... causal estimate or specifying a generalized linear ... model as they are debugging a Spark job. You'll work closely with the client's data science team to ...
... causal estimate or specifying a generalized linear ... model as they are debugging a Spark job. You'll work closely with the client's data science team to ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
... causal inference models, automated explainability frameworks, and variance bridging methodologies that translate LTPF's forecasts and plans into actionable business intelligence. - Your work will ...
... causal inference models, automated explainability frameworks, and variance bridging methodologies that translate LTPF's forecasts and plans into actionable business intelligence. - Your work will ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Senior Applied Scientist
Manhattan, NY · On-site
Our work spans graph-based entity resolution, temporal and causal modeling, data valuation systems, and retrieval-augmented applications that operate over massive, heterogeneous data assets. We are ...
Senior Applied Scientist
Manhattan, NY · On-site
Our work spans graph-based entity resolution, temporal and causal modeling, data valuation systems, and retrieval-augmented applications that operate over massive, heterogeneous data assets. We are ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
The Core Engineering-Associate, Quantitative Strategist, Core Planning and Analysis Strats
New York, NY · On-site
Causal model selection and identification, treatment-effect estimation, instrumental variables, and counterfactual / what-if analysis. * Production Cloud Deployment: Implementation of mathematical ...
The Core Engineering-Associate, Quantitative Strategist, Core Planning and Analysis Strats
New York, NY · On-site
Causal model selection and identification, treatment-effect estimation, instrumental variables, and counterfactual / what-if analysis. * Production Cloud Deployment: Implementation of mathematical ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business * Develop and productionize causal machine learning solutions ...
Our thesis is that scaling on physics yields a model capable of understanding the causal structure to predict and alter the future. Your mission is to ensure the model evolves towards this thesis ...
Our thesis is that scaling on physics yields a model capable of understanding the causal structure to predict and alter the future. Your mission is to ensure the model evolves towards this thesis ...
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 ...
Member of Technical Staff - Mechanistic Interpretability
San Francisco, CA · On-site
$300K - $500K/yr
Turn representations, features, circuits, and causal model behaviors into intrinsic rewards for reinforcement learning. * Compare interpretability-derived rewards against human feedback, learned ...
Member of Technical Staff - Mechanistic Interpretability
San Francisco, CA · On-site
$300K - $500K/yr
Turn representations, features, circuits, and causal model behaviors into intrinsic rewards for reinforcement learning. * Compare interpretability-derived rewards against human feedback, learned ...
* Use AI/ML to explore new opportunities at Kohl's, including causal models for promo-calendar decisions, gen-AI copilots for insight discovery, and explainer models for year-over-year performance ...
* Use AI/ML to explore new opportunities at Kohl's, including causal models for promo-calendar decisions, gen-AI copilots for insight discovery, and explainer models for year-over-year performance ...
Causal Model information
See salary details
$16.66 is the 25th percentile. Wages below this are outliers.
$10.10 - $22.14
46% of jobs
The median wage is $24.67 / hr.
$22.14 - $34.18
20% of jobs
$34.18 - $46.22
3% of jobs
$56.54 is the 75th percentile. Wages above this are outliers.
$46.22 - $58.26
7% of jobs
$58.26 - $70.30
0% of jobs
$70.30 - $82.34
7% of jobs
$82.34 - $94.38
13% of jobs
$94.38 - $106.42
2% of jobs
$106.42 - $118.47
0% of jobs
$118.47 - $130.51
1% of jobs
$130.51 - $142.55
1% of jobs
$10
$45
$142
How much do causal model jobs pay per hour?
As of Sep 10, 2026, the average hourly pay for causal model in the United States is $45.71, according to ZipRecruiter salary data. Most workers in this role earn between $14.90 and $72.12 per hour, depending on experience, location, and employer.
What is a causal model?
Causal models are analytical frameworks that help identify and represent cause-and-effect relationships between variables. They are used to understand how changes in one factor directly influence another, often using diagrams or mathematical equations. Causal models are essential in fields like statistics, economics, and data science for making predictions, guiding interventions, and informing decision-making. Unlike correlation-based approaches, causal models aim to uncover the true mechanisms driving observed outcomes.
What are the key skills and qualifications needed to thrive as a causal modeler, and why are they important?
