Seattle, WA

Data Jobs in Seattle, WA

79 jobs found

Include remote jobs

4d ago

Principal Data Scientist

Weyerhaeuser Seattle, WA

Seattle, WA

4d ago

$131k - $197k/yr Full time Ai Ml Analytics Artificial Intelligence

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Job Description

The Principal Data Scientist leads AI and ML initiatives across manufacturing, reliability, and supply chain at onsite Seattle, WA, shaping strategy and guiding the end-to-end model lifecycle while partnering with mills to improve quality and uptime. The role carries a salary range of USD 131,082 – 196,766 per year.

Responsibilities

Design scalable solutions that span multiple business domains.
Establish reusable patterns, standards, and best practices for model development and deployment.
Lead and develop advanced machine learning, optimization, forecasting, generative AI, and decision intelligence solutions.
Define success metrics that connect model performance to business outcomes including revenue growth, operational efficiency, customer experience, safety, and risk reduction.
Collaborate with Product Managers and Operations teams to identify, prioritize, and frame opportunities solvable with a scientific framework.
Influence technical direction across multiple programs without direct authority.
Plan, execute, and analyze online and offline experiments, including A/B testing, causal inference, and counterfactual analysis, to evaluate impact on business outcomes.
Design, develop, and evaluate machine learning and deep learning models to address forecasting, optimization, reliability, anomaly detection, and decision-support problems.
Develop statistical process control methods and anomaly detection techniques to proactively address manufacturing quality issues.
Own the end-to-end model lifecycle, including feature engineering, training, validation, deployment, monitoring, retraining, and continuous improvement.
Establish standards for feature stores, model registries, inference services, observability, and governance.
Collaborate with software engineers, ML engineers, and data engineers to productionize models and integrate AI capabilities into business workflows.
Apply data science and ML techniques across domains such as manufacturing, supply chain, pricing, and logistics, extracting core patterns and adapting solutions to new problem spaces.
Translate ambiguous business problems into scientific approaches and influence stakeholders through data-driven recommendations.
Develop analytical visualizations and communicate findings via dashboards, notebooks, and presentations to drive decisions.
Mentor junior team members and contribute to data science standards, reusable patterns, and best practices.

Requirements

10+ years of experience developing and deploying machine learning and AI solutions.
Strong software engineering skills in Python and modern ML frameworks.
Deep expertise in supervised learing, forecasting, optimization, statistical modeling, anomaly detection, model evaluation, and experimentation methodologies.
Demonstrated success delivering enterprise-scale AI products from concept through production.
Experience leading highly ambiguous technical initiatives.
Proven ability to influence technical strategy across multiple teams and organizations.
Experience with experimentation and causal inference methods, including A/B testing, quasi-experimental designs, and counterfactual analysis.
Experience communicating insights using Power BI or Python-based visualization libraries such as Plotly and Matplotlib.
Experience with modern cloud platforms and data architectures, including AWS, Azure, Snowflake, and MLOps, CI/CD, and model lifecycle management.

Technologies

Python
Power BI
Plotly
Matplotlib
AWS
Azure
Snowflake
MLOps
CI/CD

Benefits

Medical, dental, vision, short- and long-term disability, and life insurance.
Health Savings Account option with company contribution.
Voluntary Long-Term Care and Employee Assistance Programs.
Support for personal volunteerism, sponsor diversity networks, mentoring, and training and development opportunities.
401(k) plan with company match and 5% employer contribution.
Paid time off: 3 weeks vacation in the first year plus accrual after six months.
Eleven paid holidays per year (88 hours) and paid parental leave.

Education

PhD in Computer Science, Statistics, Machine Learning, Operations Research, Applied Mathematics, or related quantitative discipline; or Master’s degree with equivalent industry experience.

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5d ago

Expert Consultant, Coro, AI Engineer

Bain & Co. Seattle, WA

Seattle, WA

5d ago

$129k - $172k/yr Full time Agentic Ai Ai Workflows Artificial Intelligence

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Job Description

Join Bain Coro as an Expert Consultant, AI Engineer, delivering GenAI powered tools and agentic AI workflows for B2B Commercial Excellence. This onsite role in Seattle spans projects from proof-of-concept to production deployments, translating AI innovation into measurable business outcomes. The position offers a competitive annual salary of USD 128,500 to 171,500, and requires 3–5+ years of AI/ML engineering experience along with a Bachelor’s degree in Computer Science or Engineering, or equivalent practical experience.

