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Causal Inference Jobs in Columbia, MD (NOW HIRING)

Axle

Director of Data Solutions

Rockville, MD · On-site

... 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 ...

BullFrogAI

Principal Data Scientist

Gaithersburg, MD · On-site

$175K - $215K/yr

Causal inference experience -- a significant differentiator for this role * NLP/LLM expertise applied to life sciences and healthcare text * Strong foundation in data engineering and databases

Carnegie Mellon University

Data Scientist

Arlington, VA · On-site

Recommendation systems, Time-series forecasting (Prophet, NeuralProphet, Chronos, Lag-Llama, etc.), NLP / LLMs (fine-tuning, RAG, evaluation, prompt engineering), Causal inference / uplift modeling ...

Cmu

Senior Data Scientist

Arlington, VA · On-site

Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost, CatBoost, PyTorch,JAX, TensorFlow) * LLMs / agentic workflows (LangChain/LlamaIndex/Haystack)

Cmu

Associate Data Scientist

Arlington, VA

$67K - $68K/yr

Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost, CatBoost, PyTorch,JAX, TensorFlow) * LLMs / agentic workflows (LangChain/LlamaIndex/Haystack)

Cisive

Senior Engineer, Data and AI

Annapolis, MD · On-site

$120 - $180/hr

Solid grounding in statistical methods and experimental design (e.g., hypothesis testing, regression, causal inference) to validate models and ensure sound decision-making.MLOps/LLMOps: Experience ...

Cmu

Data Scientist

Arlington, VA · On-site

Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost, CatBoost, PyTorch,JAX, TensorFlow) * LLMs / agentic workflows (LangChain/LlamaIndex/Haystack)

Axle

Director of Data Solutions

Rockville, MD · On-site

Build reusable "engines" for RWE execution: cohorting/phenotyping pipelines, reproducible protocol templates, causal inference/target trial tooling patterns, and integration templates for multiple ...

Axle

Director of Data Solutions

Rockville, MD · On-site

Build reusable "engines" for RWE execution: cohorting/phenotyping pipelines, reproducible protocol templates, causal inference/target trial tooling patterns, and integration templates for multiple ...

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All JobsCausal Inference JobsCausal Inference Jobs in Columbia, MD

Causal Inference information

See Columbia, MD salary details

$54.6K

$98.5K

$134.5K

How much do causal inference jobs pay per year?

As of Aug 21, 2026, the average yearly pay for causal inference in Columbia, MD is $98,477.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,300.00 and $107,700.00 per year, depending on experience, location, and employer.

What is a causal inference?

A Causal Inference job involves using statistical and computational methods to determine cause-and-effect relationships from data. Professionals in this field work with observational and experimental data to identify causal impacts, often in domains like economics, healthcare, social sciences, and technology. They apply techniques such as propensity score matching, instrumental variables, and difference-in-differences to ensure rigorous analysis. These roles are commonly found in academia, policy research, and data science teams within tech and finance companies. Strong skills in statistics, programming (e.g., Python, R), and experimental design are typically required.

What skills and qualifications are needed for a causal inference position?

Success in a Causal Inference role requires strong statistical knowledge, expertise in experimental and quasi-experimental methodologies, and advanced proficiency in programming languages like R or Python, typically acquired with an advanced degree in statistics, economics, data science, or a related field. Familiarity with specialized statistical software (such as Stata, SAS, or causal inference packages in R/Python), as well as experience with large datasets and machine learning tools, is highly valued. Excellent problem-solving abilities, clear communication, and collaboration skills are essential soft skills for effectively conveying complex findings to diverse teams. These competencies are critical to producing reliable insights that guide evidence-based decision-making in business, healthcare, or policy settings.

What are common challenges faced in a causal inference position?

Professionals in Causal Inference often encounter challenges such as dealing with confounding factors, addressing selection bias, and ensuring the validity of assumptions behind statistical models. They must carefully design experiments or leverage observational data while staying vigilant about potential data quality issues and model limitations. Collaboration with subject matter experts, data engineers, and business stakeholders is common to ensure accurate contextualization of results. Overcoming these challenges requires a mix of technical acumen and strong communication skills to translate complex analyses into actionable recommendations.

What cities near Columbia, MD are hiring for Causal Inference jobs?

Cities near Columbia, MD with the most Causal Inference job openings:

Causal Inference jobs near you
Infographic showing various Causal Inference job openings in Columbia, MD as of August 2026, with employment types broken down into 81% Full Time, 17% Part Time, and 2% Contract. Highlights an 72% Physical, 4% Hybrid, and 24% Remote job distribution, with an average salary of $98,477 per year, or $47.3 per hour.

Director of Data Solutions

Axle

Rockville, MD • On-site

Full-time

Re-posted 19 days ago

Job description

Job Summary:
Axle is a bioscience and information technology company specializing in translational research, biomedical informatics, and data science applications. The Director of Data Solutions will lead technical delivery for data platforms and AI/ML solutions, setting strategy and overseeing cross-functional teams to build enterprise-grade capabilities and ensure high-quality data products.
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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