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About This Role
Bring more to life.
Are you ready to accelerate your potential and make a real difference within life sciences, diagnostics and biotechnology?
At Phenomenex, one of Danaher’s 15\+ operating companies, our work saves lives—and we’re all united by a shared commitment to innovate for tangible impact.
You’ll thrive in a culture of belonging where you and your unique viewpoint matter. And by harnessing Danaher’s system of continuous improvement, you help turn ideas into impact – innovating at the speed of life.
Phenomenex isn’t your typical scientific company. Founded nearly 40 years ago, Phenomenex is a global technology leader committed to developing novel analytical chemistry solutions that solve the separation and purification challenges of researchers, advancing the future of scientific analysis and investigation, ensuring the quality of essentials like your food, water, shampoo, and even cold medication. Be part of our global success and together, we accelerate the discovery, development and delivery of solutions that safeguard and improve human health.
Learn about the Danaher Business System which makes everything possible.
The Data Scientist is responsible for developing and applying statistical, machine learning, and advanced analytical solutions to support data\-driven decision\-making. This role works closely with data engineering, business intelligence, and functional stakeholders to translate business questions into analytical models and insights. The position focuses on applied analytics and machine learning, supporting the development, validation, and use of models in enterprise environments. The Data Scientist applies strong analytical rigor and practical modeling skills to ensure results are accurate, interpretable, and actionable for business stakeholders.
This position reports to the Senior Manager, BI and Analytics and is part of the Information Technology Department and will be fully remote.
In this role, you will have the opportunity to:
- Develop and maintain analytical models: Apply statistical and machine learning techniques (e.g., regression, classification, time series) to build, validate, monitor, and improve models that support business forecasting and decision\-making.
- Execute the end‑to‑end analytics lifecycle: Perform data preparation, exploratory and statistical analysis, feature engineering, model validation, and result interpretation using enterprise\-scale data platforms.
- Leverage core technical tools and data platforms: Use Python (pandas, NumPy, scikit‑learn) and SQL to analyze structured and semi‑structured data, work with relational databases, and support production\-ready analytical solutions.
- Translate analytics into business insight: Partner with stakeholders to define analytical questions and success metrics, clearly communicate findings, and integrate outputs into reports, dashboards, or downstream systems.
- Collaborate, document, and continuously improve: Work effectively in cross‑functional teams, document methodologies and assumptions, contribute to shared code and best practices, and stay current on evolving analytical techniques and tools.
The essential requirements of the job include:
- Bachelor’s or master’s degree in a quantitative field with 2\+ years of hands\-on experience in data analysis, data science, or applied analytics.
- Proficiency in Python and SQL, working knowledge of statistical methods and machine learning techniques, and familiarity with the end\-to\-end analytics lifecycle from data preparation through validation and interpretation.
- Strong problem\-solving skills with the ability to clearly explain analytical results, translate insights into business value, and collaborate effectively in a cross\-functional environment
It would be a plus if you also possess previous experience in:
- experience with time\-series forecasting, optimization, or anomaly detection is a plus
Phenomenex, a Danaher operating company, offers a broad array of comprehensive, competitive benefit programs that add value to our lives. Whether it’s a health care program or paid time off, our programs contribute to life beyond the job. Check out our benefits at Danaher Benefits Info.
At Phenomenex we believe in designing a better, more sustainable workforce. We recognize the benefits of flexible, remote working arrangements for eligible roles and are committed to providing enriching careers, no matter the work arrangement. This position is eligible for a remote work arrangement in which you can work remotely from your home. Additional information about this remote work arrangement will be provided by your interview team. Explore the flexibility and challenge that working for Phenomenex can provide.
The salary range for this role is *$74,000 \- $111,000* This is the range that we in good faith believe is the range of possible compensation for this role at the time of this posting. This range may be modified in the future.
This job is also eligible for bonus/incentive pay.
We offer comprehensive package of benefits including paid time off, medical/dental/vision insurance and 401(k) to eligible employees.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.
Join our winning team today. Together, we’ll accelerate the real\-life impact of tomorrow’s science and technology. We partner with customers across the globe to help them solve their most complex challenges, architecting solutions that bring the power of science to life.
For more information, visit www.danaher.com.
Salary Context
This $74K-$111K range is in the lower quartile for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Phenomenex & Agela, 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 $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($92K) sits 53% below the category median. Disclosed range: $74K to $111K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Phenomenex & Agela AI Hiring
Phenomenex & Agela has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $111K - $111K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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