378 jobs for "AI Data Scientist"

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Principal Data Scientist

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Principal Data Engineer (AWS, Databricks, Ai, ML Flow, Data Architecture, Apache Airflow)

At Mastercard - Pune

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Apple

Data Scientist, AI/ML Model Quality

Austin, TX$150,400 – $277,600Posted 27 days ago

Apply on company site View full job

Why you should apply for a job to Apple:

Ranked as one of the Best Technology Companies in 2020
3.7/5 in overall job satisfaction
3.1/5 in supportive management
66% say women are treated fairly and equally to men
65% would recommend this company to other women
66% say the CEO supports gender diversity

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Would you like to contribute to Machine Learning and Generative AI technologies? Are you passionate about the integrity of the data that powers AI systems at scale? Do you believe that trustworthy data is the foundation of every great model? We truly believe it is! We are defining what exceptional data quality looks like for machine learning across Wallet, Payments, and Commerce. As a Data Scientist, AI/ML Model Quality, you will build and maintain intelligent systems, validation frameworks, and monitoring pipelines that keep our data ecosystem healthy - ensuring that every model we build is trained, evaluated, and deployed on data we can trust. Your work sits at the foundation of every ML feature that reaches hundreds of millions of users. You'll work at the intersection of statistical rigor and production systems, collaborating closely with ML Engineering, Data Engineering, Privacy, and Legal teams. This unique opportunity puts you at the center of ML and AI quality - owning the health of training and validation datasets, defining and analyzing observability metrics to surface actionable product insights, and leading telemetry analysis across GenAI workflows - ensuring Apple's financial features are built on the highest-quality data, whether powering conventional ML models or the latest generative AI systems.

Description

The ideal candidate is a detail-obsessed data scientist who understands that model quality starts long before training - it starts with the data. You have strong statistical instincts, know how silent degradation and data drift manifest in production systems, and can translate raw quality signals into insights that drive real decisions. You will own the health of the data ecosystem that underpins ML and GenAI features across Wallet, Payments, and Commerce - building validation frameworks, defining observability metrics, and leading telemetry analysis that keeps every model trained, evaluated, and monitored on data teams can trust. Your work sits at the foundation of every ML feature that reaches hundreds of millions of users.","responsibilities":"Curate, analyze, and maintain gold-standard ground-truth datasets for model evaluation and continuous validation across both ML and GenAI systems.

Audit training data for systemic bias and fairness gaps prior to model deployment; establish ongoing analytical checks to catch bias introduced by data drift over time.

Define, track, and report key data quality metrics - completeness, accuracy, timeliness, validity - for engineering and leadership audiences.

Design and define automated data quality rules and thresholds, partnering with Data Engineering to ensure these checks are integrated into model development and CI/CD workflows

Define and own ML observability metrics - model performance, output distributions, training-serving skew, silent degradation and feature drift - translating raw production signals into actionable insights for engineering and product teams.

Design and develop observability dashboards and reporting workflows that give stakeholders a consistent, real-time view of model health across both conventional ML and GenAI systems.

Define and analyze telemetry across GenAI workflows, tracking quality signals such as output coherence, latency, task completion rates, and regression patterns.

Identify degradation patterns and domain-specific failure modes in GenAI systems through systematic telemetry analysis, translating findings into concrete recommendations for model and data teams.

Preferred Qualifications

Experience with data visualization and dashboarding tools (e.g., Tableau, Apache Superset, Databricks) to present complex ML telemetry.

Familiarity with LLM evaluation frameworks (e.g. LangSmith) or techniques like LLM-as-a-judge.

Experience with Bayesian or causal graph-based approaches to synthetic data generation.

Familiarity with confidence calibration techniques and uncertainty quantification.

Experience with ML monitoring or observability platforms (e.g., MLflow, Weights & Biases, or equivalent).

Experience working with privacy-constrained data or under regulatory compliance frameworks (GDPR, DMA).

Background in financial services, fintech, or consumer payment products.

Minimum Qualifications

A Bachelor's degree with exceptional hands-on experience in ML/AI model quality or applied research or a M.S or Ph.D in Machine Learning, Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field is strongly preferred.

3+ years of experience in data science or a closely related analytical role, with a strong focus on data quality, model evaluation, or ML observability in production environments.

Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL for complex data analysis, metric creation, and validation.

Experience querying and analyzing large-scale datasets using distributed computing frameworks (e.g., PySpark, Spark, or distributed SQL).

Solid understanding of statistical methods - hypothesis testing, distribution analysis, data drift detection, and statistical process control.

Experience in defining and tracking ML model health metrics in production - model performance monitoring, feature drift detection, and observability instrumentation.

Familiarity with GenAI or LLM systems, including common quality failure modes, output evaluation approaches, and telemetry instrumentation.

Strong communication skills - ability to translate complex data quality findings and model health risks into clear, actionable insights for both engineering and non-technical stakeholde

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

About Apple

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10,000+ employees
Industry: Technology: Manufacturing

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