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Machine Learning Data Associate Jobs in Philadelphia, PA

Data Science Tutor

Trenton, NJ ยท Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

Data Science Tutor

Chester, PA ยท Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

Data Engineer, Specialist

Wayne, PA

$103K - $124K/yr

Machine Learning * Data Governance * Minimum of five years data analytics, programming, database administration, or data management experience. * Undergraduate degree or equivalent combination of ...

Data Engineer, Specialist

Wayne, PA ยท On-site

$103K - $124K/yr

Machine Learning * Data Governance * Minimum of five years data analytics, programming, database administration, or data management experience. * Undergraduate degree or equivalent combination of ...

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Machine Learning Data Associate information

See Philadelphia, PA salary details

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How much do machine learning data associate jobs pay per hour?

As of Jun 28, 2026, the average hourly pay for machine learning data associate in Philadelphia, PA is $18.91, according to ZipRecruiter salary data. Most workers in this role earn between $15.53 and $20.14 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Data Associate, and why are they important?

To thrive as a Machine Learning Data Associate, you need strong analytical skills, attention to detail, and a basic understanding of data annotation and labeling processes, often supported by a degree in computer science or a related field. Familiarity with data management tools, annotation platforms, and sometimes scripting languages like Python is typically required. Strong communication, collaboration, and problem-solving abilities help you work efficiently with data science teams and ensure high-quality outcomes. These skills and qualities are crucial for producing accurate datasets that directly impact the effectiveness of machine learning models.

What is the salary of ML data associate?

The salary of a Machine Learning Data Associate typically ranges from $40,000 to $70,000 annually, depending on experience, location, and company size. Entry-level positions may start lower, while experienced professionals with specialized skills in data annotation and tools like Python or SQL can earn higher salaries.

What are Machine Learning Data Associates?

Machine Learning Data Associates are professionals who support the development of machine learning models by preparing, labeling, and validating data sets. Their work ensures that data used for training algorithms is accurate, consistent, and properly annotated. They may also assist with data cleaning, quality checks, and sometimes basic data analysis tasks. This role is crucial in industries where high-quality labeled data is essential for building effective AI systems.

What is the difference between Machine Learning Data Associate vs Data Analyst?

AspectMachine Learning Data AssociateData Analyst
Required SkillsData cleaning, labeling, basic programming, understanding of ML workflowsData interpretation, visualization, statistical analysis
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, marketing, healthcare sectors
Common CertificationsData Science certifications, Python, SQLExcel, Tableau, SQL certifications

The main difference is that Machine Learning Data Associates focus on preparing and labeling data specifically for machine learning models, while Data Analysts interpret data to generate insights for business decisions. Both roles require strong data skills and often overlap, but their primary objectives and work environments differ.

Is ML data associate a good job?

A Machine Learning Data Associate role involves preparing and managing data for machine learning models, often requiring skills in data cleaning, annotation, and familiarity with tools like Python or SQL. It can be a good entry-level position for those interested in AI and data science, offering opportunities to develop technical skills and gain industry experience. Job satisfaction depends on individual interests and career goals in technology and data fields.

How much do ML data associates make in the US?

Machine Learning Data Associates in the US typically earn between $35,000 and $60,000 annually, depending on experience, location, and employer. Entry-level positions may start lower, while those with specialized skills in data annotation, labeling, or familiarity with tools like Labelbox or CVAT can command higher salaries.

How does a Machine Learning Data Associate typically collaborate with data scientists and engineers within a project team?

As a Machine Learning Data Associate, you play a vital role in supporting data scientists and engineers by annotating, cleaning, and organizing large datasets to ensure high data quality. You'll frequently communicate with team members to clarify labeling guidelines, provide feedback on data inconsistencies, and report any edge cases encountered during annotation. This collaboration ensures that the datasets used for training machine learning models are accurate and comprehensive, directly impacting the success of the project. Expect regular team meetings and ongoing feedback loops to maintain alignment with evolving project requirements.

What does a machine learning data associate do?

A machine learning data associate is responsible for collecting, cleaning, and organizing data used to train machine learning models. They ensure data quality and consistency, often using tools like SQL, Python, or data annotation platforms, to support accurate model development and deployment.
Infographic showing various Machine Learning Data Associate job openings in Philadelphia, PA as of June 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, 2% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $39,328 per year, or $18.9 per hour.
Principal Machine Learning Engineer

Principal Machine Learning Engineer

Medical Guardian

Philadelphia, PA โ€ข Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 8 days ago


Job description

About Medical Guardian:ย 

Founded in 2005, Medical Guardian is a fast-growing digital health and safety company on a mission to help people live a life without limits. With 13 consecutive years on the Inc. 5000 list of Fastest Growing Companies,ย we'reย redefining what it means to age confidently and independently.ย 