To thrive as a Causal Modeler, you need strong quantitative skills, expertise in statistical methods, and a background in fields such as statistics, data science, or economics, often supported by an advanced degree. Proficiency with tools like R, Python, causal inference libraries (e.g., DoWhy, CausalImpact), and statistical software is typically required. Critical thinking, problem-solving, and clear communication are essential soft skills for interpreting data and explaining findings to stakeholders. These skills are crucial for accurately determining cause-and-effect relationships and informing data-driven decision-making in complex environments.
What are some common challenges faced by professionals working with causal models in data science roles?
One common challenge in roles focused on causal modeling is distinguishing correlation from causation, which requires rigorous experimental design and statistical analysis. Data limitations, such as missing variables or confounding factors, can complicate the identification of true causal relationships. Additionally, communicating complex causal findings to non-technical stakeholders often requires strong data storytelling skills. Collaborative work with domain experts is essential to ensure models are both mathematically sound and contextually relevant.
What is the difference between Causal Model vs Data Analyst?
| Aspect | Causal Model | Data Analyst |
|---|---|---|
| Required Credentials | Statistical or data science degrees, certifications in causal inference | Statistics, data analysis, or related degrees |
| Work Environment | Research-focused, often in academia or specialized analytics teams | Business environments, corporate analytics teams |
| Industry Usage | Used in research, policy analysis, and advanced analytics | Business decision-making, reporting, and data visualization |
| Search & Comparison Intent | Understanding causal relationships, modeling techniques | Data interpretation, reporting, and insights |
The main difference is that Causal Models focus on identifying cause-and-effect relationships using specialized statistical techniques, often requiring advanced training. Data Analysts primarily interpret data to generate reports and insights, working across various industries. While both roles involve data, Causal Models are more research-oriented, whereas Data Analysts support business decisions through data interpretation.
What other helpful pages are available for Causal Model?
Other pages related to Causal Model:
Lead Data Scientist, Predictive Modeling & Causal Inference
Remote
OneSix - External
Full-time
Medical, Dental, Vision, PTO
Posted 21 days ago
Job description
About OneSix
OneSix is a leading data and artificial intelligence (AI) consultancy that helps businesses build the strategy, technology, and teams they need to scale growth and efficiency. Its team of skilled Data Engineers, Data Scientists, Machine Learning (ML) Experts, and AI Engineers seamlessly integrate with client teams to solve their most challenging business problems. Leveraging strategic partnerships with Snowflake, AWS, Matillion, Fivetran, Pyramid Analytics, and more, the company uses modern technology, scalable architectures, and industry best practices. With the recent acquisition of Strong Analytics, an ML and AI consultancy, OneSix is a uniquely powerful business partner to the enterprise, with a talent mix that is nearly impossible to find under one roof.
OneSix is a fast-growing firm with significant career opportunities for motivated professionals who want to help create a unique company. We are committed to fostering an inclusive employee experience that reflects the world we live in today. We're an equal-opportunity employer that welcomes people regardless of backgrounds, experiences, abilities, and perspectives.
Lead Data Scientist
We're looking for a Lead Data Scientist to embed with key clients as a senior technical partner on their data science team. This is a player-coach role at the intersection of rigorous predictive modeling and production engineering: someone who is as comfortable deriving a causal estimate or specifying a generalized linear model as they are debugging a Spark job.
You'll work closely with the client's data science team to shape how the organization understands and predicts user behavior and business outcomes. Success in this role depends as much on the strength of your judgment as your ability to earn trust in a room.
Comfort in consulting work is also a requirement, working with production systems that have grown organically over years, data that isn't always clean, and business stakeholders who need answers on a timeline. You should find that kind of complexity energizing rather than draining.
What You'll Do
What You Bring
Nice to Have
This position offers a base salary in the range of $180,000-$210,000 USD annually, depending on experience and location. Compensation may vary based on factors including geographic location, level of experience, skills, and performance. This salary range reflects base pay only and does not include any additional compensation such as bonuses, equity, or benefits.
OneSix provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, familial status, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
OneSix is a leading data and artificial intelligence (AI) consultancy that helps businesses build the strategy, technology, and teams they need to scale growth and efficiency. Its team of skilled Data Engineers, Data Scientists, Machine Learning (ML) Experts, and AI Engineers seamlessly integrate with client teams to solve their most challenging business problems. Leveraging strategic partnerships with Snowflake, AWS, Matillion, Fivetran, Pyramid Analytics, and more, the company uses modern technology, scalable architectures, and industry best practices. With the recent acquisition of Strong Analytics, an ML and AI consultancy, OneSix is a uniquely powerful business partner to the enterprise, with a talent mix that is nearly impossible to find under one roof.