Benefits

Health insurance (medical, dental, and vision) with Bain paying 100% of individual premiums
Generous paid time off, including parental leave, sick leave, and paid holidays
Fully vested 401(k) company contribution
4.5% 401(k) company contribution (vesting after 3 years)
Life and Long-Term Disability insurance
Annual fitness reimbursements
Annual discretionary performance bonus

Responsibilities

Develop AI-enabled tools and products that deliver measurable business outcomes
Design and implement GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks
Implement agentic workflows with emphasis on reliability, safety, and clear failure modes, including tool use, multi-step execution, and human-in-the-loop controls
Architect and build advanced search, retrieval, and knowledge pipelines across diverse data stores (hybrid search, vector stores, graph databases / knowledge graphs, and traditional data platforms), addressing indexing, metadata, relevance tuning, freshness, caching, access controls, and source attribution
Develop robust agent capabilities including context engineering, memory and state management, orchestration, routing, and tool integration patterns
Integrate solutions into enterprise environments and workflows (APIs, data systems, collaboration tools), balancing quality, latency, cost, privacy, and adoption
Translate ambiguous client needs into clear technical requirements, tradeoffs, and delivery plans
Build and apply data science and machine learning capabilities end-to-end: data preparation, feature engineering, model selection, training, validation and testing, and performance analysis
Apply methods across classical ML and deep learning, including sequence, text, and image models when relevant
Create reproducible training and evaluation pipelines with versioning, experiment tracking, robust validation, and clear documentation
Demonstrate fluency with modern deep learning concepts, including transformer fundamentals and LLM pre-training versus post-training approaches
Write clean, testable, maintainable code and ship AI services through the full SDLC: build, test, deploy, monitor, and iterate
Implement MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, model/prompt/agent versioning, and operational readiness
Build evaluation and observability for GenAI and agentic systems: tracing, instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt and policy optimization
Design for secure enterprise deployment: access controls, auditability, data handling for sensitive and PII data, and responsible AI guardrails
Build reusable components and accelerators that scale across client contexts
Thrive in a client-facing consulting environment: communicate clearly with technical and non-technical stakeholders, lead sessions, present recommendations, and document technical details
Collaborate with Bain consultants to prioritize critical technical decisions that unlock business value and support proposal shaping and scoping

Requirements

3–5+ years of professional AI / ML engineering experience (or equivalent), with strong backend engineering fundamentals
Strong proficiency in Python and experience building APIs / services (REST / gRPC) and integrating with enterprise systems
Hands-on experience building LLM-powered applications with attention to latency, cost, reliability, and security
Experience building advanced retrieval / search systems (hybrid retrieval, vector search, reranking), and comfort working across vector, graph, relational, document, and search data stores
Experience implementing agentic patterns (context management, tool integration, orchestration, memory/state handling) with modern frameworks (LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops
Experience creating reusable skills, tools, and services (including MCP) for agent use, with schema validation (Pydantic) to enforce data contracts
Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling
Experience deploying and operating services on AWS, GCP, and / or Azure, with focus on reliability and observability
Experience with Docker and Kubernetes and operating services in production
Proven ability to implement security, privacy, and governance requirements for AI systems, including authentication/authorization and PII handling
Experience training, validating, and testing ML models; solid understanding of overfitting, generalization, and evaluation methodology
Practical experience with feature engineering and data preprocessing for real-world datasets
Familiarity with a broad set of ML algorithms (classical and deep learning) and the ability to select appropriate methods
Familiarity with deep learning frameworks (PyTorch / TensorFlow) and ML lifecycle tooling (experiment tracking, model registry, feature store concepts)
Ability to operate effectively in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a team
Excellent interpersonal and communication skills, with the ability to explain complex technical decisions to mixed audiences
Strong stakeholder management and client-facing capabilities
MBA or PhD in a technical field (preferred)
Background in consulting, professional services, or B2B analytics environments (preferred)
Experience collaborating with major AI ecosystem partners on real client deployments (preferred)

Technologies

Python
REST
gRPC
LangGraph
OpenAI Agents SDK
Pydantic AI
PyTorch
TensorFlow
Docker
Kubernetes
AWS
GCP
Azure
Vector stores
Graph databases
Knowledge graphs

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6d ago

Supply Chain Data Analyst

Buddha Jewelry Seattle, WA

Seattle, WA

6d ago

$75k - $87k/yr Full time Analyst Analytics Business Intelligence

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Job Description

Buddha Jewelry seeks a data-driven Supply Chain Data Analyst to transform sourcing, inventory, and logistics data into clear, actionable insights. This on-site role in Seattle focuses on maintaining SKU data quality, partnering with cross-functional teams on product lifecycle decisions, and leveraging AI-enhanced forecasting to align supply with demand. The position offers a salary range of USD 75,000 - 87,000 per year.

