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Contract Ai Data Annotation Jobs in Raleigh, NC (NOW HIRING)

Create objective evaluation frameworks and grading criteria to assess AI performance on contract tasks with rigor and consistency. * Collaborate with product and research teams to refine data ...

Create objective evaluation frameworks and grading criteria to assess AI performance on contract tasks with rigor and consistency. * Collaborate with product and research teams to refine data ...

Create objective evaluation frameworks and grading criteria to assess AI performance on contract tasks with rigor and consistency. * Collaborate with product and research teams to refine data ...

Create objective evaluation frameworks and grading criteria to assess AI performance on contract tasks with rigor and consistency. * Collaborate with product and research teams to refine data ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Data Engineer

Cary, NC · On-site

$106K - $127K/yr

Collaborate with AI Engineers to define data contracts and ensure pipeline outputs meet solution requirements for schema, latency, and freshness. * Instrument data pipelines for monitoring, alerting ...

Data Engineer

Cary, NC

$90K - $150K/yr

Collaborate with AI Engineers to define data contracts and ensure pipeline outputs meet solution requirements for schema, latency, and freshness. * Instrument data pipelines for monitoring, alerting ...

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Contract Ai Data Annotation information

What are the key skills and qualifications needed to thrive as a Contract AI Data Annotation Specialist, and why are they important?

To thrive as a Contract AI Data Annotation Specialist, you need attention to detail, familiarity with data labeling concepts, and at least a high school diploma or relevant experience. Proficiency with annotation tools like Labelbox, Supervisely, or Amazon SageMaker Ground Truth, as well as basic understanding of data formats, is typically required. Strong communication, time management, and the ability to follow precise guidelines help you excel in this role. These skills ensure accurate, high-quality datasets that are critical for training effective AI and machine learning models.

What are some common challenges faced by contract AI data annotators, and how can they be addressed?

Contract AI data annotators often encounter challenges such as maintaining consistency across large datasets, understanding complex labeling guidelines, and meeting tight project deadlines. To address these, it's important to thoroughly review project documentation, participate in onboarding or training sessions, and communicate proactively with project managers or team leads when questions arise. Leveraging annotation tools efficiently and seeking feedback on your work can also help improve accuracy and productivity, making it easier to adapt to varying project requirements.

What is a Contract AI Data Annotation job?

A Contract AI Data Annotation job involves labeling or tagging data, such as images, text, audio, or video, to help train artificial intelligence (AI) and machine learning models. As a contractor, you'll work on specific projects for a set period, rather than as a full-time employee. The work is detail-oriented and may involve tasks like categorizing objects in photos, transcribing audio, or marking up text for sentiment or intent. This role is crucial in ensuring that AI systems learn accurately and perform well. Contract AI data annotators often work remotely and may be paid by the hour or per task.

What is the difference between Contract Ai Data Annotation vs Data Labeler?

AspectContract Ai Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, project-basedRemote or on-site, project-based
Industry UsageAI, machine learning, tech companiesAI, machine learning, tech companies
Job FocusAnnotating data for AI trainingLabeling data for AI models

Contract Ai Data Annotation and Data Labeler roles are similar, both involve preparing data for AI systems. However, Contract Ai Data Annotation often encompasses a broader range of annotation tasks and may require familiarity with specific tools or platforms. Both roles are essential in AI development and are commonly found in tech industries, with similar work environments and credential requirements.

What are the most commonly searched types of Ai Data Annotation jobs in Raleigh, NC? The most popular types of Ai Data Annotation jobs in Raleigh, NC are:
What are popular job titles related to Contract Ai Data Annotation jobs in Raleigh, NC? For Contract Ai Data Annotation jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Contract Ai Data Annotation jobs in Raleigh, NC look for? The top searched job categories for Contract Ai Data Annotation jobs in Raleigh, NC are:
What cities near Raleigh, NC are hiring for Contract Ai Data Annotation jobs? Cities near Raleigh, NC with the most Contract Ai Data Annotation job openings:
Director, Data Science

