Interested in this Data Scientist role at DRT Strategies, Inc.?
Apply Now →About This Role
Overview
DRT Strategies delivers expert management consulting and information technology (IT) solutions to large federal agencies, state and local government and commercial clients in health care, technology, and financial services industries.
The three letters of our name, DRT, stand for Driving Resolution Together, which is the core philosophy on which the company was founded. That is, we collaborate with our clients to solve their most pressing challenges \- together.
We are problem solvers dedicated to your success, combining Fortune 500 experience with small business responsiveness. We have established a reputation with our clients as a forward\-thinking consulting firm with demonstrated success in implementing solutions that lead to meaningful results. Our world\-class consultants unite people to work collaboratively to achieve project goals and make vision a reality.
Project Description:
The Clinical Analyst contractor position provides scientific and clinical analytical support to CDER Office of New Drugs (OND) multi\-disciplinary review teams. The individual will assist in the evaluation of drug applications, review clinical safety data, labeling assessment, and preparation of scientific reports. The work requires advanced knowledge in health and data sciences and
the ability to apply scientific expertise to support risk determinations in the context of regulatory review.
Note: This is a support role. All regulatory decisions, final recommendations, and official communications with applicants remain the exclusive responsibility of qualified FDA
federal employees. The contractor's work products are subject to review and approval by FDA staff.
Job Summary:
The Clinical Analyst position interacts with many FDA stakeholders across several Offices and Centers specifically with clinical reviewers (Medical Officers) and statistical reviewers. This role will be responsible for reviewing safety data sufficiency and integrity, conducting safety data analyses, verifying safety data submitted by the applicant, and generating high\-quality scientific reports.
Responsibilities:
Clinical Data Analysis and Review* Analyze and evaluate submitted data from applicants seeking permission to market new drugs for general use and prepare analytical summaries on the adequacy of safety data provided.
- Review NDAs, BLAs, supplements, and amendments; prepare draft analytical reports and recommendations for FDA reviewer consideration.
- Incorporate summaries from clinical safety data reviews as part of integrated multi\-disciplinary assessments. Prepare, oversee, and maintain project schedules.
Labeling Review Support* Assist in the review of proposed drug labeling to assess whether safety claims are truthful and adequately supported,
- Provide draft safety data analyses on labeling accuracy and completeness for review by FDA staff.
Scientific Correspondence and Reporting* Draft scientifically sufficient reports of findings that clearly communicate clinical safety analyses and conclusions.
- Prepare draft correspondence identifying facts and information inadequately presented in sponsor submissions, for FDA reviewer finalization and issuance.
- Prepare clear summaries of clinical safety data tables, figures and listings for FDA review team use.
Literature Review and Knowledge Management* Review scientific literature and maintain awareness of current clinical developments and evolving findings in relevant therapeutic areas. Support preparation of background materials for seminars, conferences, and industry meetings.
- Stakeholder Support
- Support clinical review teams in preparing for meetings with drug company representatives, advisory committees, and external scientific bodies.
Other Tasks* Lead meetings with clinical reviewers and statistical reviewers to present results from data quality assessments and standard safety data analyses.
- Collaborate with CDER OND staff to optimize team processes and deliverables.
- Work with FDA stakeholders to review background packages and mock safety datasets to assess appropriateness of controlled terminology and safety dataset structure.
- Interact with government and contractor teams to help manage and monitor project progress, risk, issues, and track action items.
- Manage, organize, and update SharePoint sites.
- Assist in overall project support, as needed.
- Support any other DRT tasks as assigned/requested by Portfolio Manager and Account Lead.
Required Experience:* Minimum of 3 years professional experience.
- Technical proficiency in programming languages\- R (mandatory) with demonstrated experience using R for data manipulation, analysis, and visualization in a clinical or regulatory research context.
- R programming – ability to troubleshoot errors in R.
- Experience with CDISC data standards (including SDTM and ADaM) and safety dataset structure (e.g., adsl.xpt, adae.xpt, adlb.xpt, advs.xpt, and adeg.xpt)
- Understands data analytical methods (e.g., longitudinal analysis, time\-to\-event analyses, and causal/correlation analyses) for conducting safety data analyses (tables and figures)
- Understands safety review elements including trial design, demographics, exposure, death, discontinuation, dose modification, SAE, TEAE, FMQ, AESI, laboratory tests, and vital signs. Working knowledge of safety analysis methods, including the evaluation of adverse event data, safety signal detection, and the preparation of standardized safety tables and figures.
