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Annotation Finance Jobs in Atlanta, GA (NOW HIRING)

Smarsh

Lead Data Scientist

Atlanta, GA

Summary As a Lead Data Scientist (NLP & Financial Compliance) at Smarsh , you will spearhead the ... Data annotation and quality review * Exploratory data analysis and model fail state analysis

Smarsh

Lead Data Scientist

Atlanta, GA · Remote

$166K - $214K/yr

Summary As a Lead Data Scientist (NLP & Financial Compliance) at Smarsh , you will spearhead the ... Data annotation and quality review * Exploratory data analysis and model fail state analysis

Ottobock

Clinical Assistant

Stockbridge, GA · On-site

Perform outcomes measures, medical history documentation and clinical notes annotation support for ... financial responsibility, and medical documentation requirements. * Maintain a clean office work ...

Ottobock

Clinical Assistant

Atlanta, GA · On-site

Perform outcomes measures, medical history documentation and clinical notes annotation support for ... financial responsibility, and medical documentation requirements. * Maintain a clean office work ...

Ottobock

Clinical Assistant

Atlanta, GA · On-site

Perform outcomes measures, medical history documentation and clinical notes annotation support for ... financial responsibility, and medical documentation requirements. * Maintain a clean office work ...

Ottobock

Clinical Assistant

Stockbridge, GA · On-site

Perform outcomes measures, medical history documentation and clinical notes annotation support for ... financial responsibility, and medical documentation requirements. * Maintain a clean office work ...

All JobsAnnotation Finance JobsAnnotation Finance Jobs in Atlanta, GA

Annotation Finance information

See Atlanta, GA salary details

$20.2K

$51.4K

$89.9K

How much do annotation finance jobs pay per year?

As of Aug 9, 2026, the average yearly pay for annotation finance in Atlanta, GA is $51,362.00, according to ZipRecruiter salary data. Most workers in this role earn between $40,400.00 and $57,700.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an annotation finance specialist, and why are they important?

To thrive as an Annotation Finance Specialist, you need a solid understanding of financial concepts, data analysis, and attention to detail, typically supported by a degree in finance, accounting, or a related field. Familiarity with data annotation tools, financial modeling software, and spreadsheet applications like Excel is commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you interpret complex data and collaborate with stakeholders. These skills ensure accurate data labeling and analysis, which are critical for driving informed financial decisions and supporting AI or machine learning initiatives in the finance sector.

What is an annotation finance job?

An Annotation Finance job typically involves labeling and categorizing financial data to train machine learning models used in fintech applications, such as fraud detection, risk assessment, and financial forecasting. Professionals in this role review and annotate various financial documents, transactions, or datasets to ensure the accuracy and quality of the training data. Attention to detail and a good understanding of financial terminology are important for this position. Annotation Finance specialists may work for financial institutions, technology companies, or data labeling firms. Their contributions are crucial for developing reliable AI systems in the finance sector.

What are common challenges faced by professionals working in annotation finance, and how can they be addressed?

Professionals in Annotation Finance often face challenges related to maintaining high data accuracy and consistency, especially when working with large volumes of financial documents or transactions. Ensuring compliance with evolving regulatory standards and managing sensitive financial information securely are also key concerns. To address these challenges, it's important to stay updated on industry best practices, utilize robust annotation tools, and communicate closely with team members and compliance officers. Regular training and adopting quality assurance protocols can further enhance data reliability and workflow efficiency.

What is the difference between Annotation Finance vs Data Analyst?

AspectAnnotation Finance
Primary RoleAnnotating financial data for machine learning models in finance
Required SkillsFinancial knowledge, data annotation, attention to detail
Work EnvironmentData labeling teams, finance tech companies
CertificationsBasic financial certifications may help, but not mandatory

Annotation Finance focuses on labeling financial data for AI applications, requiring financial understanding and data annotation skills. Data Analysts analyze and interpret data to inform business decisions, often involving data cleaning and reporting. While both roles work with data, Annotation Finance is specialized in preparing data for machine learning, whereas Data Analysts focus on data analysis and insights.

What are popular job titles related to Annotation Finance jobs in Atlanta, GA? For Annotation Finance jobs in Atlanta, GA, the most frequently searched job titles are:
What job categories do people searching Annotation Finance jobs in Atlanta, GA look for? The top searched job categories for Annotation Finance jobs in Atlanta, GA are:
What cities near Atlanta, GA are hiring for Annotation Finance jobs? Cities near Atlanta, GA with the most Annotation Finance job openings:
Annotation Finance jobs near you
Infographic showing various Annotation Finance job openings in Atlanta, GA as of August 2026, with employment types broken down into 93% Full Time, and 7% Contract. Highlights an 100% In-person job distribution, with an average salary of $51,362 per year, or $24.7 per hour.

Lead Data Scientist

Smarsh

Atlanta, GA

Full-time

Re-posted 24 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
$166,000 - $214,000 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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