United States

Data Jobs in United States

3,284 jobs found

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1mo ago

Machine Learning Engineer

Glint Tech Solutions LLC Sunnyvale, CA

Sunnyvale, CA

1mo ago Closed

Full time Ai Ml Automation Azure Ml

This position is no longer accepting applications (closed Aug 26, 2026).

Job Description

Glint Tech Solutions LLC is seeking an experienced Machine Learning Engineer to design, deploy, and scale ML models and pipelines, and to build ML infrastructure on cloud platforms. This onsite role in Sunnyvale, CA collaborates with data science and engineering teams to deliver reliable, scalable AI solutions.

Responsibilities

Design and deploy scalable machine learning models and pipelines using Python and Java
Build and manage ML infrastructure on GCP and Azure cloud platforms
Implement and manage containerized workloads using Kubernetes
Ensure scalability and reliability of ML systems in production environments
Collaborate cross-functionally with data science and engineering teams

Requirements

Hands-on experience with Java and Python
Experience with Google Cloud Platform (GCP) and Microsoft Azure
Hands-on experience with Kubernetes and container orchestration
Strong understanding of scalability and distributed systems

Technologies

Python
Java
GCP
Azure
Kubernetes

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1mo ago

Senior Data Engineer, Underwriting Technical Lead

Travelers Hartford, CT

Hartford, CT

1mo ago Closed

$139k - $230k/yr Full time Senior Amazon Web Services AWS

This position is no longer accepting applications (closed Aug 26, 2026).

Job Description

Travelers offers an onsite opportunity in Hartford, CT to lead the acceleration of analytics growth and transformation. In this senior data engineering leadership role, you will mentor engineers, shape data solutions, and translate complex data into actionable terms to support AI, ML, and business intelligence. This position carries a salary range of USD 139,400 - 230,000 per year.

Responsibilities

Design, build, and operate sophisticated data pipelines and solutions; diagnose issues, apply transformations, and propose data cleansing and quality improvements.
Architect complex data solutions that integrate new data sources while ensuring consistency across projects and alignment with data strategies.
Analyze diverse data sources to assess value and applicability, then recommend data for use in analytics workflows.
Incorporate core data management practices including governance, security, and quality controls.
Serve as a data and technology subject matter expert within lines of business; support delivery and educate end users on data products and the analytic environment.
Conduct data and system analysis to identify defects or incidents, assess impact, and implement appropriate resolutions for high complexity scenarios.
Collaborate across teams to support delivery and educate stakeholders on complex data products and analytics environments.
Perform other duties as assigned.

Requirements

Bachelor’s Degree in a STEM field or equivalent.
Ten years of related experience, plus six additional years of data engineering experience.
Proven experience designing, building, and maintaining scalable data pipelines on AWS, with hands-on work across S3, Glue, Redshift, Lambda, and EMR.
Proficiency with Databricks, including development and optimization of data workflows and leveraging Lakehouse architecture for large-scale processing.
Solid understanding of AI and ML concepts; ability to collaborate with data science teams and support deployment or operationalization of models within the data ecosystem.
Hands-on experience with dbt for data modeling, transformation, and documentation, applying software engineering best practices to analytics workflows.
Advanced PySpark skills for distributed data processing and production-grade code for large-scale batch and streaming workloads.
Candidates from an Azure environment (Azure Data Factory, Synapse Analytics, Azure Databricks) will be considered if they demonstrate the ability to operate effectively within an AWS-based stack.
Willingness to take a technical leadership role, including mentoring engineers, shaping architectural decisions, and partnering with engineering and business stakeholders.
Bachelor’s degree in computer science or related STEM field, or equivalent education/experience.

Benefits

Health Insurance
Retirement
Paid Time Off
Wellness Program
Volunteer Encouragement

Employment Practices

Travelers is an equal opportunity employer. We value the unique abilities and talents each individual brings to our organization and recognize that we benefit in numerous ways from our differences.

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1mo ago

Data Engineer

DCI Solutions Colorado Springs, CO

Colorado Springs, CO

1mo ago Closed

$175k - $260k/yr Full time Big Data Data Engineer Data Integration

This position is no longer accepting applications (closed Aug 26, 2026).

Job Description

DCI Solutions is looking for a Data Engineer to join a cross-functional team in Colorado Springs. The role focuses on integrating data, turning insights into actionable applications, and supporting operational decisions across the organization. Candidates should bring hands-on Spark, PySpark, and Pandas experience, along with an Active Secret Security Clearance.

