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About This Role
Charlestown, MA • Beth Israel Lahey Health • Full-time • Day
When you join the growing BILH team, you're not just taking a job, you’re making a difference in people’s lives.
As a Data Scientist, you will focus on machine learning, predictive analytics, and related projects, employing machine learning frameworks across development, testing, and production environments to develop and implement innovative technologies. Your primary responsibility will be to research and advance healthcare analytics solutions. This entails designing machine learning algorithms aimed at improving outcomes, reducing computational complexities, increasing model accuracy, and optimizing critical business metrics. Moreover, you will leverage LLM, NLP, and Generative AI to refine and enhance results.
In collaboration with subject matter experts, you will uncover valuable insights and expand on concepts in statistics, optimization, and machine learning to elevate patient and provider experiences. Your role includes building dependable and scalable production machine learning services for integration with other systems. You will work on feature engineering, statistical analysis, novel ML techniques development, classifier performance assessment, and ensuring fitness-for-purpose.
Working cross-functionally, you will engage with a diverse group of stakeholders, including developers, data engineers, and physicians. This position may also involve working on special projects and performing other job duties, as required.
Job Description:
Primary Responsibilities:
- Collaborates with business stakeholders to document business problems requiring advanced analytics; develop, evaluate and implement machine learning models to improve business operations. (essential)
- Collaborates with Software Engineering, Data Engineering and clinical team to structure and normalize data systems and other data enrichment sources. (essential)
- Works on feature engineering, statistical analysis, develop novel ML techniques, understanding classifier performance, and ensuring fit-for-purpose; creates machine learning algorithms to optimize and deliver results, perform hyper-parameter tuning, and evaluated model performance. (essential)
- Works with cognitive computing platforms to improve predictions about patient outcomes, focuses on configuring, operationalizing, and reporting on vendor developed models. (essential)
- Visualizes and interprets data and model results (essential)
- Uses machine learning frameworks in both development, testing and production environments. (essential)
- Participates on all parts of the machine learning project lifecycle from dataset curation to model deployment. (essential)
- Performs all other duties as needed or directed to meet the needs of the department and or customer. (essential)
- Achieves and maintains required certifications (if applicable). (essential)
Required Qualifications:
- bachelor’s degree in computer science, statistics, mathematics, physical science, engineering, or a comparable related technical field: or equivalent years of experience.
- 1 – 3 Years: Experience in an engineer role Industry experience in software engineering and data science.
Preferred Qualifications:
- Knowledge and experience using basic and intermediate project management tools and techniques.
- Programming skills with fluency in Python and its libraryies such as NumPy, Pandas, Scikit-learn, Matplotlib and Seaborn; experience in working with deep learning framework Pytorch, tensorflow or Keras.
- Familiar with entire data science process life cycle
- A proven track record of producing quality deliverables on time, taking ownership and accountability of assignments and demonstrating a strong work ethic.
Working experiece in Healthcare
Dept/Unit Specific Skills:
- Strong background and experience in ML NLP, especially transformer-based ML and Large Language Models.
- Working knowledge of one or more of the following NLP fields: Information Retrieval, Question Answering/Conversational AI, Reinforcement Learning.
- Hands-on with latest Generative AI developments in the past year with exposure to finetuning, prompting strategies and optimization of models for inferencing.
- Experience with working on large data sets, especially with Spark.
- Experience with cloud computing platforms such as AWS, Azure or GCP.
Pay Range:
$104,998.00 USD – $141,314.00 USD
The pay range listed for this position is the annual base salary range the organization reasonably and in good faith expects to pay for this position at this time. Actual compensation is determined based on several factors, that may include seniority, education, training, relevant experience, relevant certifications, geography of work location, job responsibilities, or other applicable factors permissible by law.
As a health care organization, we have a responsibility to do everything in our power to care for and protect our patients, our colleagues and our communities. Beth Israel Lahey Health requires that all staff be vaccinated against influenza (flu) as a condition of employment.
More than 35,000 people working together. Nurses, doctors, technicians, therapists, researchers, teachers and more, making a difference in patients' lives. Your skill and compassion can make us even stronger.
Equal Opportunity Employer/Veterans/Disabled
Job details
- Employment: Full-time
- Hours/Week: 40
- Shift: Day
- Category: Analytics & Reporting
- Pay Range: $104,998.00 USD - $141,314.00 USD
- FLSA: Exempt
- Req ID: JR103599
Salary Context
This $104K-$141K range is in the lower quartile for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Beth Israel Lahey Health, 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 $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($123K) sits 35% below the category median. Disclosed range: $104K to $141K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Beth Israel Lahey Health AI Hiring
Beth Israel Lahey Health has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Boston, MA, Charlestown, MA, US. Compensation range: $141K - $141K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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