Lead complex Machine Learning, AI, and causal inference initiatives across Lyft Business products (Business Travel, Lyft Pass, Concierge) in ambiguous, high-impact problem spaces. * End-to-End ...
Lead complex Machine Learning, AI, and causal inference initiatives across Lyft Business products (Business Travel, Lyft Pass, Concierge) in ambiguous, high-impact problem spaces. * End-to-End ...
Lead complex Machine Learning, AI, and causal inference initiatives across Lyft Business products (Business Travel, Lyft Pass, Concierge) in ambiguous, high-impact problem spaces. * End-to-End ...
Lead complex Machine Learning, AI, and causal inference initiatives across Lyft Business products (Business Travel, Lyft Pass, Concierge) in ambiguous, high-impact problem spaces. * End-to-End ...
... Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to ...
... Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to ...
... Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to ...
... Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to ...
Senior Data Scientist - Network Value
New York, NY · On-site
$190K - $262K/yr
Experience with causal inference, machine learning, and Python Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven ...
Senior Data Scientist - Network Value
New York, NY · On-site
$190K - $262K/yr
Experience with causal inference, machine learning, and Python Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven ...
Member of Technical Staff, Causality
New York, NY · On-site
$100K - $300K/yr
Design and implement novel causal inference methods for treatment effect modeling. * Translate machine learning papers into production-ready code. * Build robust model evaluation frameworks.
New
Member of Technical Staff, Causality
New York, NY · On-site
$100K - $300K/yr
Design and implement novel causal inference methods for treatment effect modeling. * Translate machine learning papers into production-ready code. * Build robust model evaluation frameworks.
New
... machine learning, generative AI, causal inference, and advertising technology at Internet scale.
... machine learning, generative AI, causal inference, and advertising technology at Internet scale.
... machine learning, generative AI, causal inference, and advertising technology at Internet scale.
... machine learning, generative AI, causal inference, and advertising technology at Internet scale.
(USA) Principal, Data Scientist
Hoboken, NJ · On-site
$132K - $264K/yr
This team leads advancements in generative AI, agentic intelligence, machine learning, measurement, and causal inference to redefine retail experiences, optimize operations, and develop new business ...
(USA) Principal, Data Scientist
Hoboken, NJ · On-site
$132K - $264K/yr
This team leads advancements in generative AI, agentic intelligence, machine learning, measurement, and causal inference to redefine retail experiences, optimize operations, and develop new business ...
Quantitative Researcher, Central Execution Desk
New York, NY · On-site
$120K - $200K/yr
... machine learning. Our ongoing investment in top engineering talent and technology ensures our ... Working on causal inference methods * Developing optimization models for various execution ...
Quantitative Researcher, Central Execution Desk
New York, NY · On-site
$120K - $200K/yr
... machine learning. Our ongoing investment in top engineering talent and technology ensures our ... Working on causal inference methods * Developing optimization models for various execution ...
Quantitative Researcher, Central Execution Desk
New York, NY · On-site
$120K - $200K/yr
... machine learning. Our ongoing investment in top engineering talent and technology ensures our ... Working on causal inference methods * Developing optimization models for various execution ...
Quantitative Researcher, Central Execution Desk
New York, NY · On-site
$120K - $200K/yr
... machine learning. Our ongoing investment in top engineering talent and technology ensures our ... Working on causal inference methods * Developing optimization models for various execution ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
... inference, hypothesis testing, causal / incrementality measurement). • Build scalable tools that ... Software Engineering, Machine Learning, and/or Data Science. • Strong general software ...
... inference, hypothesis testing, causal / incrementality measurement). • Build scalable tools that ... Software Engineering, Machine Learning, and/or Data Science. • Strong general software ...
Research Engineer, Machine Learning
New York, NY · On-site
$120K - $180K/yr
Causal inference * Program synthesis and analysis * ML Ops and systems engineering These areas are ... machine learning, computational neuroscience, cognitive science, physics, mathematics.
Research Engineer, Machine Learning
New York, NY · On-site
$120K - $180K/yr
Causal inference * Program synthesis and analysis * ML Ops and systems engineering These areas are ... machine learning, computational neuroscience, cognitive science, physics, mathematics.
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Research Engineer, Platform
Manhattan, NY · On-site
... causal inference, reasoning under uncertainty, program synthesis, and foundations of machine learning. • Build Basis core technology modules producing fundamental algorithmic components that ...
Research Engineer, Platform
Manhattan, NY · On-site
... causal inference, reasoning under uncertainty, program synthesis, and foundations of machine learning. • Build Basis core technology modules producing fundamental algorithmic components that ...
Senior Data Scientist
New York, NY · On-site
Design, build, and deploy machine learning models for ad targeting, ranking, and bidding ... Strong background in incrementality measurement, experimentation, A/B testing, causal inference ...
Senior Data Scientist
New York, NY · On-site
Design, build, and deploy machine learning models for ad targeting, ranking, and bidding ... Strong background in incrementality measurement, experimentation, A/B testing, causal inference ...
The role involves designing causal inference models, creating algorithms for credit pricing, and leading ML infrastructure projects. Responsibilities : • Solve the "Why," not just the "What": You ...
