Remote SAS Programmer (SDTM/ADaM) – Clinical Data Programming Opportunity

Role Highlights

Clinical research doesn’t advance on big headlines alone. It moves because someone quietly fixes messy datasets at the right moment. That’s the space this role lives in. As a SAS Programmer working with SDTM and ADaM standards, your work sits right in the middle of clinical trial reality—where raw data arrives from hospitals, labs, and global study sites in all kinds of formats, and still needs to make sense before anyone can trust it. You’re the person who helps turn that chaos into something structured enough for real scientific decisions. It’s not flashy work, but it matters in a very direct way. If the data isn’t right, nothing downstream works properly—not analysis, not reporting, not regulatory submission.

Value of This Role

Clinical trials generate a surprising amount of inconsistency. Even well-run studies produce data that doesn’t line up perfectly across sites. That’s normal. But it still has to be fixed before it can be used. This role exists to ensure it doesn’t become a bottleneck. When SDTM datasets are correctly structured, and ADaM datasets are carefully built, everything else in the study moves faster and with fewer questions. Statisticians don’t have to guess. Regulatory teams don’t have to chase missing logic. And study timelines don’t get stuck because of preventable data issues. In a way, you’re helping remove friction from an entire research pipeline.

Day-to-Day Duties

The work is hands-on and detail-heavy, but not repetitive in the way people sometimes assume. You’ll spend a good part of your time inside SAS, working through clinical datasets that come in from multiple countries and trial sites. Some days are about mapping raw data into SDTM structures. Other days are more about building ADaM datasets that statisticians can actually analyze without second-guessing the inputs. There are also moments where you just stop and dig. A lab value doesn’t match across visits. A medication timestamp feels off. A dataset behaves differently than expected. That’s where most of the real thinking happens—figuring out whether it’s a data issue, a mapping problem, or something upstream that needs correction. You’ll also spend time coordinating with biostatisticians and data managers. Not in formal, scripted meetings every hour, but in practical back-and-forth conversations like: “Does this variable belong here?” or “Should this derivation follow study rule A or B?”

Skills & Qualifications

Strong SAS programming experience is the core expectation here—especially in clinical research environments where precision actually matters, not just speed. If you’ve worked with SDTM and ADaM before, you already know how much structure CDISC standards bring into the picture. That understanding is important here because it’s what keeps datasets consistent across studies that might otherwise look completely different. Familiarity with clinical trial workflows helps a lot, too. Knowing how data moves from collection to analysis makes it easier to spot where something went wrong. And honestly, that kind of intuition becomes more valuable than any single tool over time. Attention to detail isn’t just a nice-to-have—it’s basically part of the job description, even if it’s not written as a bullet point.

Work Arrangement

This is fully remote, but it doesn’t feel disconnected. You’re still very plugged into global teams, just without the office layer in between. Most communication happens through shared platforms and documentation tools. Some days are quiet and focused, other days involve more coordination when deadlines are close, or datasets are being finalized for submission. There’s flexibility in how you structure your day, but not much flexibility in the quality of output. Clinical timelines are real, and they don’t shift easily.

Tools Overview

SAS is the main environment you’ll live in. That’s where most of the programming, transformation, and validation happens. Alongside that, you’ll interact with clinical data management systems that hold trial data from different sites. CDISC guidelines act like the backbone for structuring everything properly. You’ll also use validation checks, review tools, and version-controlled environments to make sure nothing breaks when multiple people are working on the same study at the same time.

Real Work Scenario

Picture a late-phase clinical trial where data is coming in from hospitals across several countries. Everything looks fine at first glance—but once you go deeper, the inconsistencies start showing up. One site records lab values in a slightly different format. Another has missing medication timing entries. Nothing is completely broken, but nothing is fully aligned either. This is where your work kicks in. You start by mapping raw inputs to SDTM domains, ensuring the structure is consistent across the entire dataset. Then you move on to ADaM creation, where the data is ready for statistical analysis. At some point, you notice something odd—dosage intervals don’t line up for a subset of patients. Instead of ignoring it or patching it quickly, you trace it back through the transformation logic, confirm the issue with the data team, and adjust the derivation to reflect the correct rule. By the time everything is finalized, the dataset is clean enough for submission without last-minute corrections slowing things down.

Ideal Candidate

The best fit for this role isn’t just someone who knows SAS. It’s someone who actually enjoys working with structured data and doesn’t mind spending time fixing things most people would overlook. You’re comfortable working independently, especially in a remote setup, but you also know when to ask questions rather than assume. That balance matters a lot in clinical programming environments. Experience with SDTM, ADaM, and CDISC standards is important, but equally important is how you think when data doesn’t behave the way it should. That’s where strong programmers stand out. This role suits someone who prefers accuracy over speed and finds satisfaction in getting things genuinely right rather than just getting them done.

How to Apply

This position offers an annual salary of $82,434 and the opportunity to work directly with data that supports real clinical research and healthcare outcomes. If your background includes SAS programming in clinical environments and you’re comfortable working with structured data that impacts regulatory decisions, this could be a solid next step. Submit your application if you’re looking for work where careful thinking, clean data, and real-world impact actually matter together—not separately.