Interested in this Data Scientist role at RiverTowns Group?
Apply Now →About This Role
This is a unique opportunity to join an established firm and work in an entrepreneurial environment, building and scaling a fast\-growing team within a core strategic priority area for this leading life sciences company.
We are looking for experienced analytics talent with a proven ability to:
- Sell, manage, and deliver a book of business
- Act as a thought leader in technology solutions and/or integrated analytics that address specific commercial pharma/biotech needs (e.g., customer engagement, forecasting, insights democratization)
- Build strong teams, collaborate cross\-functionally, and contribute to internal development and growth of innovative capabilities
Essential Functions
Business Development
- Identify and drive opportunities to expand existing client engagements by recognizing emerging needs or gaps.
- Drive proposal development by crafting scopes of work, methodologies, timelines, and materials.
- Participate in thought‑leadership‑aligned initiatives when appropriate (e.g., internal points of view, methodology playbooks, capability development).
Client \& Project Delivery
Our Analytics Strategy team serves as the “tip of the spear” as client\-embedded leaders with deep understanding of client needs and analytics methodologies. They have a deep understanding of client portfolio, therapeutic landscape, and analytics \+ technology capabilities with a broad analytics repertoire. This role:
- Serves as a key client contact for analytics engagements—supporting discussions, participating in presentations, and ensuring high\-quality deliverables.
- Translates client business questions into practical, analytics and AI‑driven approaches across forecasting, sales analytics, omnichannel measurement, and data science.
- Oversees execution of project activities, including timelines, workplans, stakeholder coordination, deliverable development, and solution hand‑offs.
- Maintains accountability for project quality, accuracy, and consistency across all workstreams.
- Surfaces risks or scope changes early and guide clients through potential solutions or adjustments.
Team Leadership \& Development
- Oversee and coach junior team members in day‑to‑day analytics tasks, project execution, and professional development.
- Provide guidance on analytical methodologies, data structures, and tools.
- Drive development of internal best practices, training materials, and process improvements.
Internal Collaboration \& Influence
- Collaborate closely with forecasting, data science, product, and data solutions SMEs to bring the right expertise to each engagement.
- Lead internal initiatives such as process improvement, training content, or product/tool enhancements.
- Demonstrate strong project leadership behaviors: structured problem‑solving, expectation setting, quality control, and effective client communication.
Qualifications
Education
- Bachelor’s degree required; advanced degree a plus.
- Quantitative or life‑sciences‑related fields preferred (e.g., health sciences, analytics, engineering, economics, statistics, marketing).
Work Experience
- 7–9 years of experience in life sciences analytics, consulting, or related fields.
- Experience with commercial pharma datasets, advanced analytics applications, omnichannel measurement, marketing analytics, or technology‑enabled solutions is preferred.
Skills
- Strong analytical and structured problem‑solving abilities.
- Foundational understanding of AI, LLMs, and life sciences real\-world data
- Excellent communication skills (written and verbal).
- Ability to independently manage multiple workstreams and deadlines.
- Strong collaboration and stakeholder‑management capabilities.
- Proficiency with Microsoft Office Suite.
Pay: $128,768\.20 \- $155,075\.67 per year
Work Location: In person
Salary Context
This $128K-$155K range is below the median 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 RiverTowns Group, 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 $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 ($141K) sits 26% below the category median. Disclosed range: $128K to $155K.
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.
RiverTowns Group AI Hiring
RiverTowns Group has 2 open AI roles right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $155K - $155K.
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.
Frequently Asked Questions
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