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Financial Data Scientist Jobs in Oregon (NOW HIRING)

Smarsh

Lead Data Scientist

OR · On-site +1

Summary As a Lead Data Scientist (NLP & Financial Compliance) at Smarsh , you will spearhead the development of state-of-the-art natural language processing (NLP) and large language model (LLM ...

Concora Credit Inc.

Data Scientist

Beaverton, OR · On-site

As a Data Scientist, your primary role will be to develop custom fraud detection, credit risk ... in the Finance industry, or Ph.D in field mentioned above with 1-3 years of relevant experience ...

Concora Credit Inc.

Data Scientist

Beaverton, OR

As a Data Scientist, your primary role will be to develop custom fraud detection, credit risk ... in the Finance industry, or Ph.D in field mentioned above with 1-3 years of relevant experience ...

Concora Credit Inc.

Data Scientist

Beaverton, OR · On-site

As a Data Scientist, your primary role will be to develop custom fraud detection, credit risk ... in the Finance industry, or Ph.D in field mentioned above with 1-3 years of relevant experience ...

Concora Credit Inc.

Data Scientist

Beaverton, OR · On-site

As a Data Scientist, your primary role will be to develop custom fraud detection, credit risk ... in the Finance industry, or Ph.D in field mentioned above with 1-3 years of relevant experience ...

ICI Services

... Data Scientist to Join our Team in a Remote capacity. At ICI Services, our employee-owners drive ... Our diverse team of acquisition experts, financial analysts, engineers, logisticians, IT ...

Instacart

Senior People Data Scientist

OR · On-site +1

We're hiring a Senior People Data Scientist (L5) to serve as an enterprisewide people analytics ... Finance on data architecture, and driving adoption through enablement and training. * Own Data ...

Amerilife Group, LLC

Finance Data Platform Specialist

OR · On-site +1

$115K - $130K/yr

The ideal candidate possesses strong analytical skills, experience with enterprise financial ... Bachelor's degree in Information Systems, Computer Science, Business, Finance, Accounting, or a ...

Instacart

Senior Marketing Decision Scientist II

OR · On-site +1

Overview Instacart's Marketing Data Science and Analytics team partners across Marketing, Strategic Finance, and Product to power data-driven growth. As a Senior Marketing Decision Scientist II, you ...

Instacart

Senior Data Engineer II, Finance

OR · On-site +1

$105K - $143K/yr

The team plays a critical role in defining how financial data is modeled and standardized for ... Bachelor's degree in Computer Science, computer engineering, electrical engineering or equivalent ...

Pigment

... data, people, and processes, enabling them to plan and adapt rapidly. With a vibrant team of over ... Our recognition as a Visionary in the 2024 Gartner Magic Quadrant for Financial Planning Software ...

Sun Life Financial

Director, Data Science

Portland, OR · On-site

At Sun Life, we're driven by our Purpose: helping our Clients achieve lifetime financial security ... The Director is responsible for advancing how Sun Life US uses data science, GenAI, and emerging AI ...

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All JobsFinancial Data Scientist JobsFinancial Data Scientist Jobs in Oregon

Financial Data Scientist information

See Oregon salary details

$39.6K

$129.8K

$207.8K

How much do financial data scientist jobs pay per year?

As of Aug 28, 2026, the average yearly pay for financial data scientist in Oregon is $129,770.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,100.00 and $143,800.00 per year, depending on experience, location, and employer.

What does a financial data scientist do?

A Financial Data Scientist analyzes complex financial data using statistical, machine learning, and computational techniques to identify patterns, forecast trends, and support decision-making within financial institutions. They work with large datasets from sources like market data, customer transactions, and economic indicators to develop predictive models and data-driven strategies. Their work helps organizations manage risk, optimize portfolios, detect fraud, and gain a competitive edge in the financial sector.

What are the key skills and qualifications needed to thrive as a financial data scientist?

To thrive as a Financial Data Scientist, you need strong quantitative skills, proficiency in statistical analysis, and a background in finance or economics, typically supported by a relevant degree. Familiarity with programming languages such as Python or R, experience with machine learning frameworks, and knowledge of financial databases and tools like Bloomberg Terminal are also important. Critical thinking, problem-solving, and effective communication help you translate complex data into actionable insights for stakeholders. These skills are crucial for building accurate financial models, driving data-driven decision-making, and delivering value in dynamic financial environments.

How does a financial data scientist typically collaborate with other departments within a financial organization?

Financial Data Scientists regularly work alongside cross-functional teams, including risk analysts, portfolio managers, and software engineers. They collaborate to develop predictive models, automate data pipelines, and translate complex data insights into actionable business strategies. Effective communication is key, as they must explain technical findings to stakeholders with varying levels of data literacy. This collaborative environment not only fosters innovation but also offers opportunities to learn from other experts and expand your professional network.

What is the difference between Financial Data Scientist vs Quantitative Analyst?

AspectFinancial Data ScientistQuantitative Analyst
Required CredentialsDegree in Finance, Data Science, or related fields; often certifications like CFA or FRMDegree in Mathematics, Statistics, Finance; CFA or FRM common
Work EnvironmentFinancial institutions, tech firms, investment firms; focus on data modeling and predictive analyticsInvestment banks, hedge funds, asset management; focus on trading strategies and risk modeling
Employer & Industry UsageUsed across finance and tech sectors for data-driven decision makingPrimarily in finance for trading, risk, and portfolio management

Financial Data Scientists analyze large datasets to develop predictive models and insights, often combining finance knowledge with data science skills. Quantitative Analysts focus on developing mathematical models for trading and risk management. While both roles require strong quantitative skills and finance knowledge, Financial Data Scientists tend to work more on data analysis and machine learning, whereas Quantitative Analysts focus on financial modeling and trading strategies.

