provide clear guidance to others
The Team
Deloitte's Government & Public Services (GPS) practice - our people, ideas, technology and outcomes - is designed for impact. Serving federal, state, & local government clients as well as public higher education institutions, our team of professionals brings fresh perspective to help clients anticipate disruption, reimagine the possible, and fulfill their mission promise.
Our Enterprise Security offering embeds security in all aspects of digital transformation by securing a client's technical backbone while enabling secure digital transformation. Includes security architecture, secure development and deployment, end-to-end cyber cloud capabilities, application security, and security for emerging technologies and connected products.
Qualifications
Required:
- Bachelor's degree
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
- Active Secret security clearance required
- Ability to travel 25%, on average, based on the work you do and the clients and industries/sectors you serve
- 5+ years of experience with:
- Data Modeling
- JIRA
- Machine Learning
- Python
- SQL
Data Scientist Jobs by Experience Level
Top Cities Hiring Data Scientists
Explore data scientist openings in the cities hiring most right now.
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Find Data Scientist JobsData Scientist Job Market
Who's Hiring
- Booz Allen Hamilton83
- Google55
- CVS Health55
- Amazon48
- JPMorganChase46
Top Industries Hiring
- Technology & Software133
- Science & Research23
- Banking & Financial Services21
- Consulting & Professional Services18
- Insurance16
What Employers Look For
The qualifications that appear most often in data scientist jobs.
- Proficiency in Python and SQL for data manipulation and analysis
- Experience building and deploying machine learning models in production
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud
- Bachelor's or master's degree in statistics, computer science, or a related quantitative field
- Ability to communicate findings and model results clearly to non-technical stakeholders
- Experience with data visualization tools such as Tableau, Power BI, or Looker
Tips for Your Data Scientist Job Search
Tailor your resume to each posting
Copy the exact model names and tools listed in the job description, such as XGBoost, PyTorch, or dbt, into your resume where you've used them. Applicant tracking systems score resumes on keyword matches before a human ever reads them.
Show model impact, not just methods
Hiring managers want to know what your model did in production, not just that you built one. Replace phrases like 'developed a classification model' with outcomes: how it changed a decision, reduced error, or moved a business metric.
Build a portfolio that reflects the industry
A healthcare data scientist role and a fintech one call for different demos. Pick one or two portfolio projects that use data problems from your target industry, even if the work was done independently, so reviewers immediately see relevant context.
Apply early to roles that fit
Migrate Mate lists data scientist openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare for a take-home case study
Most data scientist interviews include a take-home assignment involving messy real-world data. Practice writing clean, commented code and a short executive summary alongside your analysis, since both are evaluated separately in most scoring rubrics.
Negotiate by anchoring to scope, not title
When discussing offers, frame your ask around the complexity of the modeling work, the expected data volume, and cross-functional scope rather than just job title. That framing shifts the conversation toward business value and tends to support a stronger outcome.
Data Scientist Jobs: Frequently Asked Questions
Which companies are hiring the most data scientists?
Booz Allen Hamilton, Google, and CVS Health are hiring the most data scientists right now, with openings concentrated in California, Virginia, and New York, based on current listings on Migrate Mate as of August 2026. Volume and location shift month to month, so checking current listings gives you the most accurate picture.
How many data scientist jobs are remote?
About 57% of data scientist openings are fully remote or hybrid as of August 2026, making it one of the more remote-accessible roles in the technology sector. Machine learning research, analytics engineering, and NLP roles tend to have the highest share of fully remote postings compared to embedded or product-facing data science positions.
How do you become a data scientist?
Most data scientists start by building fluency in Python and SQL, then move into statistics, machine learning fundamentals, and a tool like scikit-learn or PyTorch. A degree in a quantitative field helps, but employers weigh portfolio projects and demonstrated modeling skills heavily. Working through real datasets, publishing notebooks, and completing a capstone project in your target industry will move your application forward faster than coursework alone.
How do you get hired as a data scientist with little experience?
Focus on one specialized area, such as natural language processing, computer vision, or time-series forecasting, rather than trying to show breadth. Build two or three end-to-end projects that go from raw data to a deployed or shareable model output. Entry-level and associate data scientist roles, as well as data analyst positions with modeling components, are realistic starting points and often lead to a full data scientist role within a year or two.
What does the data scientist interview process look like?
Most data scientist interview processes include a recruiter screen, a technical phone interview covering statistics and Python, a take-home assignment or case study, and a final loop with the team that often includes a presentation of your case study findings. Some companies add a system design or data modeling round. The full process typically runs across several weeks from first contact to offer.
Where can I find and apply to data scientist jobs?
You can find and apply to data scientist jobs on Migrate Mate, which lists current openings from employers across the United States. Search the listings to find roles that match your background and apply directly to each one that fits. New postings are added regularly, so checking back often gives you access to the most current opportunities.
See All 2,294+ Data Scientist Jobs
Find roles that match your experience and apply in just a few clicks.
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