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Ml Data Associate Jobs (NOW HIRING)

Takeda

Associate Director, Predictive Analytics

Cambridge, MA · On-site

$64K - $65K/yr

As the Associate Director of Predictive Analytics, you will contribute to Takeda's mission by ... Lead Data Science team efforts in developing advanced analytic AI/ML models providing continuous ...

Russell Investments

Principal Data Architect

New York, NY · Hybrid

$160K - $190K/yr

Enable AI/ML use cases by designing reliable, well-structured, and observable data pipelines ... all associates and employment applicants regardless of race, religion, ancestry, creed, color ...

Russell Investments

Principal Data Architect

New York, NY · On-site

$160K - $190K/yr

Enable AI/ML use cases by designing reliable, well-structured, and observable data pipelines ... all associates and employment applicants regardless of race, religion, ancestry, creed, color ...

vTech Solution

Data Quality Engineer

Montgomery, AL · On-site

$113K - $136K/yr

... ML data readiness and feature-store-aligned data structuring. (5-8 Years) * Cloud data engineering exposure (Azure, Databricks, GCP). (5-8 Years) * Master's degree preferred. * DAMA CDMP (Associate ...

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Ml Data Associate information

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How much do ml data associate jobs pay per year?

As of Jun 16, 2026, the average yearly pay for ml data associate in the United States is $68,039.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,000.00 and $59,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an ML Data Associate, and why are they important?

To thrive as an ML Data Associate, you need strong analytical skills, attention to detail, and a solid understanding of data annotation or labeling, often supported by a degree in a technical field. Familiarity with data labeling tools, basic programming (such as Python), and experience working with machine learning platforms are typically required. Excellent communication, problem-solving abilities, and the capacity to work efficiently in teams are important soft skills. These skills ensure high-quality, accurately labeled datasets that are essential for training effective machine learning models.

What are ML Data Associates?

ML Data Associates are professionals who support machine learning projects by preparing, labeling, and validating data used to train and evaluate algorithms. They often work with large datasets, ensuring data quality and accuracy, and may use specialized tools to annotate images, text, or audio. Their work is essential for enabling machine learning models to learn from high-quality, well-structured data, and they often collaborate with data scientists and engineers to optimize data pipelines.

What is the difference between Ml Data Associate vs Data Analyst?

AspectML Data AssociateData Analyst
Required CredentialsTypically a degree in computer science, data science, or related field; familiarity with machine learning conceptsUsually a degree in statistics, mathematics, or business analytics; strong Excel and data visualization skills
Work EnvironmentTech companies, AI startups, or organizations focusing on machine learning projectsBusiness, finance, marketing, and consulting firms analyzing data for insights
Employer & Industry UsageUsed in industries developing AI models, machine learning pipelines, and data infrastructureCommon across industries for reporting, trend analysis, and strategic decision-making

While both roles involve working with data, ML Data Associates focus on preparing and managing data specifically for machine learning models, whereas Data Analysts interpret data to generate business insights. The roles overlap in data handling skills but differ in their end goals and technical focus.

What are some common challenges faced by ML Data Associates when labeling complex datasets, and how can they be effectively addressed?

ML Data Associates often encounter challenges with ambiguous data, inconsistent labeling guidelines, or rapidly evolving project requirements. To address these, it's important to maintain open communication with data scientists and project leads, ask clarifying questions, and participate in regular calibration sessions to ensure consistency. Utilizing annotation tools efficiently and staying up-to-date with best practices can also help manage complexity and improve label quality. Collaboration and feedback within the team are key to overcoming these challenges and ensuring high-quality datasets.
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Ml Data Associate jobs near you
Principal Data Architect - Data Platforms

Principal Data Architect - Data Platforms

Russell Investments

Seattle, WA • On-site

$160K - $190K/yr

Full-time

Medical, Retirement, PTO

Posted 10 days ago

Job description

Salary Range:
$160,000 USD - $190,000 USD
Specific compensation will be based on candidate's experience, skills, qualifications, commercial considerations, and other job-related factors permitted by law. At Russell Investments, salary is just one part of our compensation package. Our total rewards approach includes an annual performance bonus (subject to eligibility criteria) in addition to participation in our competitive benefits programs including healthcare, retirement, vacation, and wellbeing programs.
Job Description:
Principal Data Architect - Data Platforms
Russell Investments is seeking a hands-on Principal Data Architect to design and build modern data platforms across our enterprise. This role is focused on practical architecture, data modeling, and platform implementation in a hybrid (on-prem + cloud) environment.
You will work closely with engineering, analytics, and platform teams to design scalable data solutions, modernize legacy systems, and enable high-quality data for analytics and AI/ML use cases.
Key Responsibilities
  • Design and implement scalable data architectures, models, and pipelines for analytics and operational use cases.
  • Build and optimize data warehouse and lakehouse solutions using modern cloud platforms (Snowflake, Databricks, Azure).
  • Lead hands-on data modeling (3NF, dimensional, Data Vault) to ensure performance, consistency, and reuse.
  • Execute migration of legacy systems (SQL Server, on-prem) to cloud-based architectures, including hybrid patterns.
  • Implement data quality, MDM, and governance standards directly within data pipelines and models.
  • Enable AI/ML use cases by designing reliable, well-structured, and observable data pipelines.
  • Collaborate with engineering and analytics teams to deliver production-ready data solutions and guide technical design.

Qualifications
Required:
  • 10+ years of hands-on experience in data architecture or data engineering
  • Strong expertise in SQL and relational databases (SQL Server preferred)
  • Experience building data pipelines using DBT (3+ years preferred)
  • 5+ years of Proven experience designing and implementing cloud data platforms (Azure preferred)
  • Deep knowledge of data modeling (3NF, dimensional, Data Vault)
  • Experience with data warehouse/lakehouse design and optimization
  • Hands-on experience with data migration and hybrid architectures
  • Excellent communication and stakeholder management across technical and business domains with a demonstrated ability to translate architecture into business outcomes.

Preferred:
  • Experience with Snowflake, Databricks, Spark, or Python
  • Familiarity with data quality, observability, and governance tools
  • Exposure to AI/ML data pipelines or MLOps frameworks
  • Financial services or investment domain experience

This role is not eligible for employment-based immigration sponsorship. Applicants must be legally authorized to work in the United States without employer sponsorship, now or in the future.
Equal Employment Opportunity
Russell Investments is committed to providing equal employment opportunities for all associates and employment applicants regardless of race, religion, ancestry, creed, color, gender (including gender identity which refers to a person's actual or perceived sex, and includes self-image, appearance, behavior or expression, whether or not different from that traditionally associated with a person's biological sex), age, national origin, citizenship status, disability, medical condition, military status, veteran status, marital status, sexual orientation, past or present unemployment status , or any other characteristic protected by law.

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