Responsibilities

Deliver analytics and AI-enhanced reporting across sourcing, inventory, and logistics to inform strategic decisions in the supply chain.
Collaborate with cross-functional teams to analyze product lifecycle data and support decisions with timely, accurate information.
Own the setup and upkeep of SKUs, tags, and item configurations to ensure clean, reliable data across systems.
Identify data-driven improvements to streamline supply chain workflows and boost overall system efficiency.
Manage AI-enabled demand forecasting models and outputs to optimize inventory levels and reduce stockouts or overstock scenarios.

Requirements

3+ years of experience in supply chain analytics, data analysis, or related fields.
Strong analytical abilities with the capacity to translate data into clear, actionable insights.
Experience with demand forecasting, inventory management, or logistics reporting.
Comfort working with AI-enhanced tools and a genuine curiosity about how technology can improve outcomes.
Highly detail-oriented with a focus on data integrity.
Tech-savvy with experience in NetSuite, Shopify, and Google Suite/MS tools.
Strong collaborator capable of partnering with Operations, Finance, and Product teams.
100% on-site presence in Seattle.

Technologies

NetSuite
Shopify
Google Suite
Microsoft Office
Looker
Tableau
Google Data Studio

Benefits

Health insurance
Vision insurance
Life insurance
Dental insurance
SIMPLE retirement savings plan
Commuter benefits
Employee discount
Paid holidays

Physical Demands

Ability to remain stationary 50% or more of the time.
Capability to move around the office to access equipment and resources.
Proficiency in operating a computer and common office devices such as scanners, copiers, and printers.
Frequent handling of non-electronic resources and small items.
Occasional lifting or moving of equipment up to 50 pounds.
Ability to observe, assess, and distinguish office resources, documents, and product-related materials.

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6d ago

Data Scientist

Starbucks Seattle, WA

Seattle, WA

6d ago

$113k - $170k/yr Full time Analytics Business Analytics Business Intelligence

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Job Description

Starbucks is seeking a Data Scientist in Seattle, onsite, to partner with cross-functional teams, unravel complex data relationships, and build core data science models that inform business decisions.

Responsibilities

Extract, transform, and validate data using SQL and other appropriate tools to produce analysis-ready datasets and scalable dimensional/relational processes that support measurement and decision making.
Develop or refine data science models and associated pipelines, creating data workflows for model training and outputs within shared codebases.
Communicate insights clearly by detailing methodology, building dashboards and visualizations to validate results and monitor model performance, and translating outputs into actionable recommendations.

Requirements

1+ year of experience in data science, applied analytics, or related roles
BA/BS or equivalent experience
Proficiency in SQL and Python and/or R for data preparation, pipelines, and modeling
Working knowledge of common modeling techniques such as regression, classification, decision trees, clustering, and causal inference
Experience with data visualization tools (Tableau, Power BI, or similar)
Strong communication skills and meticulous attention to detail

Technologies

SQL
Python
R
Tableau
Power BI

Benefits

Medical, dental, vision, basic and supplemental life insurance, and other voluntary insurance benefits
Short-term and long-term disability
Paid parental leave
Family expansion reimbursement
Paid vacation from date of hire
Sick time accrued at 1 hour for every 25 hours worked
Eight paid holidays
Two personal days per year
401(k) retirement plan with employer match
Discounted company stock program (S.I.P.)
Starbucks equity program (Bean Stock)
Incentivized emergency savings
Financial well-being tools
100% upfront tuition coverage for a first-time bachelor’s degree through Arizona State University’s online program via the Starbucks College Achievement Plan
Student loan management resources
Access to other educational opportunities
Backup care
DACA reimbursement

Preferred Qualifications

Degree concentration in a quantitative field such as Statistics, Mathematics, Computer Science, Engineering, Economics, Psychology, Quantitative Social Science, or related
MS or PhD advanced degree preferred
Experience in shared analytics/engineering environments with version control, documentation, and reproducible workflows
Experience supporting model measurement and monitoring through dashboards and performance reporting
Expertise in building Bayesian elasticity models and causal inference
Experience in optimization techniques such as linear programming, mixed-integer programming, or heuristic algorithms for decision support

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6d ago

Senior/Lead Data Scientist, Data & Analytics

Starbucks Seattle, WA

Seattle, WA

6d ago

$130k - $217k/yr Full time Senior Big Data Data Analysis

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Job Description

Starbucks is seeking a Senior/Lead Data Scientist, Data & Analytics to develop analytical models and machine learning solutions that guide business decisions and operationalize insights across cross-functional teams. This onsite role is based in Seattle, WA.

Role Details

Location: Seattle, WA (onsite)

Salary: USD 130,300 - 217,400 per year

Minimum Experience: 4 years

Education: BA/BS in a quantitative field

Responsibilities

Create and apply analytical models using statistical methods or machine learning to enhance decision making, performance, and customer and partner outcomes.
Translate business challenges into data science solutions by outlining approach, identifying and validating data sources, and delivering clear recommendations to both technical and non-technical stakeholders.
Operationalize work through repeatable analysis and model pipelines, monitor and improve model performance over time, and contribute to shared standards including documentation, version control, and reproducibility.

