As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
... end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust estimation ...
... end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust estimation ...
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust ...
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Senior Staff Data Scientist - Bayesian Experimentation & Causal Inference
New York, NY · On-site
$249K - $312K/yr
Own causal inference and experimentation standards across Headway. Define the canonical approaches, guardrails, documentation, and review mechanisms for experiments and quasi-experiments, including ...
Senior Staff Data Scientist - Bayesian Experimentation & Causal Inference
New York, NY · On-site
$249K - $312K/yr
Own causal inference and experimentation standards across Headway. Define the canonical approaches, guardrails, documentation, and review mechanisms for experiments and quasi-experiments, including ...
Applied AI/ML & Causal Inference - Senior Associate
Jersey City, NJ · On-site
$128K - $195K/yr
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust ...
Applied AI/ML & Causal Inference - Senior Associate
Jersey City, NJ · On-site
$128K - $195K/yr
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust ...
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust ...
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust ...
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust ...
Design, build, and deploy end-to-end ML and causal inference solutions: uplift and heterogeneous treatment effect models, observational causal studies (DiD, IV, RDD, synthetic controls, doubly robust ...
Data Scientist
Manhattan, NY · On-site
Advanced Statistical & Causal Inference: Apply deep knowledge of experimental design, regression, classification, causal inference (difference-in-differences, propensity scores, instrumental ...
Data Scientist
Manhattan, NY · On-site
Advanced Statistical & Causal Inference: Apply deep knowledge of experimental design, regression, classification, causal inference (difference-in-differences, propensity scores, instrumental ...
Senior Data Scientist, Causal Science
Manhattan, NY · On-site
$124K - $186K/yr
You will raise the bar for the team by anticipating experimentation risks and mentoring junior analysts in the nuances of causal inference. Why This Role Matters Validating the Gateway: You ensure ...
Senior Data Scientist, Causal Science
Manhattan, NY · On-site
$124K - $186K/yr
You will raise the bar for the team by anticipating experimentation risks and mentoring junior analysts in the nuances of causal inference. Why This Role Matters Validating the Gateway: You ensure ...
Apply causal inference methods where experimentation isn't feasible * Develop models and analyses that inform pricing, segmentation, and revenue optimization * Design, run, and analyze A/B ...
Apply causal inference methods where experimentation isn't feasible * Develop models and analyses that inform pricing, segmentation, and revenue optimization * Design, run, and analyze A/B ...
Senior Data Scientist, Causal Science
Manhattan, NY · On-site +1
$124K - $186K/yr
You will raise the bar for the team by anticipating experimentation risks and mentoring junior analysts in the nuances of causal inference. Why This Role Matters Validating the Gateway: You ensure ...
Senior Data Scientist, Causal Science
Manhattan, NY · On-site +1
$124K - $186K/yr
You will raise the bar for the team by anticipating experimentation risks and mentoring junior analysts in the nuances of causal inference. Why This Role Matters Validating the Gateway: You ensure ...
Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact. * Partner with engineering to ...
Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact. * Partner with engineering to ...
Senior Staff Data Scientist
New York, NY · On-site
Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact. * Partner with engineering to ...
Senior Staff Data Scientist
New York, NY · On-site
Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact. * Partner with engineering to ...
Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact. * Partner with engineering to ...
Quick apply
Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact. * Partner with engineering to ...
Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact. * Partner with engineering to ...
Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact. * Partner with engineering to ...
Apply modeling, advanced analytics, experimentation, and causal inference techniques (e.g., A/B testing, difference-in-differences, synthetic control, quasi-experimental methods) to drive decision ...
Apply modeling, advanced analytics, experimentation, and causal inference techniques (e.g., A/B testing, difference-in-differences, synthetic control, quasi-experimental methods) to drive decision ...
Apply modeling, advanced analytics, experimentation, and causal inference techniques (e.g., A/B testing, difference-in-differences, synthetic control, quasi-experimental methods) to drive decision ...
Apply modeling, advanced analytics, experimentation, and causal inference techniques (e.g., A/B testing, difference-in-differences, synthetic control, quasi-experimental methods) to drive decision ...