OneSix is a fast-growing firm with significant career opportunities for motivated professionals who want to help create a unique company. We are committed to fostering an inclusive employee experience that reflects the world we live in today. We're an equal-opportunity employer that welcomes people regardless of backgrounds, experiences, abilities, and perspectives.
Lead Data Scientist
We're looking for a Lead Data Scientist to embed with key clients as a senior technical partner on their data science team. This is a player-coach role at the intersection of rigorous predictive modeling and production engineering: someone who is as comfortable deriving a causal estimate or specifying a generalized linear model as they are debugging a Spark job.
You'll work closely with the client's data science team to shape how the organization understands and predicts user behavior and business outcomes. Success in this role depends as much on the strength of your judgment as your ability to earn trust in a room.
Comfort in consulting work is also a requirement, working with production systems that have grown organically over years, data that isn't always clean, and business stakeholders who need answers on a timeline. You should find that kind of complexity energizing rather than draining.
What You'll Do
- Design, build, and validate predictive models, from GLMs and causal/econometric methods to deep learning-based forecasting, to answer questions about user behavior, retention, and business performance.
- Apply causal inference techniques (quasi-experimental design, uplift modeling, propensity methods, and related econometric tools) to move client stakeholders beyond correlation and toward decisions they can act on with confidence.
- Own the full lifecycle of your models: from exploratory analysis and feature engineering through deployment, monitoring, and retraining in a live production environment.
- Work fluently across the stack, writing production-grade SQL, processing data at scale in Spark, and building and deploying models in Python to get from idea to shipped solution without waiting on a hand-off.
- Partner directly with the client's data science and broader analytics team, translating ambiguous business questions into well-scoped modeling problems and pushing back, respectfully and with evidence, when the data leads somewhere unexpected.
- Communicate technical work clearly to both technical and non-technical stakeholders, building the kind of credibility that earns you a seat at the table on strategic decisions, not just implementation ones.
- Bring engineering discipline to a production environment that is mature but imperfect, improving reliability and maintainability incrementally.
What You Bring
- 7+ years of hands-on experience in predictive analytics, applied statistics, or machine learning, with a track record of taking models from concept into production. (Strong candidates with somewhat less experience but exceptional depth are still encouraged to apply.)
- Deep fluency in predictive modeling techniques spanning generalized linear models, econometric methods, causal inference, and time-series forecasting, including deep learning-based forecasting approaches with the judgment to speak to trade-offs and failure modes from experience, not just theory.
- Strong software engineering fundamentals: you've deployed and maintained models in production, not just prototyped them in a notebook, and you're comfortable owning code quality, testing, and monitoring for the solutions you build.
- Proficiency across the modern data stack (e.g., SQL, Spark, and Python) and the judgment to work effectively in a production environment that's mature but occasionally messy, without losing momentum
- Excellent communication and interpersonal skills. You'll be working alongside smart technical leaders, and you need to be able to build trust quickly, hold your ground when you have good reason to, and adapt when you don't. Keen client/stakeholder capability is important.
- A graduate degree (M.S. or Ph.D.) in a quantitative or behavioral field ( statistics, economics, computer science, cognitive science, or a related discipline) or equivalent demonstrated experience.
- Based in the US or Canada.
Nice to Have
- Experience modeling user behavior as it relates to downstream outcomes like churn, lifetime value, engagement, or propensity to convert are all directly relevant.
- A Ph.D. in cognitive science, behavioral economics, or a similarly human-behavior-oriented quantitative field.
- Prior consulting or professional services experience, particularly in client-facing technical roles.
- Competitive compensation
- Company-paid medical, vision, dental, and wellness benefits for employees
- Company-provided home office equipment
- Flexible vacation and sick days
- Team-oriented and supportive working environment
- Company-sponsored events and swag
This position offers a base salary in the range of $180,000-$210,000 USD annually, depending on experience and location. Compensation may vary based on factors including geographic location, level of experience, skills, and performance. This salary range reflects base pay only and does not include any additional compensation such as bonuses, equity, or benefits.
OneSix provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, familial status, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.