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About This Role
DescriptionLocation: Indianapolis, IN Metro (Hybrid / 3-Day Onsite) (Open to Regional/EST Candidates with Onsite Travel)
Contract Type: Contractor Full-Time / Enterprise Project Engagement (Outsourced via Xenon7)
About Xenon7*Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. Our expertise in AI solution architecture and specialized technical talent allows us to partner with enterprise leaders on transformative initiatives, driving innovation and business growth.*
Role purpose
Lead the development and application of digital twins to improve clinical trial design and support evidence-based clinical development decisions. The role prioritizes digital-twin, simulation, and AI/ML expertise, supported by clinical-development experience and close partnership with trial-design experts.
Position overview You will join a multidisciplinary clinical development environment and partner with clinical scientists, physicians, biostatisticians, pharmacometricians, biomarker experts, data scientists, data engineers, and other stakeholders. You will translate clinical development questions into quantitative strategies and deliver interpretable analyses that inform protocol design, patient selection, endpoint strategy, dosing, operational planning, and decision-making under uncertainty.
The role prioritizes digital twins as the primary capability for simulating patients, disease trajectories, treatment response, and clinical trials. The successful candidate will bring strong digital-twin, simulation, and AI/ML expertise, along with sufficient clinical-development knowledge to work effectively with trial teams. Deeper trial-design expertise can be developed through close collaboration with clinical scientists, biostatisticians, and pharmacometricians.
Key responsibilitiesDigital-twin development and validation* Lead the design, development, and evaluation of digital twins of patients, disease trajectories, treatment response, or clinical trials using clinical trial and relevant external data.
- Combine mechanistic, statistical, causal, and machine learning approaches to create fit-for-purpose digital twins that can be simulated under alternative populations, interventions, dosing strategies, endpoints, or trial designs.
- Define use cases and contexts of use with clinical and quantitative experts so each digital twin addresses a specific trial-design or development decision.
- Assess calibration, validation, robustness, transportability, uncertainty, and practical relevance using appropriate internal and external evaluation strategies.
- Develop reproducible workflows and reusable digital-twin components that can be applied across programs or therapeutic areas.
Clinical trial design and quantitative decision support* Partner with trial teams to frame development questions and evaluate design options, including target populations, eligibility criteria, enrichment strategies, comparators, endpoints, visit schedules, dosing strategies, and operational tradeoffs.
- Use digital twins and complementary simulation methods to compare plausible scenarios, characterize uncertainty, and support model-informed decision-making.
- Work closely with statisticians and pharmacometricians so analyses complement established trial-design methods and align with clinical and regulatory objectives.
- Communicate assumptions, limitations, and decision-relevant insights clearly to technical and non-technical stakeholders.
Data integration and scientific collaboration* Analyze and integrate clinical trial data and, where relevant, biomarkers, imaging, digital health data, omics, patient-reported outcomes, and real-world data.
- Partner with data engineering and platform teams to create analysis-ready datasets, data-quality checks, lineage, and reproducible pipelines.
- Contribute to scientific documentation, presentations, publications, and responsible adoption of digital-twin methods across clinical development.
Model quality and responsible use* Define the intended use, decision context, success criteria, and limitations of each model before development.
- Ensure models are interpretable, auditable, reproducible, and supported by evidence appropriate to their intended use.
- Apply appropriate governance, privacy, security, and scientific-review requirements for clinical data and analytical outputs.
- Clearly distinguish exploratory analyses from validated decision-support evidence and avoid overclaiming model capability.
Requirements* PhD in data science, statistics, biostatistics, pharmacometrics, computer science, biomedical informatics, engineering, mathematics, operations research, or a related quantitative field.
- Demonstrated experience developing digital twins, virtual patients, disease-progression models, clinical trial simulations, synthetic controls, or closely related computational models.
- Strong experience applying machine learning, statistical modeling, causal inference, or simulation to biomedical, healthcare, pharmaceutical, or other complex scientific data.
- Strong programming skills in Python, R, or a comparable analytical language, with experience using version-controlled and reproducible workflows.
- Sufficient clinical-development knowledge to frame relevant questions and collaborate effectively with clinical and quantitative trial experts.
- Ability and interest to deepen clinical trial-design knowledge through partnership with clinical scientists, biostatisticians, and pharmacometricians.
- Strong written and verbal communication skills and demonstrated effectiveness in cross-functional scientific collaboration.
Preferred qualifications* Strongly preferred: experience supporting clinical trial design, protocol development, clinical development strategy, model-informed drug development, or regulatory-facing quantitative evidence.
- Familiarity with one or more trial-design topics, such as endpoint strategy, sample size or power assumptions, patient selection or enrichment, longitudinal outcomes, missing data, adaptive designs, or decision criteria.
- Knowledge of digital-twin architecture and validation, supported by expertise in causal inference, Bayesian modeling, survival analysis, longitudinal modeling, clinical trial simulation, or synthetic controls.
- Experience with clinical data standards and common clinical-development data sources.
- Experience with biomarker, imaging, omics, digital health, or real-world data and integration of multiple data modalities.
- Experience with scalable cloud-based analytical environments.
- Track record of translating analytical work into decisions, reusable capabilities, publications, presentations, or other measurable scientific impact.
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 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Xenon7, 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 Required
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 $190,000 based on 1,564 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Xenon7 AI Hiring
Xenon7 has 1 open AI role right now. They're hiring across Data Scientist. Based in Ohio City, OH, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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
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