Causal Inference information
What are the key skills and qualifications needed to thrive in the Causal Inference position, and why are they important?
Success in a Causal Inference role requires strong statistical knowledge, expertise in experimental and quasi-experimental methodologies, and advanced proficiency in programming languages like R or Python, typically acquired with an advanced degree in statistics, economics, data science, or a related field. Familiarity with specialized statistical software (such as Stata, SAS, or causal inference packages in R/Python), as well as experience with large datasets and machine learning tools, is highly valued. Excellent problem-solving abilities, clear communication, and collaboration skills are essential soft skills for effectively conveying complex findings to diverse teams. These competencies are critical to producing reliable insights that guide evidence-based decision-making in business, healthcare, or policy settings.
What are some common challenges faced in a Causal Inference position?
Professionals in Causal Inference often encounter challenges such as dealing with confounding factors, addressing selection bias, and ensuring the validity of assumptions behind statistical models. They must carefully design experiments or leverage observational data while staying vigilant about potential data quality issues and model limitations. Collaboration with subject matter experts, data engineers, and business stakeholders is common to ensure accurate contextualization of results. Overcoming these challenges requires a mix of technical acumen and strong communication skills to translate complex analyses into actionable recommendations.
What is a Causal Inference job?
A Causal Inference job involves using statistical and computational methods to determine cause-and-effect relationships from data. Professionals in this field work with observational and experimental data to identify causal impacts, often in domains like economics, healthcare, social sciences, and technology. They apply techniques such as propensity score matching, instrumental variables, and difference-in-differences to ensure rigorous analysis. These roles are commonly found in academia, policy research, and data science teams within tech and finance companies. Strong skills in statistics, programming (e.g., Python, R), and experimental design are typically required.
What are the most commonly searched types of Causal Inference jobs in New York? The most popular types of Causal Inference jobs in New York are:
What are popular job titles related to Causal Inference jobs in New York? For Causal Inference jobs in New York, the most frequently searched job titles are:
What job categories do people searching Causal Inference jobs in New York look for? The top searched job categories for Causal Inference jobs in New York are:
What cities in New York are hiring for Causal Inference jobs? Cities in New York with the most Causal Inference job openings:
Other
Medical, Dental, Vision, Retirement, PTO
Re-posted 2 days ago
Lyft rating
7.4
Based on 32 frontline employees who took The Breakroom Quiz
2nd of 9 rated taxi private hire
Job description
At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
The Growth Products team drives rider and driver acquisition to scale the business and balance the marketplace. We specialize in incentive and messaging targeting, budget optimization, and paid media measurement, and move rapidly to test new ideas and products.
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels.
Responsibilities:- Deliver results across the entire lifecycle of data science solutions for Growth: from defining the problem with cross-functional stakeholders to deploying production models that address key business problems.
- Own complex domains and develop long-term roadmaps to maximize business impact.
- Build statistical pipelines, write production code, and design/analyze experiments.
- Participate in the science on-call rotation to ensure automated campaigns operate successfully.
- Advanced degree in statistics, economics, mathematics, or equivalent industry experience.
- 4+ years of industry experience in causal inference or data science.
- Proven ability to apply statistics to unstructured problems and deliver measurable results.
- Deep technical expertise in causal inference and tackling challenging measurement problems.
- Expertise in marketing mix modeling is highly preferred.
- Expertise in SQL and experience with large-scale data platforms.
- Proficiency in Python and working within production coding environments.
- Great medical, dental, and vision insurance options with additional programs available when enrolled
- Mental health benefits
- Family building benefits
- Child care and pet benefits
- 401(k) plan with company match to help save for your future
- In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
- 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
- Subsidized commuter benefits
- Monthly Lyft credits and complimentary Lyft Pink membership
Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.
Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule - Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid
The expected base pay range for this position in the New York City area is $148,000 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.
About Lyft
Sourced by ZipRecruiter
At Lyft, our mission is to improve people's lives with the world's best transportation. To do this, we start with our own community by creating an open, inclusive, and diverse organization.
Industry
Ground public transportation
Company size
5,001 - 10,000 Employees
Headquarters location
San Francisco, CA, US
Year founded
2012