We support overย 625,000 membersย nationwide with life-saving emergency response systems and remote patient monitoring solutions. Trusted by families, healthcare providers, and care managers, our work is powered by a culture of innovation, compassion, and purpose.ย 

Medical Guardian boasts a 95% customer satisfaction rate, a #1 ranking on 16 medical alert consumer choice sites and achieves a 4.7+ star rating on Google Reviews.ย 

Position Overview:

We are looking for aย Principal Machine Learning Engineerย to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus onย building,ย validating, deploying, and improving machine learning models, while also bringing principal-level judgment to problem definition, model design, stakeholder engagement, and production readiness.ย 

This is aย hands-on model-building role first. The ideal candidate should be comfortable spending most of their time working directly with data, features, models, scoring logic, validation methods, production workflows, and model improvement. They should also be able toย operateย with the maturity of a principal-level engineer: shaping unclear problems, making pragmatic technical decisions, mentoring others, and driving work forward without waiting for perfect requirements.ย 

Key Responsibilities:ย 

Hands-On Model Developmentย 

  • Build, test,ย validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support.ย 
  • Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation.ย 
  • Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness.ย 
  • Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features.ย 
  • Use tools such as Python, SQL, Spark, Databricks,ย MLflow, scikit-learn,ย XGBoost, or similar platforms and libraries.ย 
  • Move quickly from data exploration to prototype toย validatedย model to production-ready capability.ย 

Scoring, Scorecards, and Transparent Modelsย 

  • Design and implement predictive scores, risk tiers, score bands, thresholds, cut points, and intervention logic.ย 
  • Build transparent and interpretable models where explainability is important, including logistic regression, generalized linear models, decision trees, monotonic models, calibrated models, scorecard-style models, or explainable boosting approaches.ย 
  • Evaluate models for accuracy, calibration, stability, drift, fairness, interpretability, and operational usefulness.ย 
  • Help stakeholders understand what a scoreย represents, how it should be used, how it should not be used, and how changes in the score should be interpreted.ย 
  • Document model logic, features, assumptions, limitations, validation results, and recommended usage in a way that business and technical stakeholders can understand.ย 
  • Define the evidence needed to show that a model or score is valid, stable, explainable, actionable, and useful.ย 

Production ML andย MLOpsย 

  • Partner with data engineering, analytics engineering, platform engineering, and application engineering teams to move models from experimentation into reliable production workflows.ย 
  • Support model deployment, batch scoring, real-time or near-real-time inference, model versioning, monitoring, retraining, and performance tracking.ย 
  • Help define data pipelines, feature pipelines, inference flows, model outputs, feedback loops, and monitoring requirements.ย 
  • Ensure models are observable, supportable, secure, scalable, and aligned with enterprise architecture and governance expectations.ย 
  • Establish practical monitoring and feedback loops toย determineย whether models continue to perform and create value over time.ย 

Product and Rapid-Build Executionย 

  • Operate effectively in a rapid-build, startup-like environment where speed, ownership, and pragmatic decision-making are important.ย 
  • Turn early-stage ideas, ambiguous business needs, and rough concepts into working ML products, scores, prototypes, and production capabilities.ย 
  • Bring a product-engineering mindset to ML development, including user needs, workflow integration, adoption, usability, feedback loops, and measurable outcomes.ย 
  • Drive work forward without waiting for perfect requirements, while stillย identifyingย critical assumptions, risks, dependencies, and evidence needed before scaling.ย 
  • Partner with business and product stakeholders to define MVPs, iterate quickly, learn from usage, and improve models over time.ย 
  • Make smart tradeoffs between quick prototypes, durable platforms, transparent models, GenAI-enabled workflows, and longer-term ML architecture.ย 

Generative AI and AI Automationย 

  • Support the design and development of GenAI-enabled solutions, including LLM-powered workflows, RAG, summarization, conversational agents, document intelligence, and decision-support tools.ย 
  • Help evaluate when GenAI isย appropriate versusย when traditional ML, rules, analytics, or transparent scoring models are a better fit.ย 
  • Partner with product, engineering, and business stakeholders to integrate predictive models, scores, and GenAI outputs into practical workflows.ย 
  • Applyย appropriate evaluation, guardrails, monitoring, privacy controls, and human-in-the-loop processes for GenAI use cases.ย 
  • Help the organization balance innovation with explainability, safety, reliability, privacy, and operational usefulness.ย 