Location

Colorado Springs, CO

Job Type

Full-time

Salary

USD 175,000 - 260,000 per year

Work Location

In person

Responsibilities

Serve as a Data Engineer on a cross-functional team to help solve pressing problems
Integrate data, transform insights, and build applications for operational decisions
Setup transfers of data feeds from source systems into location
Write transformations and derive new datasets using Spark for distributed computation
Monitor build progress and debug build problems
Design applications that address operational questions
Execute requests for information (RFI’s) surrounding the platform’s data footprint
Work with product teams while debugging/understanding the code backing our platform

Requirements

BS in Computer Science / Engineering (or related career field) preferred
Strong Data Engineering background
Experience with Spark, Pyspark, and/or Pandas
Extensive experience coding/programming in: Python, SQL, R or Similar
Strong understanding of algorithms and data structures as they pertain to development
Experience with Foundry is preferred
Active Secret Security Clearance required

Technologies

Spark
Pyspark
Pandas
Python
SQL
R
Foundry

Benefits

401(k)
401(k) matching
Dental insurance
Flexible schedule
Flexible spending account
Health insurance
Health savings account
Life insurance
Paid time off
Parental leave
Professional development assistance
Relocation assistance
Retirement plan
Tuition reimbursement
Vision insurance

Experience (preferred)

data engineering: 5 years (Preferred)
Python coding/programming: 5 years (Preferred)
building and managing data pipelines: 5 years (Preferred)
data transformation: 5 years (Preferred)
Foundry : 1 year (Preferred)
Spark and/or Pyspark: 3 years (Preferred)
Pandas: 3 years (Preferred)

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1mo ago

Staff Data Engineer

Okta New York, NY

New York, NY

1mo ago Closed

$174k - $239k/yr Full time Data Data Architecture Data Engineer

This position is no longer accepting applications (closed Aug 26, 2026).

Job Description

Okta is seeking a Staff Data Engineer to join the Auth0 Data Engineering Team in New York City. This onsite role centers on steering the architectural evolution of the data platform, beginning with FEDRamp compliance, and collaborating across data, platform, and security teams to deliver scalable and secure data solutions. The position offers a yearly salary range of USD 174,000 to 238,700.

Responsibilities

Spearhead the architectural design and ongoing evolution of the data platform, establishing the roadmap toward its future state.
Shape the technical strategy for large-scale platform enhancements, balancing long-term objectives with incremental delivery.
Collaborate with engineers and leaders across data, platform, and security teams to craft a cohesive and secure architecture.
Create architectural artifacts such as design documents, threat models, and technical specifications to clarify and align on complex initiatives.
Serve as a technical mentor and multiplier for the Data Engineering team, raising performance through coaching, code reviews, and sharing best practices.
Implement core components of the new architecture, validate designs through hands-on coding, and model how others can follow.

Requirements

A strong passion for building secure, reliable, and scalable data systems.
8+ years of software development experience, with at least 5+ years in a data engineering or data platform role.
Proven experience designing and delivering large-scale, cross-functional technical projects.
Extensive experience architecting and deploying data infrastructure in a cloud environment, with AWS strongly preferred.
Deep architectural knowledge of the modern data stack, including Snowflake, dbt, and Airflow.
Strong proficiency in Python and SQL, with a high bar for code quality and system design.
Excellent communication skills, with the ability to create clarity and drive decisions on complex technical topics.
The ability to navigate ambiguity, define a path forward, and lead others to successful outcomes.

Technologies

Snowflake
dbt
Airflow
Python
SQL
Terraform
Kubernetes
AWS
AWS GovCloud

The Auth0 Data Engineering Team

Pipeline team, situated near the Platform group and data producers, responsible for efficient data ingestion and rapid access to unmodeled data
Warehouse team, closer to business users and data consumers, modeling data in the warehouse to abstract complexity and support analysis, reporting, and decision-making
Interface team, focused on creating and managing connections between the data platform and external systems to ensure consistent and reliable data access

The Okta Experience

Supporting Your Well-Being
Driving Social Impact
Developing Talent and Fostering Connection + Community

Extra Credit If You Have Experience In Any Of The Following

Direct experience with security compliance frameworks like FEDRamp, SOC2, or HIPAA.
Hands-on experience with Infrastructure as Code (Terraform) and container orchestration (Kubernetes).
Experience working in government cloud environments, such as AWS GovCloud.
Familiarity with the Identity Access & Management (IAM) domain.

