Applied Scientist
$175K - $308K/yr
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 ...
Applied Scientist
$175K - $308K/yr
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 ...
Applied Scientist
$175K - $308K/yr
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 ...
Applied Scientist
$175K - $308K/yr
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 ...
Staff Data Scientist- Pricing Science
Austin, TX · On-site +1
Strong grounding in causal inference and experimental design, including the ability to distinguish ... Hiring Manager Interview: Conversation with the Director of Finance and Business Intelligence ...
Staff Data Scientist- Pricing Science
Austin, TX · On-site +1
Strong grounding in causal inference and experimental design, including the ability to distinguish ... Hiring Manager Interview: Conversation with the Director of Finance and Business Intelligence ...
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Lancaster, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Lancaster, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Addison, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Addison, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Mesquite, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Mesquite, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Irving, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Irving, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Highlands, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Highlands, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Dallas, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Data Scientist
Dallas, TX · On-site
Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Excellent analytical, communication, and stakeholder management skills. Roles & Responsibilities
Manager Causal Inference information
What does a manager causal inference do?
A Manager Causal Inference leads teams that analyze data to determine cause-and-effect relationships, often in business, healthcare, or technology settings. They design experiments or use statistical methods to understand how different factors influence outcomes, helping organizations make data-driven decisions. This role typically involves managing projects, overseeing analysts or data scientists, and communicating findings to stakeholders. Strong expertise in statistics, data analysis, and leadership is essential for success in this position.
What are the key skills and qualifications needed to thrive as a manager causal inference?
To thrive as a Manager of Causal Inference, you need a deep understanding of statistics, econometrics, and experimental design, typically supported by an advanced degree in a quantitative field. Proficiency with data analysis tools such as R, Python, SQL, and specialized causal inference libraries, along with experience using data visualization and project management platforms, is crucial. Strong leadership, communication, and critical thinking skills help you effectively guide teams and translate complex findings to stakeholders. These skills ensure rigorous, actionable insights that drive strategic decision-making and organizational impact.
How does a manager causal inference typically collaborate with cross-functional teams to drive impactful business insights?
Managers of Causal Inference frequently work alongside data scientists, product managers, engineers, and business leaders to design and execute experiments that reveal the true impact of business decisions. They translate complex statistical findings into actionable recommendations, ensuring stakeholders understand both the methodology and implications. Regularly, they lead discussions on experiment design, data collection strategies, and result interpretation, fostering a culture of evidence-based decision-making across the organization.
What are the most commonly searched types of Causal Inference jobs in Texas?
The most popular types of Causal Inference jobs in Texas are:
What are popular job titles related to Manager Causal Inference jobs in Texas?
For Manager Causal Inference jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Manager Causal Inference jobs in Texas look for?
The top searched job categories for Manager Causal Inference jobs in Texas are:
What cities in Texas are hiring for Manager Causal Inference jobs?
Cities in Texas with the most Manager Causal Inference job openings:
$175K - $308K/yr
Full-time
Medical, Dental, Retirement
Re-posted 14 days ago
Apple rating
8.0
Based on 677 frontline employees who took The Breakroom Quiz
7th of 30 rated technology retailers
Job description
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 brand 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.
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 brand 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
Sourced by ZipRecruiter
Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.
Industry
Computer and electronic product manufacturing
Company size
10,000+ Employees
Headquarters location
Cupertino, CA, US
Year founded
1976