- Strong analytical and statistical skills to assess safety data.
- Excellent organizational, time management, verbal and written communication skills.
- Ability to independently manage a variety of projects with frequent interruptions and shifting priorities.
- Ability to organize a continuous flow of work in a timely manner and meet mandatory deadlines.
- Computer skills: MS Office Suite (particularly PowerPoint, Word, Excel), Adobe Acrobat.
- Ability to work independently within a multidisciplinary team.
Preferred Experience:* Proficiency in manipulating data using R programming.
- Experience and/or knowledge of analytical software including JReview, JMP, JMP Clinical, etc.
- Experience in SAS programming.
- Ability to apply knowledge of scientific research principles, study design concepts, and methods sufficient to evaluate clinical drug development programs.
- Experience in applying clinical safety data analytical skills, including the ability to synthesize clinical and scientific evidence to inform risk assessments.
- Experience in clinical trials, especially statistical hypothesis testing methods. Understands general concept of clinical trial design and drug development (e.g., adequate and well\-controlled studies).
- Statistical background, including experience with biostatistical methods commonly
applied in clinical trial design, analysis, and interpretation (e.g., survival analysis, mixed\-
effects models, hypothesis testing).
- Machine learning and AI background, including familiarity with predictive modeling
techniques (e.g., classification models, regression models, random forest, or neural
networks) and their potential applications in drug safety evaluation and regulatory science.
- Epidemiological background, including experience with observational study design, real\-world evidence, pharmacoepidemiology, or population\-level safety surveillance methods.
- Ability to work with little direct supervision on loosely defined tasks and coordinate work across multiple projects.
- Experience identifying, articulating, and resolving complex, unique, and previously unresolved.
- Familiarity with FDA regulatory process and/or working experience at FDA.
Education \& Training:* PharmD or PhD in the STEM disciplines: bioinformatics, Public Health, Pharmacology, Toxicology, Biology, Biomedical Engineer, biology, biostatistics, epidemiology, health informatics, or pharmaceutical science.
Work Authorization, Clearance Requirement, \& Additional Information:* This position requires the ability to obtain and maintain a U.S. government Public Trust clearance. Due to contract requirements, candidates must be U.S. citizens or lawful permanent residents (green card holders) to be eligible.
- No agencies, third parties, or Corp\-to\-Corp submissions.
Salary Range:* $90,000\-120,000
- Salary commensurate with experience.
DRT Strategies, Inc. (DRT) follows the guidelines outlined by the Equal Employment Opportunity Commission (EEOC) to provide all employees and qualified applicants employment without regard to race, color, religion, sex (including pregnancy, childbirth, or related conditions, transgender status, and sexual orientation), national origin, age, genetic information, disability, protected veteran status, or any other protected characteristic under federal, state, or local law.
Reasonable accommodations for applicants and employees with disabilities will be provided. If a reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact Human Resources by emailing [email protected], or by dialing 571\-482\-2517\.
For additional information, please review the Know Your Rights: Workplace Discrimination is Illegal, E\-Verify (English), E\-Verify (Spanish). Right to Work (English), Right to Work (Spanish).
Please be aware of recruitment fraud where malicious individuals might pose as DRT Strategies. Only job postings and emails from drtstrategies.com are authentic and legitimate communications regarding DRT Strategies employment opportunities. Please contact Human Resources at [email protected] if you believe you have received a fraudulent email.
6wWbCValJI
Salary Context
This $90K-$120K range is in the lower quartile for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At DRT Strategies, Inc., this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills in Demand for This Role
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880. This role's midpoint ($105K) sits 47% below the category median. Disclosed range: $90K to $120K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
DRT Strategies, Inc. AI Hiring
DRT Strategies, Inc. has 2 open AI roles right now. They're hiring across Data Scientist. Based in Silver Spring, MD, US. Compensation range: $120K - $135K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.