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1mo ago

Data Scientist

Leidos Bethesda, MD

Bethesda, MD

1mo ago Closed

$70k - $126k/yr Full time Analytics Artificial Intelligence Big Data

This position is no longer accepting applications (closed Aug 26, 2026).

Job Description

The Data Scientist role at Leidos focuses on research‑level data analytics, machine learning, natural language processing, and data fusion for national security. This position operates on a research‑oriented team at a customer site in Bethesda, MD, delivering results, publications, and actionable intelligence insights on site.

Job Details

Location: Bethesda, MD (onsite)
Salary: USD 69,550 – 125,725 per year
Minimum Experience: 2+ years
Education: Bachelor's degree or Master's degree (or equivalent graduate degree)

Responsibilities

Collaborate within a compact data science team and with a broader group of software developers, network engineers, senior investigators, program managers, researchers, and data analysts to design, build, and optimize a Data Science platform that produces results, disseminates findings, and supports publications and presentations.
Advance small projects that analyze diverse big data across national security, cyber security, business intelligence, online social media, human behavior, and related domains.
Support multiple concurrent projects and operate under open‑ended or high‑level guidance.
Independently and collaboratively identify discoveries with mission relevance.
Package and convey findings to non‑technical audiences in a clear, actionable manner.
Apply a blend of curiosity, quantitative acumen, and customer focus to identify novel data sources, improve predictive algorithms, and promote user adoption of advanced analytics platforms in partnership with a motivated team.
Leverage expertise in research design, exploratory analysis, quantitative methods, user interface design for applications, and customer outreach to drive impact.

Requirements

Bachelor’s degree in a quantitative or analytical field (including Computer Science, Mathematics, Economics, Statistics, Engineering, Physics, or Computational Social Science) with 2+ years of experience; or a Master’s degree or equivalent graduate degree with less than 2 years of experience.
Active TS/SCI clearance with the ability to obtain TS/SCI with polygraph.
Experience in data science, analytics, or quantitative intelligence analysis, showing progressive technical development and outcomes.
Proficiency in Python or other scripting languages.
Experience deploying data science applications (for example, using Streamlit) with Docker, Kubernetes, or OpenShift.
Familiarity with information retrieval and search technologies such as Elastic Stack (Elasticsearch and Kibana) and/or SQL.
Experience collaborating with a hybrid team of analysts, engineers, and developers to conduct research and develop deployable yet user‑friendly algorithms and analytical platforms.
Previous work in data science or research focusing on data analytics or big data that does not fit into memory.
Proven track record of active learning and creative problem solving.
Ability to analyze software development or data acquisition requirements and determine optimal, cost‑effective solutions.
Familiarity with git or other version control systems.
Demonstrated ability for independent ownership of work and timely delivery with minimal supervision.

Technologies

Python
Streamlit
Docker
Kubernetes
OpenShift
Elasticsearch
Kibana
SQL
git
neo4j
Graph ML
AWS

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1mo ago

Quantitative Data Scientist

Software Technology Inc Reston, VA

Reston, VA

1mo ago Closed

Full time Python Big Data Data Analysis

This position is no longer accepting applications (closed Aug 26, 2026).

Job Description

Quantitative Data Scientist role at Software Technology Inc in Reston, VA onsite, focusing on Python based modeling, risk analytics, and data engineering with AWS and big data tooling.

Responsibilities

Leverage advanced Python expertise across core libraries like NumPy, pandas, SciPy, statsmodels, scikit-learn, and QuantLib to design and maintain quantitative models.
Operate on large and complex mortgage and loan datasets with expert SQL skills to support analytics workflows.
Design, calibrate, and optimize Monte Carlo simulations and time series models used for risk assessment.
Apply counterparty credit risk concepts, including Potential Future Exposure methodologies, within modeling work.
Model interest rate dynamics, derivative pricing, and macro risk factor influences within analytical frameworks.
Develop and sustain data pipelines and analytics on AWS using S3, Lambda, Batch, Glue, EMR, CloudWatch, IAM, and EC2.
Adopt software engineering practices such as Git version control, unit testing, CI/CD pipelines, and shell scripting to ensure reliable deliverables.
Work with data lakes, NoSQL systems, and orchestration tools like Spark, Hive, and Airflow to enable scalable data processing.
Demonstrate strong analytical thinking and meticulous attention to detail across modeling tasks.
Communicate intricate technical concepts clearly to both technical and non-technical audiences.
Meet a minimum of five years of experience in quantitative modeling, data engineering, or related fields, with a Bachelor's degree as the educational baseline.

