382 jobs for "Data Scientist I"

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Senior Data Scientist - Enterprise Credit

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Apple

Applied Scientist

Austin, TX$175,000 – $308,500Posted Oct 7, 2026

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

Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›

Services at Apple help hundreds of millions of customers get the most out of the devices they love through amazing apps, award-winning shows and movies, immersive music in spatial audio, world-class workouts and meditations, super fun games and more! The Services Data Science & Analytics organization is passionate about developing discerning insights and AIML solutions to help continually improve these services and accelerate growth while maintaining a strong dedication to customer privacy.

We are currently seeking an experienced and passionate Applied Scientist, who will work on innovative products at the intersection of causal inference, statistics, and machine learning to help optimize marketing channels, via observational testing frameworks, counterfactual modeling, and lifetime value estimation. As a key member of our diverse organization, you'll have the rare and rewarding opportunity to work with datasets of unique magnitude, richness, and dedication to privacy that will frequently require novel approaches. You'll work alongside partners across Business, Marketing, Product, Finance, and Engineering daily to deliver material customer and business value.

Description

As an Applied Scientist, you will have the responsibility of pushing the boundaries of how Causal Inference and AIML can be leveraged to better serve our customers. You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference solutions, that directly impact our products and provide a granular understanding of key marketing effectiveness. You will also be instrumental in defining the technical vision, strategy, and execution roadmap for our AIML initiatives, ensuring that we deliver high-quality, scalable, and impactful models that solve complex customer acquisition and engagement challenges. You will also be a key driver in fostering a vibrant culture of innovation, continuous learning, and collaborative problem-solving.","responsibilities":"Engineer end-to-end scalable and robust Causal Inference products which provide Apple with an understanding of the health of our Services' marketing efforts.

Dive deep into large-scale data sources to uncover opportunities for Causal Inference automation, predictive methods, and quantitative modeling.

Collaborate with product managers, data scientists, and other engineering teams to translate business requirements into technical specifications and deliver impactful, practical solutions, increasing internal adoption of causal inference approaches and democratizing data

Stay abreast of the latest advancements in causal inference and AIML research, evaluating and integrating new frameworks where appropriate

Champion best practices in software engineering, MLOps, code quality, testing, documentation, and ensure compliance with data privacy and security

Preferred Qualifications

PhD in related field

Hands-on experience leveraging Generative AI to improve productivity and generate new insights

Curious business attitude with an ability to condense complex concepts and models into clear and concise takeaways that drive action

Minimum Qualifications

Master's degree in Statistics, Economics, Mathematics, Machine Learning, Computer Science, Engineering, or a related technical field

3+ years of experience as an Applied Scientist, Machine Learning, or Data Scientist role

Familiarity with a broad range of quasi-experimental Causal Inference techniques such as diff-in-diff, synthetic control method, panel analysis, regression discontinuity design, interrupted time series, and propensity score matching

Hands-on experience building Marketing Mix models and validation through Matched Market testing

Solid understanding of AIML technologies including Generative AI

Proven track record of successfully delivering complex projects from start to finish

Proficiency in programming languages such as Python, R, SQL, Java, or C++

Experience with cloud platforms, Spark, Docker, and MLOps tools and best practices

Excellent communication, collaboration, and presentation skills with meticulous attention to detail

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 $175,000 and $308,500, 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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