Director, Data Science

Fidelity Investments

Durham, NC • On-site

Full-time

Retirement

Re-posted 8 days ago


Fidelity Investments rating

8.7

Company rating: 8.7 out of 10

Based on 266 frontline employees who took The Breakroom Quiz

17th of 148 rated financial services


Job description


Position Description:
Leads and oversees end-to-end data science initiatives, guiding teams through data cleansing, preparation, annotation, feature engineering, exploratory analysis, and model development. Provides strategic direction on Machine Learning (ML) pipeline architecture, ensures alignment with business objectives, and drives cross-functional collaboration to deliver scalable, high-impact solutions. Draws on in-depth knowledge of the business or function to provide business unit-wide solutions by building, testing and monitoring AI models. Researches and recommends new technologies, and seizes opportunities by staying abreast of publications, tools, and techniques from the global Artificial Intelligence (AI/ML) community, in support of the strategic direction of the business unit and to achieve business-unit-wide solutions.
Primary Responsibilities:
  • Identifies business opportunities and evaluates best approaches for predictive or prescriptive analytics.
  • Implements best practices for model development, iteration, as well as code management and conducts code reviews.
  • Draws key business insights from advanced quantitative analyses and presents findings to broader audience.
  • Leads the design and deployment of advanced analytics solutions that convert raw data into actionable intelligence.
  • Delivers scalable insights, while aligning analytics infrastructure with business priorities.
  • Directs the development and integration of analytics frameworks that transform raw data into strategic insights.
  • Ensures solutions are scalable, business-aligned, and drive data-informed decision-making across the organization.
  • Leads and oversees the full AI/ML lifecycle -- data ingestion, model development, training, deployment, and monitoring.
  • Identifies and consults with internal and external technical resources to produce cross-company strategic designs.
  • Consults on deployment of major project deliverables.
  • Initiates and drives project or strategy discussions with users or external groups to resolve issues.
  • Sets vision, goals, and direction of team/organization.
  • Plans and leads organization-wide initiatives.
  • Provides leadership, technical supervision, and expertise to multiple teams in broad technical areas on complex organization-wide projects.
  • Advises senior management on technical strategy.
  • Regularly provides guidance, training, and coaching to other team members for performance and career development.
  • Identifies and plans for future resource needs.

Education and Experience:
Bachelor's degree in Analytics, Computer Science, Data Science, Operations Research, Economics, or a closely related field (or foreign education equivalent) and six (6) years of experience as a Director, Data Science (or closely related occupation) designing and building complex and scalable Artificial Intelligence (AI) pipelines to improve customer experience and drive business results in the financial services industry.
Or, alternatively, Master's degree in Analytics, Computer Science, Data Science, Operations Research, Economics, or a closely related field (or foreign education equivalent) and four (4) years of experience as a Director, Data Science (or closely related occupation) designing and building complex and scalable Artificial Intelligence (AI) pipelines to improve customer experience and drive business results in the financial services industry.
Skills and Knowledge:
Candidate must also possess:
  • Demonstrated Expertise ("DE") developing supervised and unsupervised Machine Learning (ML) algorithms -- regression, gradient boosting trees/random forest, neural network, feature selection/reduction, clustering, and parameter tuning -- using R, Python, and SAS programming languages; and analyzing and evaluating model results by creating data visualizations and business intelligence reports in Tableau and Adobe Analytics.

  • DE performing data wrangling and feature engineering for large, complex data across Cloud and on-premise data warehouses -- Oracle, Greenplum/Postgres, Hadoop/Hive, Snowflake, S3, and Redis -- using SQL, Python, and database specific SQL; standardizing and optimizing complex queries using database techniques -- partitioning and parallel processing; aggregating time series and transaction tables; creating appropriate features for modeling out of structured and unstructured data; detecting and preventing data leakage and model biases through model fairness measures using open-source AI fairness and ethics libraries.

  • DE analyzing technology solutions for supporting model deployment and integration in Cloud and on premise environments; and building model deployment and integration workflows on Amazon Web Services (AWS), on-premise Hadoop, and UNIX platforms through Git, Jenkins, Python scripts, cron jobs, step functions, Docker images, and APIs.

  • DE migrating existing AI/ML processes from on-premise environments to AWS platforms, using Extract- Transform-Load (ETL) procedures, Python, and Docker containers; creating data quality guardrails to validate model inputs and outputs using ICEDQ; and addressing financial services Cloud security constraints and record systems for workplace services -- 401(K), defined benefits, and workplace compensation and retirement plans, using AWS security tools.

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Certifications:
Category:
Data Analytics and Insights
Please be advised that Fidelity's business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.

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