Requirements

Strong Python proficiency with libraries such as NumPy, pandas, SciPy, statsmodels, scikit-learn, and QuantLib.
Advanced SQL skills for handling large and complex mortgage or loan datasets.
Experience designing and optimizing Monte Carlo simulations and time-series models.
Solid understanding of counterparty credit risk, including Potential Future Exposure methodologies.
Familiarity with interest rate modeling, derivative pricing, and macro risk factor models.
Hands-on experience with AWS services including S3, Lambda, Batch, Glue, EMR, CloudWatch, IAM, and EC2.
Competence in software engineering practices such as Git, unit testing, CI/CD, and shell scripting.
Experience working with data lakes, NoSQL systems, and tools like Spark, Hive, and Airflow.
Strong analytical thinking and attention to detail.
Ability to communicate complex technical concepts clearly to both technical and non-technical audiences.
Minimum five years of experience in quantitative modeling, data engineering, or a related field, with a Bachelor's degree required.

Technologies

Python
NumPy
pandas
SciPy
statsmodels
scikit-learn
QuantLib
SQL
S3
Lambda
Batch
Glue
EMR
CloudWatch
IAM
EC2
Git
Spark
Airflow
NoSQL
Shell scripting
CI/CD

Skills

Business Analysis
Shell Script
SQL
NoSQL

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1mo ago

Senior Data Scientist

Brooksource Houston, TX

Houston, TX

1mo ago Closed

$70 - $80/hr Contract Senior Artificial Intelligence Azure

This position is no longer accepting applications (closed Aug 26, 2026).

Job Description

Brooksource is partnering with a rapidly growing Data and AI organization within a leading energy company to build enterprise scale AI, MLOps, and analytics capabilities. This onsite, contract-to-hire opportunity sits in Houston, TX, offering $70-$80 per hour and the chance to influence the organization’s AI strategy while working hands-on with modern cloud platforms. The centralized innovation team acts as the hub for Data Science, AI, and Data Engineering, delivering reusable solutions that accelerate value across multiple business units.

Overview

In this lead data science role, you will supervise the design and execution of AI and analytics initiatives at the enterprise level. The ideal candidate blends deep technical expertise with the ability to shape scalable, governed platforms that move AI from experimentation into production.

Team & Environment

You will report to a Data Science leadership group and serve as a technical leader on a growing enterprise AI team that supports several business units. This is a high-visibility position with substantial influence on the organization’s AI roadmap and digital transformation efforts. Preference is given to candidates located in or willing to relocate to the Houston area, with onsite work and a path toward long-term conversion to full-time.

Responsibilities

Steer end-to-end lifecycle of ML and AI solutions within Azure and Databricks environments, from design through deployment and production readiness.
Design scalable data science workflows that transition models from research to production.
Collaborate with Data Engineering to build robust ETL/ELT pipelines supporting enterprise AI programs.
Develop and optimize machine learning pipelines using Python, PySpark, Spark SQL, Databricks, and Azure services.
Lead MLOps and AgentOps practices, including model lifecycle management, monitoring, deployment automation, and governance.
Implement CI/CD pipelines with Git, GitHub, Azure DevOps, or equivalent tools to automate testing, deployment, and releases.
Partner with AI Engineers to develop and productionize Generative AI, LLM, and intelligent agent solutions when applicable.
Champion AI governance, model management, reproducibility, security, and responsible AI practices.
Mentor junior Data Scientists and Engineers while providing technical leadership across cross-functional initiatives.
Translate complex business problems into scalable AI and analytics solutions for enterprise reuse.
Collaborate with business stakeholders to prioritize high-impact opportunities and deliver measurable value.
Influence architectural decisions and contribute to the long-term roadmap for enterprise AI capabilities.

Requirements

7+ years of experience in Data Science, ML Engineering, Data Engineering, or a related technical field.
Proven experience leading production AI or advanced analytics initiatives in enterprise environments.
Strong hands-on experience with Databricks and Azure Cloud; other major cloud platforms considered.
Advanced proficiency in Python, PySpark, Spark SQL, and SQL.
Experience building scalable ETL/ELT pipelines and modern data architectures.
Experience deploying ML models into production using MLOps frameworks and best practices.
Hands-on experience implementing CI/CD pipelines using Git, GitHub, Azure DevOps, or comparable technologies.
Experience working with large-scale distributed data processing platforms.
Strong understanding of data governance, model governance, security, and enterprise AI best practices.
Ability to lead technical initiatives while remaining hands-on with development.
Excellent communication skills with experience partnering across technical teams and business stakeholders.