Requirements

At least four years of experience in data science, applied analytics, or a closely related field
Bachelor's degree in a quantitative field or equivalent practical experience
Proficiency in Python or R and SQL

Technologies

Python
R
SQL
AWS
Azure
Spark

Benefits

Medical, dental, and vision insurance, plus basic and supplemental life insurance
Short-term and long-term disability coverage
Paid parental leave
Family expansion reimbursement
Paid vacation from date of hire
Sick time accrued at 1 hour per 25 hours worked
Eight paid holidays
Two personal days per year
401(k) retirement plan with employer match
Discounted company stock program (S.I.P.)
Starbucks equity program (Bean Stock)
Incentivized emergency savings and financial well-being tools
100 percent tuition coverage for a first-time bachelor’s degree through Arizona State University’s online program via the Starbucks College Achievement Plan
Student loan management resources
Access to additional educational opportunities
Backup care
DACA reimbursement

Preferred Qualifications

Master’s or PhD in a quantitative discipline or equivalent advanced experience
Experience deploying or maintaining models in production environments, including monitoring, retraining, and performance measurement
Experience with cloud analytics platforms (AWS or Azure) and distributed computing (Spark)
Familiarity with experimental design or causal inference and translating results into business actions
Proven ability to mentor others and influence cross-functional partners toward adoption

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7d ago

Machine Learning Engineer

Amazon.com Services LLC Seattle, WA

Seattle, WA

7d ago

$144k - $194k/yr Full time Ai Ml Amazon Web Services AWS

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Job Description

Join Amazon.com Services LLC in Seattle, WA, as a Machine Learning Engineer on the Amazon Ads team, developing near real time ML systems and infrastructure for ad relevance and real time personalization across Amazon surfaces.

Responsibilities

Design, build, and operate near real time data ingestion pipelines with Apache Flink, Kinesis, and DynamoDB to process shopper behavioral signals at scale (100K+ TPS, sub-second latency).
Develop and maintain ML feature generation services that convert raw customer interactions into ML ready signals used by prediction models across Amazon Ads.
Improve the scalability, automation, and efficiency of large scale training and real time inference systems.
Build and enhance data quality monitoring, automated alerting, and self healing mechanisms to ensure signal reliability at 99.9%+ availability.
Collaborate with applied scientists and partner engineering teams to onboard new shopper signals, define feature schemas, and optimize serving latency for real time ad personalization.
Contribute to system design discussions, propose technical solutions for ambiguous problems, and drive end to end implementation with guidance from senior engineers.

Requirements

2+ years of non internship professional software development experience.
Experience programming in at least one software language.
2+ years of design or architecture experience for new and existing systems, including design patterns, reliability, and scaling.
Experience in machine learning, data mining, information retrieval, statistics, or natural language processing.
Bachelor's degree in computer science or equivalent.
2+ years of full software development lifecycle experience, including coding standards, code reviews, source control, build processes, testing, and operations.

Technologies

Apache Flink
Kinesis
DynamoDB

Benefits

Sign-on payments
Restricted stock units (RSUs)
Health insurance
401(k) matching
Paid time off
Parental leave
Flexible Spending Accounts
Adoption and Surrogacy Reimbursement coverage
Employee Assistance Program (EAP)
Mental Health Support
Medical Advice Line
Basic Life and AD&D insurance
Supplemental life plans

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7d ago

Senior AWS Redshift Data Engineer (Contract | 1099)

Braintrust Seattle, WA

Seattle, WA

7d ago

$140 - $173/hr Contract Amazon Web Services Analytics Big Data

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Job Description

A contract role for a Senior AWS Redshift Data Engineer at Braintrust, focused on optimizing large-scale Redshift environments and data infrastructure.

Responsibilities

Optimize and maintain large-scale AWS Redshift clusters and data warehouse performance (60%)
Improve and support ETL pipelines using Kafka and ECS jobs (30%)
Enhance BI performance and data availability for reporting tools such as Looker (10%)
Prepare and expose data for AI-enabled applications and MCPs
Collaborate closely with the data engineering and BI teams

Requirements

7–10 years of experience in data engineering, database administration, or related roles
Must be based in the United States
Extensive experience optimizing AWS Redshift clusters, including environments with hundreds of terabytes of data
Strong experience tuning, repairing, and maintaining heavily queried and frequently updated Redshift tables
Experience configuring and managing Kafka streams
Experience with Looker or a similar BI platform
Familiarity with Python and/or AI-enabled data workflows is a strong plus