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Causal Inference Machine Learning Postdoctoral Jobs in Washington, DC

Somatus, Inc.

Machine Learning Engineer

Mclean, VA · On-site

$105K - $115K/yr

As a Machine Learning Engineer at Somatus, you will work collaboratively with our data and ... Perform exploratory data analysis, statistical modeling, causal inference, and other advanced ...

Jobtailor

Director of Machine Learning

Washington, DC · On-site

$180 - $260/hr

Manage the complete data lifecycle from acquisition through model inference and postprocessing ... Machine Learning Engineering Leadership * Production Deployment Experience * Python Proficiency

2U

Data Scientist II

Washington, DC · On-site +1

Experimentation and causal inference Own A/B tests end-to-end, from design and power analysis ... Applied machine learning Use standard ML techniques (classification, regression, clustering) where ...

3 Media Web Solutions, Inc

Data Scientist II

Arlington, VA · On-site

$107.30 - $124.20/hr

Experimentation and causal inference - Own A/B tests end-to-end, from design and power analysis ... Applied machine learning - Use standard ML techniques (classification, regression, clustering ...

Umd

Senior Faculty Specialist

College Park, MD · On-site

Expertise in data analysis and machine learning, with experience applying these techniques in an educational context preferred. *Familiarity with experimental design and causal inference ...

GEICO

Senior Applied Research Scientist

Bethesda, MD · On-site

$115K - $230K/yr

... expertise in machine learning and artificial intelligence. As the AI Research team at GEICO ... Experimentation & Causal Inference: Design online experiments and quasi-experimental analyses ...

Geico

Senior Applied Research Scientist

Bethesda, MD · On-site

$115K - $230K/yr

... expertise in machine learning and artificial intelligence. As the AI Research team at GEICO ... Experimentation & Causal Inference: Design online experiments and quasi-experimental analyses ...

Carnegie Mellon University

Data Scientist

Arlington, VA · On-site

They are seeking a Data Scientist to leverage advanced statistics, data analytics, machine learning ... Causal inference / uplift modeling / synthetic controls, Modern ML frameworks: LightGBM/XGBoost ...

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Washington, DC

Causal Inference Machine Learning Postdoctoral information

See Washington, DC salary details

$40.2K

$61.4K

$69.1K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Aug 20, 2026, the average yearly pay for causal inference machine learning postdoctoral in Washington, DC is $61,413.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,600.00 and $64,000.00 per year, depending on experience, location, and employer.

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?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

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.

Is it difficult to get a causal inference machine learning postdoctoral position?

Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Washington, DC?

For Causal Inference Machine Learning Postdoctoral jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Washington, DC look for?

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Washington, DC are:

Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Washington, DC as of June 2026, with employment types broken down into 74% Full Time, 24% Part Time, and 2% Contract. Highlights an 91% Physical, 1% Hybrid, and 8% Remote job distribution, with an average salary of $61,413 per year, or $29.5 per hour.

Senior Data Scientist - Machine Learning & AI

Team Velocity

Herndon, VA • Remote

$160K - $190K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted yesterday

New

Job description

Senior Data Scientist – Machine Learning & AI
Remote | Full-Time | $160,000–$190,000

Team Velocity is seeking a Senior Data Scientist to develop and deploy machine learning, predictive analytics, and AI solutions that improve customer engagement, marketing performance, operational efficiency, and business intelligence.

This is a hands-on role for an experienced data scientist who can take models from data exploration and development through production deployment, monitoring, and optimization. You will partner with Product, Data Engineering, Software Engineering, Analytics, and business leadership to deliver measurable business impact.

This is a full-time remote position. Candidates must reside in the Continental U.S. and be able to support an 8:30 AM–5:30 PM ET business hours. Eastern and Central Time Zones highly preferred.

KEY RESPONSIBILITIES

  • Design, build, evaluate, and deploy production machine learning models.
  • Develop predictive models for churn, propensity, lead scoring, customer lifetime value, recommendations, forecasting, personalization, and marketing attribution.
  • Perform statistical analysis, hypothesis testing, A/B testing, causal inference, and time-series analysis.
  • Build feature engineering, model training, and inference pipelines.
  • Deploy and monitor ML models, including model performance, drift detection, and retraining.
  • Apply Generative AI, LLMs, RAG, and vector databases to business and customer applications.
  • Partner with Product, Engineering, Analytics, and leadership to translate business problems into scalable data science solutions.
  • Mentor junior data scientists and establish best practices for model development, documentation, and code quality.

REQUIREMENTS

  • Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field; Master's or PhD preferred.
  • 5+ years | Python + SQL | production ML | predictive modeling | model deployment | MLOps | cloud | measurable business impact
  • Proven ability to deliver measurable business impact through data science and machine learning.
  • Strong communication, analytical, and business problem-solving skills.
  • Expert Python and SQL skills.

TECHNICAL EXPERIENCE

  • Machine Learning: XGBoost, LightGBM, Random Forest, Neural Networks, Deep Learning
  • Statistics: Regression, Bayesian methods, hypothesis testing, experimental design, causal inference, time series
  • Data & Cloud: Snowflake, dbt, Spark, Airflow, GCP preferred; AWS or Azure considered
  • MLOps: MLflow, Kubeflow, Vertex AI, feature stores, CI/CD, model monitoring
  • AI/LLMs: OpenAI, Gemini, Claude, LangChain, LangGraph, RAG, embeddings, vector databases
  • Experience with data quality and observability tools such as Great Expectations or Monte Carlo is a plus.

*You do not need experience with every technology listed above. Strong production machine learning experience is the priority.

Preferred Experience

  • Large-scale customer or behavioral data
  • Marketing analytics, personalization, or customer intelligence
  • SaaS, automotive, retail, advertising, or marketing technology
  • Real-time inference or streaming data
  • Production Generative AI applications

COMPENSATION & BENEFITS
The expected salary range is $160,000–$190,000 annually, based on experience, skills, and qualifications. Benefits include medical, dental, vision, 401(k) matching, unlimited paid leave, wellness programs, and more.

NEXT STEPS
If you meet the requirements, and are interested in applying for this role, please complete the online employment application and be sure to upload a current resume and current contact information.

About Team Velocity
Team Velocity is a full-service marketing and technology company serving automotive manufacturers and dealerships nationwide. Our proprietary Apollo® technology platform uses data, predictive analytics, and AI to predict consumer behavior, personalize marketing, and help dealerships increase sales and service revenue.

Join us in applying data science, machine learning, and AI to real-world business problems at scale.

Apply