What are popular job titles related to Financial Data Scientist jobs in Oregon?

For Financial Data Scientist jobs in Oregon, the most frequently searched job titles are:

What job categories do people searching Financial Data Scientist jobs in Oregon look for?

The top searched job categories for Financial Data Scientist jobs in Oregon are:

What cities in Oregon are hiring for Financial Data Scientist jobs?

Cities in Oregon with the most Financial Data Scientist job openings:

Financial Data Scientist jobs near you
Infographic showing various Financial Data Scientist job openings in Oregon as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 15% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $129,770 per year, or $62.4 per hour.

Lead Data Scientist

Smarsh

OR • On-site, Remote

Full-time

Re-posted 12 days ago

Job description

Who are we?

Smarsh empowers its customers to manage risk and unleash intelligence in their digital communications. Our growing community of over 6500 organizations in regulated industries counts on Smarsh every day to help them spot compliance, legal or reputational risks in 80+ communication channels before those risks become regulatory fines or headlines.  Relentless innovation has fueled our journey to consistent leadership recognition from analysts like Gartner and Forrester, and our sustained, aggressive growth has landed Smarsh in the annual Inc. 5000 list of fastest-growing American companies since 2008.

Summary
 
As a Lead Data Scientist (NLP & Financial Compliance) at Smarsh, you will spearhead the development of state-of-the-art natural language processing (NLP) and large language model (LLM) solutions that power next-generation compliance and surveillance systems. You'll work on highly specialized problems at the intersection of natural language processing, communications intelligence, financial supervision, and regulatory compliance, where unstructured data from emails, chats, voice transcripts, and trade communications hold the keys to uncovering misconduct and risk.
 
The role will involve working with other Senior Data Scientists and mentoring Associate Data Scientists in analyzing complex data, generating insights, and creating solutions as needed across a variety of tools and platforms. This role demands both technical excellence in NLP modeling and a deep understanding of financial domain behavior-including insider trading, market manipulation, off-channel communications, MNPI, bribery, and other supervisory risk areas. The ideal candidate for this position will possess the ability to perform both independent and team-based research and generate insights from large data sets with a hands-on/can do attitude of servicing/managing day to day data requests and analysis.
 
This role also offers a unique opportunity to get exposure to many problems and solutions associated with taking machine learning and analytics research to production. On any given day, you will have the opportunity to interface with business leaders, machine learning researchers, data engineers, platform engineers, data scientists and many more, enabling you to level up in true end-to-end data science proficiency.
How will you contribute?
  • Collect, analyze, and interpret small/large datasets to uncover meaningful insights to support the development of statistical methods / machine learning algorithms.
  • Lead the design, training, and deployment of NLP and transformer-based models for financial surveillance and supervisory use cases (e.g., misconduct detection, market abuse, trade manipulation, insider communication).
  • Development of machine learning models and other analytics following established workflows, while also looking for optimization and improvement opportunities
  • Data annotation and quality review 
  • Exploratory data analysis and model fail state analysis 
  • Contribute to model governance, documentation, and explainability frameworks aligned with internal and regulatory AI standards.
  • Client/prospect guidance in machine learning model and analytic fine-tuning/development processes
  • Provide guidance to junior team members on model development and EDA
  • Work with Product Manager(s) to intake project/product requirements and translate these to technical tasks within the team's tooling, technique and procedures
  • Continued self-led personal development
What will you bring?
  • Strong understanding of financial markets, compliance, surveillance, supervision, or regulatory technology
  • Experience with one or more data science and machine/deep learning frameworks and tooling, including scikit-learn, H2O, keras, pytorch, tensorflow, pandas, numpy, carot, tidyverse
  • Command of data science and statistics principles (regression, Bayes, time series, clustering, P/R, AUROC, exploratory data analysis etc...)
  • Strong knowledge of key programming concepts (e.g. split-apply-combine, data structures, object-oriented programming)
  • Solid statistics knowledge (hypothesis testing, ANOVA, chi-square tests, etc...)
  • Knowledge of NLP transfer learning, including word embedding models (gloVe, fastText, word2vec) and transformer models (Bert, SBert, HuggingFace, and GPT-x etc.)
  • Experience with natural language processing toolkits like NLTK, spaCy, Nvidia NeMo
  • Knowledge of microservices architecture and continuous delivery concepts in machine learning and related technologies such as helm, Docker and Kubernetes
  • Familiarity with Deep Learning techniques for NLP.
  • Familiarity with LLMs - using ollama & Langchain
  • Excellent verbal and written skills
  • Proven collaborator, thriving on teamwork
 
Preferred Qualifications
  • Master's or Doctor of Philosophy degree in Computer Science, Applied Math, Statistics, or a scientific field
  • Familiarity with cloud computing platforms (AWS, GCS, Azure)
  • Experience with automated supervision/surveillance/compliance tools
$180,000 - $200,000 a year
 
The above salary range represents Smarsh's good faith and reasonable estimate of the range of possible base compensation at the time of posting. Any applicable bonus programs will be discussed during the recruiting process.
 
The salary for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, location, specialty and training.
 
Local cost of living assessments are done for each new hire at the time of offer.
About our culture

Smarsh hires lifelong learners with a passion for innovating with purpose, humility and humor. Collaboration is at the heart of everything we do. We work closely with the most popular communications platforms and the world's leading cloud infrastructure platforms. We use the latest in AI/ML technology to help our customers break new ground at scale. We are a global organization that values diversity, and we believe that providing opportunities for everyone to be their authentic self is key to our success. Smarsh leadership, culture, and commitment to developing our people have all garnered Comparably.com Best Places to Work Awards. Come join us and find out what the best work of your career looks like.
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