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About This Role
Description
### About Norstella
Norstella is a group of prominent pharmaceutical solutions providers – Evaluate, MMIT, Panalgo, The Dedham Group, Citeline – that help clients navigate complexities at each step of the drug development life cycle, from pipeline to patient. For more information, please visit Norstella.com.
### The Team
Our dedicated Data Science team is at the forefront of revolutionizing pharma intelligence and how patients gain access to life\-saving therapies. Armed with cutting\-edge technology and a passion for innovation, we leverage the vast landscape of data to extract actionable insights that drive informed decision making.
Our unique collaborative approach fosters a dynamic synergy between data science, product development, pharmaceutical industry experts, and engineering. Our deep expertise in artificial intelligence, machine learning, and advanced statistical modelling, combined with our domain knowledge, enables us to deliver comprehensive solutions that empower our clients to stay ahead in a rapidly evolving industry. We have delivered multiple products live to customers, with many more in development.
### Scope of the Role
In this role as a Senior Data Scientist, you will:
- Collaborate with product partners and others to identify new opportunities to apply AI / ML to our content and products
- Conduct research and identify AI / ML algorithms and methods to solve specific business problems
- Implement rigorous testing and evaluation of potential solutions; review indicative results and proof\-of\-concepts with multi\-disciplinary teams as part of customer\-led product development
- Deploy your solutions as production\-grade microservices in collaboration with engineering and DevOps teams
- Contribute towards common data science platforms and data assets
- Stay up\-to\-date, constantly learning about advances in the field, and deliver periodic presentations to internal teams on these developments
- All other duties, as assigned
As a senior\-level scientist, you will primarily be responsible for one major release at a time with a high degree of individual ownership. While not all research leads to successful deployed systems, past successes have seen a general availability launch typically \~6 months from project start. Our solutions have used a wide range of approaches, including: agentic systems and model context protocol (MCP) servers, retrieval augmented generation (RAG), classical machine learning, knowledge graphs, and simply well\-designed data transformations and business logic – our focus is on solving problems rather than the technology used.
### What it Takes
Requirements:
- Graduate degree in a STEM field such as Computer Science, Engineering, Statistics, or equivalent practical experience
- 5\+ years of experience developing AI / ML applications and data driven solutions
- Excellent knowledge of Python and core data science libraries such as pandas, scikit\-learn, LangChain, etc.
- Experience with LLMs, e.g. foundation model APIs, prompt engineering, retrieval augmented generation
- Substantial depth and breadth across state\-of\-the\-art AI / ML techniques such as Generative AI, NLP, Deep Learning, etc.
- Understanding of CS fundamentals, computational complexity, and algorithm design
- Ability to engineer and deploy well\-architected software packages
- Strong communication and stakeholder management skills, ability to independently own and execute a project holistically at a high level
- Experience mentoring junior team members
Preferred Qualifications:
- Deep expertise in engineering agentic AI systems
- Knowledge of the healthcare / pharma domain and experience applying AI to healthcare data
- Experience with AWS, especially ECS, Bedrock, SageMaker, serverless compute and storage
- Ability to prototype PoC webapps with familiarity across the full stack
- Expert usage of AI coding tools and workflows
### Benefits:
- Medical and Prescription Drug Benefits
- Health Savings Accounts (HSA) or Flexible Spending Accounts (FSA)
- Dental \& Vision Benefits
- Basic Life and AD\&D Benefits
- 401k Retirement Plan with Company Match
- Company Paid Short \& Long\-Term Disability
- Paid Parental Leave
- Paid Time Off \& Company Holidays
*Norstella is an equal opportunity employer. All job applicants will receive equal treatment regardless of race, creed, color, religion, alienage or national origin, ancestry, citizenship status, age, physical or mental disability or handicap, medical condition, sex (including pregnancy and pregnancy\-related conditions), marital or domestic partner status, military or veteran status, gender, gender identity or expression, sexual orientation, genetic information, reproductive health decision making, or any other protected characteristic as established by federal, state, or local law.*
*All legitimate roles with Norstella will be posted on Norstella’s job board which is located at norstella.com/careers. If a role is not posted on this job board, a candidate should assume the role is not a legitimate role with Norstella. Norstella is not responsible for an application that may be submitted by or through a third\-party and candidates should proceed with extreme caution if a third\-party approaches them about an open role with Norstella. Norstella will never ask for anything of value or any type of payment during or as part of any recruitment, interview, or pre\-hire onboarding process. If you are aware of or have reason to believe a job posting purportedly for a role with Norstella is fraudulent or otherwise not authorized by Norstella, please contact the Company using the following email address: [email protected].*
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 Norstella, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Norstella AI Hiring
Norstella has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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.
Frequently Asked Questions
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