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About This Role
Overview:
The Data Scientist, ITA will be a primary analytical contributor within a small, high\-performance team. Working directly alongside the Director / VP and two peer Data Scientists, this individual will own the design and execution of analytical models, generate program insights across key KPI families, and help build the client\-facing outputs (pilot QBR materials, trend narratives, analytical summaries, prototype of insight\-focused dashboarding) that define the ITA value proposition. The role requires a combination of technical depth in SQL and Python, working familiarity with statistical methods applicable to program performance analysis, and an orientation toward clear, audience\-appropriate communication. Experience in pharma, pharma consulting, at a health insurer, or at a PBM is required: this work depends on understanding how specialty programs function, how manufacturers define success, and how data from hub operations connects to the broader access and reimbursement landscape.
Responsibilities:
- Analytical Execution
- Design and execute ad hoc and recurring analyses for ITA client engagements, covering six defined families of KPIs
- Build and maintain analytical models within the six KPI families, from patient outcomes through data quality, ensuring consistent definitions, reproducible methodology, and appropriate statistical rigor
- Develop cohort analyses, funnel decompositions, survival curves, regression models, and distribution\-based performance metrics that surface actionable insights within program data
- Identify trends, anomalies, and comparative performance differentials across programs, payors, geographies, and time periods; frame findings as hypotheses with supporting evidence
- Client\-Facing Output Development
- Contribute to quarterly business review (QBR) preparation, including data pulls, visualization development, and narrative drafting under the direction of the Director / VP
- Produce clean, client\-ready analytical exhibits (charts, tables, and written summaries) formatted for manufacturer audiences including market access leadership and patient services teams
- Participate in select client meetings as a technical resource; communicate methodology and findings clearly to non\-technical stakeholders
- Productization \& Documentation
- Define analytical specifications and prototype outputs for validated insights; partner with the dedicated BI and report development team to translate ITA analytical work into productized ThoughtSpot and Power BI dashboards; ITA owns the logic, acceptance criteria, and narrative framing; the report development team handles production build
- Maintain clear documentation of analytical methodologies, data transformations, and code to support reproducibility and team knowledge management
- Surface upstream data quality issues that affect insight reliability; partner with data governance teams on remediation
- Continuous Improvement
- Contribute to the evolution of the ITA analytical framework as new client engagements reveal novel questions and additional KPI families emerge
- Stay current on emerging methods in healthcare analytics, specialty pharmacy data, and applied machine learning relevant to program performance and patient journey analysis
Qualifications:
- Approximately 5 years of experience in healthcare analytics, with direct experience in one or more of the following: pharmaceutical manufacturer (commercial, market access, or patient services analytics), pharma consulting, health insurer, or pharmacy benefit manager (PBM)Strong proficiency in SQL; experience querying and manipulating data in cloud data warehouse environments (Snowflake preferred)Python or R required; Python preferred (pandas, numpy, scipy, and scikit\-learn)Working knowledge of statistical methods applicable to program performance analysis: cohort analysis, survival analysis, funnel decomposition, regression modeling, distribution\-based metrics, and experimental design and A/B testing frameworks
- Experience producing structured, audience\-ready analytical outputs, not just raw analyses; strong attention to how findings are communicated, not just computed
- Clear, organized written and verbal communication skills; ability to explain quantitative findings in plain language
- Bachelor's degree in a quantitative field (statistics, mathematics, data science, computer science, economics, or related); advanced degree a plus
- (Preferred) Familiarity with pharmaceutical hub program workflows, including case management, prior authorization, benefits verification, payor adjudication, and patient financial assistance programs
- (Preferred) Experience working with specialty pharmacy data structures and source systems (CRM, pharmacy dispensing platforms, benefits verification systems)Background in patient access or market access analytics at a pharmaceutical manufacturer, including program KPI development and payor\-level performance analysis
- (Preferred) Analytical experience at a health insurer or PBM, particularly in formulary analytics, specialty drug utilization, or patient access reporting
- (Preferred) Experience with ThoughtSpot or Power BI preferred
- (Preferred) Exposure to or curiosity about generative AI and its applications in analytics workflows (natural language querying, entity resolution, or automated insight generation)Experience contributing to a client\-facing analytics product or repeatable analytical framework
- (Preferred) Familiarity with dbt or similar data transformation frameworks
Pay Range: USD $105,000\.00 \- USD $133,500\.26 /Hr.
Salary Context
This $105K-$133K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At AssistRx, 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. This role's midpoint ($119K) sits 38% below the category median. Disclosed range: $105K to $133K.
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.
AssistRx AI Hiring
AssistRx has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $133K - $133K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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