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About This Role
### Job Information
- Date Opened 08/07/2026
- Industry Pharma
- Job Type Full time
- Years of Experience 5
- City Indianapolis
- State/Province Indiana
- Country United States
- Zip/Postal Code 46201
### About Us
Founded in 2015, RADcube is a leading technology consulting and software development firm headquartered in Carmel, Indiana. The company specializes in transforming enterprise ideas into real\-world innovations by leveraging emerging technologies such as Artificial Intelligence, Blockchain, and Cloud Computing.
With nearly a decade of industry experience, RADcube serves diverse sectors, including healthcare, finance, government, and manufacturing. Their core service portfolio includes:
Digital Transformation and strategy consulting.
Custom Software Development tailored to specific business needs.
Advanced Data Analytics and AI\-driven platforms.
Cybersecurity and risk management.
Recognized for its innovation\-led culture, RADcube operates RADlabs, an R\&D hub focused on high\-impact solutions like Responsible AI and Intelligent Automation. The firm is committed to a human\-centric approach, ensuring cutting\-edge technology delivers measurable business outcomes and long\-term success for global clients.
The company’s commitment to innovation has earned significant industry honors:
2026 TechPoint Mira Awards Finalist: Named a finalist for Tech Company of the Year, recognizing high\-growth pioneers that demonstrate extraordinary leadership.
Public Sector Excellence: Awarded the Utah NASPO Cloud \& Software Solutions Contract, solidifying their role as a trusted partner for large\-scale government digital initiatives and more.
### Job Description
Data Scientist
Hybrid – Indianapolis, IN About the Role
We are seeking a Data Scientist with 3–5 years of experience working specifically within the pharma industry to join a pharma\-focused data team. This role combines applied statistical/ML modeling with strong data engineering fluency, working against large\-scale data housed in Databricks and AWS. You will partner with business groups to frame problems, build models and analyses that answer them, and communicate results in terms that drive pharma business decisions. Key Responsibilities
- Develop statistical models, machine learning models, and advanced analyses using large\-scale datasets in Databricks
- Access, prepare, and engineer features from data processed through Apache Spark and AWS data services
- Partner with business groups to understand pharma\-specific problems and translate them into data science approaches
- Build and validate ETL/data pipelines as needed to support modeling and experimentation workflows
- Communicate modeling results, insights, and recommendations clearly to both technical and non\-technical business stakeholders
- Apply pharma domain knowledge to ensure models and analyses are relevant and interpretable in a business context
- Collaborate with data engineers and analysts to productionize models and integrate outputs into reporting/decision workflows
- Monitor model performance over time and iterate as needed
### Requirements
Required Qualifications
- 3–5 years of data science / applied statistics / machine learning experience specifically within the pharma industry
- Hands\-on experience with Databricks for data science/ML workflows
- Working knowledge of AWS data services
- Strong experience with Big Data processing using Apache Spark, PySpark.
- Experience building ETL pipelines to support data science workflows
- Strong Python and SQL skills; experience with ML libraries
- Demonstrated ability to understand pharma business needs and speak to pharma business groups
- Strong communication skills with demonstrated ability to present technical findings to business stakeholders
- Bachelor's or master's degree in data science, Statistics, Computer Science, or a related quantitative field
Preferred Qualifications
- Experience with Delta Lake, Snowflake, or similar modern data platforms
- Familiarity with MLOps practices and model deployment/monitoring
- Prior experience supporting pharma commercial, clinical, or R\&D data science functions
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At RadCube, 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 $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
RadCube AI Hiring
RadCube has 1 open AI role right now. They're hiring across Data Scientist. Based in Indianapolis, IN, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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.
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