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Causal Inference Machine Learning Postdoctoral Jobs in Maryland

Axle

Director of Data Solutions

Rockville, MD · On-site

... causal inference/target trial tooling patterns, and integration templates for multiple data sources ... learning/inference) and/or sensitive data platforms. • Experience leading simulation/modeling ...

Axle

Director of Data Solutions

Rockville, MD · On-site

... causal inference/target trial tooling patterns, and integration templates for multiple data sources ... learning/inference) and/or sensitive data platforms. • Experience leading simulation/modeling ...

Geico

Director, Machine Learning Engineering

Bethesda, MD · On-site

$150K - $300K/yr

Real-time inference systems * Strong business acumen with the ability to translate strategy into execution * Demonstrated success leading large, complex, cross-functional initiatives Preferred

Block

... causal reasoning, agentic systems, and product intelligence. The goal is not simply to build ... Preference and goal inference * Learning when intervention creates value versus friction Agentic ...

Block

... causal reasoning, agentic systems, and product intelligence. The goal is not simply to build ... Preference and goal inference * Learning when intervention creates value versus friction Agentic ...

Block

... causal reasoning, agentic systems, and product intelligence. The goal is not simply to build ... Preference and goal inference * Learning when intervention creates value versus friction Agentic ...

Block

... causal reasoning, agentic systems, and product intelligence. The goal is not simply to build ... Preference and goal inference * Learning when intervention creates value versus friction Agentic ...

Block

... causal reasoning, agentic systems, and product intelligence. The goal is not simply to build ... Preference and goal inference * Learning when intervention creates value versus friction Agentic ...

Block

... causal reasoning, agentic systems, and product intelligence. The goal is not simply to build ... Preference and goal inference * Learning when intervention creates value versus friction Agentic ...

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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?

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 Maryland?

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

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

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Maryland are:

Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Maryland as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 78% Full Time, 18% Part Time, and 2% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution.

Director of Data Solutions

Rockville, MD • On-site

Axle
See more jobs at Axleexternal

Full-time

Re-posted 14 days ago

Job description

Job Summary:
Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications. The Director of Data Solutions is responsible for leading the technical delivery of data platforms and AI/ML solutions, while establishing an organization-wide modeling and simulation practice to ensure high-quality outcomes.
Responsibilities:
• Define reference architectures and technical standards for data/AI platforms (security, scalability, reliability, cost governance, developer experience).
• Own platform modernization plans and technical debt reduction sequencing.
• Make build/buy/partner decisions and establish patterns that can be reused across programs.
• Lead delivery of repeatable ingestion and transformation pipelines with testing, validation, and change control.
• Own harmonization capabilities (terminology translation, unit normalization, episode building) as production services with documentation and quality dashboards.
• Partner with governance and stakeholders to define “minimum acceptable quality” and publish transparent quality measures.
• Lead delivery of production AI/ML solutions (NLP, CV, predictive models, representation learning) and deploy them with evaluation and monitoring.
• Own GenAI patterns and platforms (RAG, agentic workflows, human-in-the-loop review, traceability, privacy safeguards) as reusable services.
• Establish model lifecycle governance: approvals, audits (as needed), drift monitoring, incident response, and continuous improvement.
• Build reusable “engines” for RWE execution: cohorting/phenotyping pipelines, reproducible protocol templates, causal inference/target trial tooling patterns, and integration templates for multiple data sources.
• Staff and support analysis pods for time-sensitive, high-stakes deliverables with rigorous QC and reproducibility practices.
• Define the modeling/simulation practice charter: scope, service model, standards, compute strategy (HPC/cloud), and hiring/partnering plan.
• Lead simulation/modeling teams directly or via domain SMEs; ensure reproducible workflows and high quality bars.
• Identify and prioritize high-value hybrid ML+simulation opportunities.
• Partner with security/privacy to implement strong access controls, auditability, and (where needed) privacy-preserving approaches.
• Establish operational excellence: release management, observability, on-call/incident processes (as appropriate), and runbooks.
• Hire, grow, and retain a high-performing organization; create clear roles, career paths, and performance expectations.
• Build a culture of “research-grade rigor + production-grade discipline,” emphasizing accountability, documentation, and sustainability.
Qualifications:
Required:
• 6+ years in data science, ML engineering, data platform engineering, applied research engineering, or closely related fields
• 3+ years leading multi-disciplinary teams.
• Demonstrated success delivering production data/AI platforms (not only analyses), including architecture, delivery planning, and operational ownership.
• Strong familiarity with modern data stacks and cloud delivery (distributed compute, ETL/ELT, data quality tooling, MLOps/LLMOps concepts).
• Ability to translate ambiguous stakeholder needs into shipped products and measurable outcomes.
• Strong people leadership: recruiting, coaching, performance management, org design.
• Comfort operating in regulated and high-governance environments (privacy, compliance, access control).
Preferred:
• Healthcare data platform experience, especially interoperability/harmonization at scale (OMOP/FHIR/PCORNet/CDISC) and clinical terminology systems.
• Experience shipping GenAI solutions with governance (PII handling, traceability, human review, evaluation, monitoring).
• Experience with privacy-preserving ML patterns (federated learning/inference) and/or sensitive data platforms.
• Experience leading simulation/modeling initiatives (scientific computing, HPC workflows, domain simulations) and partnering effectively with scientific SMEs.
• Track record of publications, open-source leadership, or scientific impact.
Company:
At Axle, we are driven by the mission to accelerate discovery and enhance organizational outcomes by revolutionizing operations with our innovative solutions. Founded in 2002, the company is headquartered in Rockville, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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