Requirement Shaping and Stakeholder Partnershipย 

  • Work directly with business, product, analytics, operations, and engineering stakeholders to clarify what a model is intended to predict, explain, recommend, or trigger.ย 
  • Translate business questions into measurable MLย objectives, target variables, features, validation approaches, and success metrics.ย 
  • Ask practical questions early: who will use the score, what action will it inform, what does a false positive or false negative mean, and how will we know the model is creating value?ย 
  • Communicate model behavior, tradeoffs, limitations, and recommended usage clearly to both technical and non-technical audiences.ย 
  • Help the team avoid becoming an AI ticket factory by shaping solutions, not just executing requests.ย 

Principal-Level Technical Leadershipย 

  • Provide technical leadership through hands-onย example, strong engineering judgment, and clear recommendations.ย 
  • Proactivelyย identifyย model risks, data gaps, unclear requirements, design issues, and opportunities for improvement.ย 
  • Helpย establishย practical standards for model development, validation, documentation, monitoring, and production readiness.ย 
  • Mentor other engineers and data scientists through code reviews, design reviews, modeling guidance, and shared best practices.ย 
  • Demonstrate high ownership by driving clarity, execution, and continuous improvement.ย 

Required Qualifications:ย 

  • 8+ years of professional experienceย in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields.ย 
  • 5+ years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration.ย 
  • 3+ years of experience deploying, operationalizing, or supporting modelsย in production or business-critical environments.ย 
  • Strong hands-on experience withย Python and SQL.ย 
  • Experience with modern ML and data platforms such as Databricks, Spark,ย MLflow, Snowflake, Azure, AWS, or similar technologies.ย 
  • Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, andย productionย ML lifecycle management.ย 
  • Experience translating ambiguous business problems into concrete ML designs, model requirements, validation plans, and measurable outcomes.ย 
  • Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders.ย 
  • Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability.ย 
  • Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.ย 

Preferred Qualifications:ย 

  • 10+ years of relevant professional experienceย in ML, data science, applied AI, software engineering, decisioning systems, commercial software, or production analytics.ย 
  • Experience buildingย scorecards, risk scores, health scores, engagement scores, churn scores, fraud scores, credit-style models, prioritization models, or operational decision-support models.ย 
  • Experience with transparent or interpretable models such as logistic regression, GLMs, GAMs, decision trees, monotonic models, calibrated models, scorecard-based models, or Explainable Boosting Machines.ย 
  • Experience designing score bands, thresholds, risk tiers, intervention rules, recommended actions, or decision logic based on model outputs.ย 
  • Experience working inย commercial software, SaaS, digital products, gaming, fintech,ย healthtech, consumer technology, marketplace, or other product-driven environments.ย 
  • Experience building ML, AI, analytics, or decisioning capabilities embedded into customer-facing products, operational workflows, commercial platforms, or revenue-impacting systems.ย 
  • Experience in startup, scale-up, innovation lab, new product development, or rapid-build environments where the candidate had toย operateย with ambiguity and drive work forward independently.ย 
  • Experience partnering with product managers, designers, software engineers, business leaders, and operational teams to turn ML models into usable product capabilities.ย 
  • Experience building MVPs,ย validatingย assumptions, iterating based on feedback, and maturing prototypes into production systems.ย 
  • Experience with GenAI, LLMs, RAG, AI agents, prompt engineering, model evaluation, conversational AI, summarization, document intelligence, or AI-enabled workflow automation.ย 
  • Experience combining traditional ML models with GenAI-enabled workflows, such as using predictive scores to trigger outreach, summarize customer/member context, recommend next actions, or support human decision-making.ย 
  • Experience in healthcare, population health, remote patient monitoring, insurance, financial services, safety, operations, or other domains where model trust and explainability are important.ย 
  • Experience withย MLOpsย practices including model registries, deployment pipelines, monitoring, drift detection, retraining strategies, and model governance.ย 

Success in This Role Looks Like:ย 

  • High-quality models and scores are built,ย validated, deployed,ย monitored, and improved over time.ย 
  • Model outputs are explainable and trusted by business and operational stakeholders.ย 
  • Scores are connected to real decisions, workflows, interventions, or measurable outcomes.ย 
  • The organization moves faster because this person can turn ambiguity into working ML capabilities.ย 
  • The ML team has stronger standards for model development, validation, documentation, monitoring, and production readiness.ย 
  • Business partners understand what the models do, how to use them, where their limitations are, and how to interpret changes in outputs.ย 
  • The team avoids building models in isolation and instead builds ML capabilities that are connected to products, workflows, users, and business value.ย 
  • GenAI is applied thoughtfully where it improves workflow, decision support, summarization, automation, or user experience, without replacingย appropriate modelย governance or human judgment.ย 

Benefits

  • Health Care Plan (Medical, Dental & Vision)
  • Paid Time Off (Vacation, Sick Time Off & Holidays)
  • Company Paid Short Term Disability and Life Insurance
  • Retirement Plan (401k) with Company Match