The role involves designing causal inference models, creating algorithms for credit pricing, and leading ML infrastructure projects. Responsibilities : • Solve the "Why," not just the "What": You ...
Causal Inference Machine Learning Postdoctoral information
What is a causal inference machine learning postdoctoral researcher?
A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.
What are the key skills and qualifications needed to thrive as a causal inference machine learning postdoctoral researcher?
To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.
What are some common challenges faced by causal inference machine learning postdoctoral researchers when integrating causal models with real-world data?
Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.
What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?
| Aspect | Causal Inference Machine Learning Postdoctoral | Data Scientist |
|---|---|---|
| Required Credentials | PhD in statistics, machine learning, or related field | Bachelor's or Master's in data science, computer science, or related field |
| Work Environment | Academic research, research labs, universities | Corporate, tech companies, startups |
| Industry Usage | Research, academia, specialized industry projects | Business analytics, product development, data-driven decision making |
| Common Search/Comparison | Yes | Yes |
The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.
What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in New York?
For Causal Inference Machine Learning Postdoctoral jobs in New York, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in New York look for?
The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in New York are:
What cities in New York are hiring for Causal Inference Machine Learning Postdoctoral jobs?
Cities in New York with the most Causal Inference Machine Learning Postdoctoral job openings:
Full-time
Medical, Dental, Vision, Retirement, PTO
Re-posted 8 days ago
Lyft rating
7.6
Based on 33 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.
Data Science is at the heart of Lyft's products and decision-making. Data Scientists at Lyft operate in dynamic environments, moving quickly to build the world's best transportation solutions.We tackle a wide range of challenges - from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products.
Lyft Business builds products that help organizations move the people who matter most - employees, customers, patients, and guests - easily and efficiently. Our offerings include Business Travel, Lyft Pass, and Concierge (for healthcare and non-healthcare rides), enabling companies to manage transportation at scale through APIs, integrations (e.g., Concur, Expensify), and dedicated tools. These platforms power high-impact B2B use cases across corporate travel, healthcare access, customer experience, and community programs.
We are seeking a Senior Data Scientist to lead technical initiatives across the entire Lyft Business product suite. In this role, you will shape the technical vision, define algorithmic roadmaps, and drive execution for data science projects that accelerate growth, improve operational efficiency, and deliver measurable value to our enterprise partners. You'll collaborate closely with Product, Engineering, Design, and Go-to-Market teams to build production ML models, experimentation frameworks, and advanced analytics that inform strategy and power product innovation.
This is a high-visibility, high-impact role with direct influence on Lyft's enterprise offerings. The ideal candidate will bring deep expertise in algorithm development, machine learning, causal inference, and experimentation, alongside strong business acumen in B2B contexts and a proven track record of technical leadership in fast-paced, cross-functional environments.
Responsibilities- Technical Leadership: Lead complex Machine Learning, AI, and causal inference initiatives across Lyft Business products (Business Travel, Lyft Pass, Concierge) in ambiguous, high-impact problem spaces.
- End-to-End Modelling: Own the complete lifecycle of algorithmic solutions-from problem formulation, data exploration, and feature engineering to deployment, monitoring, and iteration.
- Production Deployment: Partner closely with Engineering to build and scale production-grade ML systems, real-time inference services, batch pipelines, and feature stores.
- Experimentation & Rigor: Define offline/online metrics, evaluation frameworks, and A/B testing strategies to ensure algorithms are reliable, fair, and aligned with business outcomes.
- System Optimization: Continually improve model performance across latency, accuracy, cost, and reliability using advanced tuning and scientific rigor.
- Algorithmic Innovation: Drive scientific excellence by introducing modern techniques in ML, optimization, reinforcement learning, or graph-based methods to unlock new product capabilities.
- Cross-Functional Influence: Translate complex business challenges into concrete algorithmic solutions in close collaboration with Product, Engineering, Operations, and Science teams.
- Mentorship & Quality Bar: Mentor junior and mid-level scientists, providing technical guidance, conducting modeling critiques, and contributing to Lyft's broader ML standards and tooling.
- Master's or PhD in Machine Learning, Computer Science, Statistics, Optimization, or a related quantitative field (or equivalent applied experience)
- Industry Background: 5+ years of hands-on experience developing, deploying, and maintaining production machine learning models and optimization systems.
- Core Technical Expertise: Deep knowledge of supervised/unsupervised learning, ranking/decisioning systems, probabilistic modeling, and causal inference.
- Technical Stack: Strong proficiency in Python, modern ML frameworks (PyTorch, TensorFlow, scikit-learn), and distributed data systems (Spark, Snowflake, Databricks).
- Production ML Systems: Hands-on experience building end-to-end ML architectures, including online/batch pipelines, feature engineering, and automated monitoring frameworks.
- Experimental Design: Demonstrated track record of designing rigorous experimentation strategies, A/B tests, and offline/online validation methodologies.
- Domain Ownership: Proven ability to independently drive multi-project algorithmic scopes and navigate technical ambiguity from ideation to delivery.Communication & Leadership: Exceptional ability to translate complex technical concepts for non-technical stakeholders, alongside a history of mentoring peers and raising technical bars.
- 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 area is $136,160 - $170,200, 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