Post Doctoral Medical Fellow - Applied Biostatistics, Clinical Trial Simulation

Description

The global biostatistics and data sciences (gBDS) at Boehringer Ingelheim is seeking a Postdoctoral Research Fellow to help modernize clinical trial simulations. The goal is to use AI and agentic workflows to speed up the setup of simulations: gathering relevant data, drafting data-informed priors and design assumptions, and making scenarios easier to iterate on. Statisticians and clinical teams curate and validate every assumption and remain in charge of the simulation and its conclusions, so that assumptions reflect how trials actually run.

This is an applied research and implementation role at the intersection of data science, AI engineering, and biostatistics. The fellow will not be expected to invent new simulation methodology. Instead, they will design, build, and evaluate AI-assisted workflows that help statisticians and clinical development teams move from question to credible simulation results more efficiently — for example, agents that extract design assumptions from protocols and historical trials, benchmark those assumptions against internal and external data, draft simulation setups for statisticians to review, adjust, and approve, and summarize results for decision-makers.

The work emphasizes transparency, reproducibility, and human oversight: AI prepares and proposes, people curate, validate, and decide, so that AI accelerates the setup without obscuring the assumptions behind it. The ideal candidate is self-driven, works independently and takes initiative, and benefits from scientific guidance and a collaborative team environment.

As an employee of Boehringer Ingelheim, you will actively contribute to the discovery, development and delivery of our products to our patients and customers. Our global presence provides opportunity for all employees to collaborate internationally, offering visibility and opportunity to directly contribute to the companies´ success. We realize that our strength and competitive advantage lie with our people. We support our employees in a number of ways to foster a healthy working environment, meaningful work, mobility, networking and work-life balance. Our competitive compensation and benefit programs reflect Boehringer Ingelheim´s high regard for our employees.

Duties & Responsibilities

You will work with considerable autonomy and take ownership of your projects, with scientific guidance from your supervisor(s) and the broader team. Core responsibilities include:

• Design and build AI-agentic workflows (LLM-based agents, tool-calling pipelines) that support the trial simulation lifecycle end to end: assumption gathering, scenario setup, execution, and reporting.

• Develop approaches to ground simulation assumptions in reality by retrieving, extracting, and reconciling evidence from multimodal sources — internal trial data, operational metrics (enrollment, site performance, dropout, protocol deviations), protocols, publications, trial registries, and real-world data.

• Integrate AI workflows with existing statistical simulation tools and packages in R/Python, so that agents orchestrate validated statistical code rather than replace it.

• Build, test & assess scenario and "what-if" capabilities (site mix, visit schedules, monitoring intensity, enrichment approaches) that let cross-functional teams explore trade-offs quickly and translate results into decision support.

• Evaluate the reliability of AI-assisted workflows: accuracy of extracted assumptions, reproducibility of outputs, failure modes, and agreement with expert-built simulations. Establish human-in-the-loop checkpoints, audit trails, and guardrails appropriate for a regulated environment.

• Partner with biostatisticians to keep outputs statistically sound — operating characteristics (power, type I error, bias), missing data and dropout assumptions, site heterogeneity, and Bayesian borrowing from historical data. Biostats own the methodology; you make it faster to apply, easier to explore, and better grounded in data.

• Carry out broad exploratory and applied data analysis across operational and clinical datasets to surface patterns, data quality issues, and opportunities where AI or automation adds value.

• Create reusable, well-tested, version-controlled, and documented pipelines and tools that clinical development teams can adopt and maintain over time.

• Communicate results clearly to cross-functional stakeholders (biostatistics, clinical operations, clinical development, data science, IT) through demos, presentations, internal whitepapers, manuscripts, and conference abstracts.

Qualifications

• Ph.D. in Data Science, Statistics, Biostatistics, Computer Science, Epidemiology, or a closely related quantitative discipline, awarded prior to the start date. This role is designed for recent graduates who want an applied postdoc in an industry setting, with a structured yet independent environment to support that transition.

• Hands-on experience building applications with large language models and/or agentic frameworks (e.g. LangChain/LangGraph, OpenAI or Anthropic APIs, or similar), including prompt design, retrieval-augmented generation, tool use, and evaluation of model outputs.

• Strong programming skills in Python; working knowledge of R is a plus. Experience with reproducible software practices — Git, unit testing, code review, APIs, and literate reporting such as Quarto or Jupyter — is preferred.

• Working knowledge of applied statistics — regression, mixed models, survival or longitudinal data, uncertainty quantification, Monte Carlo simulation — and comfort with Bayesian ideas is aa plus. You do not need to be a specialist in either: methodological depth sits with the biostatistics team, and curiosity about clinical trial methodology matters more here than prior expertise in it.

• Broad, generalist data analysis skills: comfort with messy, heterogeneous, and unstructured data — tables, documents, free text — and the judgment to choose pragmatic methods that fit the question.

• Familiarity with clinical trial concepts, operational trial data, or real-world health data is a plus but not required.

• Awareness of responsible AI practices, including validation, transparency, bias, and traceability; experience in regulated or otherwise high-stakes settings is a plus.

• Ability to work in a highly collaborative environment and explain technical and statistical results to non-technical stakeholders with clarity and pragmatism.

• Demonstrated capacity to work independently, manage your own research agenda, and move problems forward without waiting for constant direction. Proactive initiative is a key expectation of this role.

Eligibility Requirements :

• Must be legally authorized to work in the United States without restriction.

• Must be willing to take a drug test and post-offer physical (if required).

• Must be 18 years of age or older.

Application Requirements

1. Curriculum vitae

2. Letter of intent - focusing on how a fellowship at Boehringer Ingelheim can help further your career growth. *Please upload under My Documents, Additional Attachments.

Compensation

This position offers a base salary of $80,000. The position may be eligible for a role specific variable or performance based bonus and or other compensation elements.  For an overview of our benefits please click here. (https://www.boehringer-ingelheim.com/us/careers/benefits-rewards)

Duration: Two years

Location: Remote (within US)

JOB ID

31802

Job function

Medicine

Career level

Post Doctorate opportunities

Organization

BI Pharmaceuticals, Inc.

Working time

Full-Time

Job flexibility

Hybrid

Boehringer Ingelheim is an equal opportunity global employer who takes pride in maintaining a diverse and inclusive culture. We embrace diversity of perspectives and strive for an inclusive environment, which benefits our employees, patients, and communities. All qualified applicants will receive consideration for employment without regard to a person’s actual or perceived race, including natural hairstyles, hair texture and protective hairstyles; color; creed; religion; national origin; age; ancestry; citizenship status, marital status; gender, gender identity or expression; sexual orientation, mental, physical or intellectual disability, veteran status; pregnancy, childbirth or related medical condition; genetic information (including the refusal to submit to genetic testing) or any other class or characteristic protected by applicable law