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Phd Causal Inference Jobs in Miami, FL (NOW HIRING)

CookUnity

Director, Data Science/ML

Miami, FL · On-site

Develop causal inference methodologies to understand true incrementality of product changes ... PhD or Master's degree in Computer Science, Statistics, Mathematics, or related quantitative field

Opendoor

Applied Scientist- Pricing

Miami, FL · On-site

$156K - $335K/yr

Experience with one or more of the following: causal inference, Bayesian modeling, structural ... Advanced degree (MS or PhD preferred) in statistics, mathematics, economics, operations research ...

All JobsPhd Causal Inference JobsPhd Causal Inference Jobs in Miami, FL

Phd Causal Inference information

See Miami, FL salary details

$38.3K

$117.6K

$170.7K

How much do phd causal inference jobs pay per year?

As of Aug 10, 2026, the average yearly pay for phd causal inference in Miami, FL is $117,574.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,400.00 and $132,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a PhD causal inference researcher?

To thrive as a PhD Causal Inference researcher, you need advanced knowledge of statistics, econometrics, and causal modeling, typically supported by a doctoral degree in a quantitative field. Familiarity with statistical programming languages (such as R or Python), specialized software (like STATA or SAS), and experience with experimental or quasi-experimental methods are essential. Strong analytical thinking, attention to detail, and the ability to communicate complex findings clearly make a candidate stand out. These skills ensure rigorous, credible research that can inform policy, product development, or scientific understanding by accurately identifying causal relationships.

What collaborative opportunities can a PhD specializing in causal inference expect within a multidisciplinary research team?

PhD professionals in Causal Inference frequently collaborate with experts from fields such as epidemiology, economics, computer science, and public health. They often work closely with data scientists, subject matter experts, and statisticians to design studies, interpret complex datasets, and develop robust analytical models. This multidisciplinary environment fosters continuous learning and often leads to co-authorship on research publications, participation in grant writing, and involvement in high-impact policy or product decisions. Effective communication and teamwork skills are essential to translate technical findings for diverse audiences and drive actionable insights.

What is a PhD in causal inference?

A PhD in Causal Inference is an advanced research degree focused on understanding and identifying cause-and-effect relationships using statistical and computational methods. Students in this field learn to design studies, analyze data, and develop new methodologies to answer complex causal questions in areas such as social sciences, medicine, economics, and artificial intelligence. Graduates often work in academia, research institutions, or industries where evidence-based decision-making is essential.
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Phd Causal Inference jobs near you

Senior Data Scientist

Haystack News

Fort Lauderdale, FL • On-site

Full-time

Re-posted 25 days ago

Job description

Haystack News, the number one destination for news on streaming platforms, is looking for a Sr Data Scientist to join our team. Haystack is trusted by over 30 million viewers and is among the fastest-growing TV news companies in the world.
Join our team at Haystack News as a Senior Data Scientist and become a pivotal force in redefining user experiences through cutting-edge algorithm enhancements. In this role, you'll leverage your advanced statistical analysis, modeling, causal inference, experimental design (A/B testing) and data analytics expertise to drive substantial improvements in user engagement and retention, directly impacting our product's success. This is an exceptional opportunity to showcase your robust problem-solving capabilities and to thrive in a collaborative environment, working alongside a team of passionate professionals dedicated to innovation and excellence. Be part of a dynamic workplace where your contributions make a meaningful difference and help shape the future of news consumption.
MINIMUM QUALIFICATIONS
  • PhD or M.S. in Computer Science, Mathematics, Electrical Engineering, Statistics, Economics or Operations Research with 5+ years of professional experience in data science, machine learning or related quantitative field
  • 3+ years of professional experience with large-scale online ranking/recommender systems (for news feeds, shopping, ads, music, etc).
  • Deep expertise in statistical inference and experimental design: hypothesis testing, power/sample size calculations, variance reduction, etc.
  • Proficiency in causal inference methods to measure product impact.
  • Proven ability to translate offline analysis into product decisions and measurable improvements in online metrics.
  • Fluency in the Python analytics stack (pandas, NumPy), statistical modeling (statsmodels or scikit-learn) and machine learning packages such as LightGBM and XGBoost.
  • Strong experience with SQL (e.g. postgres, snowflake, etc).

PREFERRED QUALIFICATIONS:
  • Experience working on consumer-facing products with millions of users.
  • Hands-on experience with orchestration/transformation tools (e.g. dbt and Airflow).
  • Experience with deep learning and being familiar with tools such as PyTorch or TensorFlow.
  • Hands-on development of products/tools incorporating GenAI, LLMs, RAG, and/or Agents.

RESPONSIBILITIES
  • Build statistical and machine learning models to improve content discovery and user engagement.
  • Work closely with ML engineers to translate models and insights into production systems.
  • Have curiosity and apply analytical skills to dive deep into data to find key insights that would impact the business.
  • Apply causal inference methods to understand the impact of potential product changes.
  • Define and build new ML features using text and multimodal embeddings and GenAI.
  • Validate offline learnings with online outcomes through AB testing. Design, execute, and analyze experiments to prove product change attribution.

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